diff --git a/OceanDataStore/data/ARMOR3D/create_ARMOR3D_P1M-m_monthly_climatology.py b/OceanDataStore/data/ARMOR3D/create_ARMOR3D_P1M-m_monthly_climatology.py
new file mode 100755
index 00000000..fed4148b
--- /dev/null
+++ b/OceanDataStore/data/ARMOR3D/create_ARMOR3D_P1M-m_monthly_climatology.py
@@ -0,0 +1,135 @@
+# =========================================================
+# create_ARMOR3D_P1M-m_climatology.py
+#
+# Script to create ARMOR3D REP monthly climatologies for
+# climate normals (1971-2000, 1981-2010, 1991-2020) and
+# write to local netCDF files.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import logging
+
+import numpy as np
+import xarray as xr
+import zarr
+
+from OceanDataStore.cli import initialise_logging
+from OceanDataStore.data.utils import (
+    compute_dx,
+    compute_dy,
+    compute_cell_area,
+    compute_cell_thickness,
+    compute_land_sea_mask,
+)
+
+logger = logging.getLogger(__name__)
+
+def main():
+    # ========== Initialize Logging and Print Banner ========== #
+    initialise_logging()
+
+    # ========== Prepare Data ========== #
+    # Open complete ARMOR3D REP monthly climatology dataset:
+    filepath = "/dssgfs01/scratch/otooth/npd_data/observations/ARMOR3D/armor-3d_rep_monthly_NA_*.zarr"
+    ds = xr.open_mfdataset(filepath, compat="no_conflicts", data_vars="all", engine="zarr")
+    logging.info("-> Completed: Opened ARMOR-3D REP monthly climatology dataset from Zarr stores.")
+
+    # Rename variables to standard names:
+    ds = ds.rename({"to": "thetao",
+                    "so": "so",
+                    })
+
+    # Update global attributes:
+    ds.attrs.clear()
+
+    ds = ds.assign_attrs({
+        "Conventions": "CF-1.0",
+        "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD.",
+        "description": "Multi Observation Global Ocean ARMOR3D multi-year reprocessed temperature salinity, sea surface height, geostrophic current and mixed layer depth climatology on 1/8 degree regular grid and 50 depth levels.",
+        "source": "Numerical models: Multiple Linear Regression, Optimal Interpolation. In-situ observations: Copernicus In Situ TAC (including Argo, XBT, CTD and moorings) Copernicus Sea Level TAC, CNES-CLS22 Mean Dynamic Topography, OSTIA Sea Surface Temperature Analysis, Copernicus MOB TAC (Sea Surface Salinity), and World Ocean Atlas 2018 (WOA18).",
+        "dataset_type": "observation",
+        "product_type": "climatology",
+        "product_version": "2.0",
+        "institution": "Copernicus Marine Service, Mercator Ocean International, France",
+        "citation": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). DOI: 10.48670/moi-00052 (Accessed on 21 04 2026).",
+        "references": "Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845-857. Mulet, S., M.-H. Rio, A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. Deep Sea Research Part II : Topical Studies in Oceanography, 77-80(0):70-81.",
+        "acknowledgement": "Generated using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00052.",
+        "license": "ARMOR3D data were obtained from https://doi.org/10.48670/moi-00052, and are provided under the Copernicus Marine Environment Monitoring Service Service Level Agreement (SLA) https://marine.copernicus.eu/user-corner/service-commitments-and-licence?pk_vid=42ac3e352be888641780994034c3bb6e",
+        "doi": "10.48670/moi-00052",
+        "platform": "gr",
+        "horizontal_grid_type": "regular rectilinear",
+        "horizontal_grid_resolution": "0.125 degree",
+        "vertical_grid_type": "z",
+        "vertical_grid_coordinate": "depth",
+        "vertical_grid_levels": 50,
+        "aggregation": "mean",
+        "aggregation_frequency": "monthly",
+        "status": "completed",
+        "update_frequency": "None",
+        "bbox": "[-180.0, 180.0, -90.0, 90.0]",
+    })
+
+    logging.info("-> Completed: Updated ARMOR3D CF-metadata.")
+
+    # -- Calculate climate normal monthly climatologies -- #
+    output_dir = "/dssgfs01/scratch/otooth/npd_data/observations/ARMOR3D/climatology/"
+    start_years = [1971, 1981, 1991]
+    end_years = [2000, 2010, 2020]
+
+    for start_year, end_year in zip(start_years, end_years):
+        logging.info(f"In Progress: Calculating monthly climatology for {start_year}-{end_year} climate normal period...")
+        # Calculate monthly climatology for the specified period:
+        ds_climatology = ds.sel(time=slice(f'{start_year}-01', f'{end_year}-12')).groupby('time.month').mean()
+
+        # Add ancillary variables:
+        ds_climatology['mask'] = compute_land_sea_mask(ds['thetao'].isel(time=0, depth=0))
+        ds_climatology['dx'] = compute_dx(ds)
+        ds_climatology['dy'] = compute_dy(ds)
+        ds_climatology['cell_area'] = compute_cell_area(ds)
+        # Custom ancillary variables:
+        ds_climatology['cell_thickness'] = compute_cell_thickness(ds_climatology)
+        ds_climatology['cell_volume'] = ds_climatology['cell_thickness'] * ds_climatology['cell_area']
+
+        # Update attributes for custom ancillary variables:
+        ds_climatology['cell_volume'].attrs.update({
+            'long_name': "Grid-Cell Volume",
+            'standard_name': "cell_volume",
+            'units': "m3",
+        })
+
+        # Update time bounds to reflect climatological period:
+        ds_climatology['time_bnds'] = xr.DataArray(data=np.zeros((12, 2), dtype='datetime64[M]'), dims=('month', 'bnds'))
+        ds_climatology['time_bnds'][:, 0] = np.arange(f'{start_year}-01', f'{start_year+1}-01', dtype='datetime64[M]')
+        ds_climatology['time_bnds'][:, 1] = np.arange(f'{end_year}-01', f'{end_year+1}-01', dtype='datetime64[M]')
+        logging.info(f"-> Completed: Calculated monthly climatology for {start_year}-{end_year} climate normal period.")
+
+        # Update title attribute to reflect climatological period:
+        ds_climatology.attrs['title'] = f"Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD monthly climatology ({start_year}-{end_year})."
+        ds_climatology.attrs['start_datetime'] = f"{start_year}-01-01"
+        ds_climatology.attrs['end_datetime'] = f"{end_year}-12-31"
+
+        # Update variable encodings:
+        blosccodec = zarr.codecs.BloscCodec(cname="lz4", clevel=5, shuffle=zarr.codecs.BloscShuffle.shuffle)
+
+        # Infer variable chunk sizes (preserve source chunking):
+        for var in list(ds_climatology.data_vars) + list(ds_climatology.coords):
+            source_chunks = ds_climatology[var].encoding.get('chunks', None)
+            ds_climatology[var].encoding.clear()
+            ds_climatology[var].encoding['compressors'] = [blosccodec]
+            # Cast float64 back to float32 to match source precision:
+            if ds_climatology[var].dtype == np.float64:
+                ds_climatology[var].encoding['dtype'] = 'float32'
+            if source_chunks is not None:
+                ds_climatology[var].encoding['chunks'] = source_chunks
+
+        # Write monthly climatology to Zarr:
+        output_filepath = f"{output_dir}ARMOR3D_global_{start_year}_{end_year}_monthly_climatology.zarr"
+        ds_climatology.to_zarr(output_filepath, zarr_format=3, mode="w")
+        logging.info(f"-> Completed: Saved monthly climatology for {start_year}-{end_year} climate normal period to {output_filepath}.")
+
+    # -- Close ARMOR3D analysis datasets -- #
+    ds_climatology.close()
+    ds.close()
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/ARMOR3D/download_ARMOR3D_0.125def_P1M-m_1993_2024.py b/OceanDataStore/data/ARMOR3D/download_ARMOR3D_0.125def_P1M-m_1993_2024.py
new file mode 100755
index 00000000..1ca6ab6d
--- /dev/null
+++ b/OceanDataStore/data/ARMOR3D/download_ARMOR3D_0.125def_P1M-m_1993_2024.py
@@ -0,0 +1,33 @@
+"""
+Download global domain of ARMOR-3D dataset from Copernicus Marine.
+This script downloads the monthly reprocessed files from 1993 to 2024.
+
+Created By: Ollie Tooth
+Created On: 2026-04-21
+Contact: oliver.tooth@noc.ac.uk
+
+Note: This download script should be run using the env_oceandatastore environment.
+"""
+
+# Import the Copernicus Marine API toolbox:
+import copernicusmarine
+
+# Define filepath to credentials:
+credentials_fpath = ".../copernicusmarine-credentials"
+# Define output directory:
+out_fdir = "/dssgfs01/scratch/otooth/npd_data/observations/ARMOR3D/"
+
+# Download the ARMOR3D analysis dataset for North Atlantic subdomain:
+for year in range(2001, 2025):
+  print(f"In Progress: Downloading ARMOR3D analysis dataset for: {year}...")
+  copernicusmarine.subset(
+    dataset_id="cmems_obs-mob_glo_phy_my_0.125deg_P1M-m",
+    variables=["mlotst", "so", "to", "ugo", "vgo", "zo"],
+      start_datetime=f"{year}-01-01T00:00:00",
+      end_datetime=f"{year}-12-01T00:00:00",
+      credentials_file=credentials_fpath,
+      output_directory=out_fdir,
+      file_format="zarr",
+      output_filename=f"armor-3d_rep_monthly_NA_{year}.zarr",
+  )
+  print(f"Completed: downloading ARMOR3D analysis dataset for: {year}.")
diff --git a/OceanDataStore/data/ARMOR3D/run_create_ARMOR3D_P1M-m_monthly_climatology.slurm b/OceanDataStore/data/ARMOR3D/run_create_ARMOR3D_P1M-m_monthly_climatology.slurm
new file mode 100755
index 00000000..d2639819
--- /dev/null
+++ b/OceanDataStore/data/ARMOR3D/run_create_ARMOR3D_P1M-m_monthly_climatology.slurm
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH --job-name=armor3d_monthly_climatology
+#SBATCH --partition=compute
+#SBATCH --time=04:00:00
+#SBATCH --ntasks-per-core=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --ntasks-per-socket=32
+#SBATCH --nodes=1
+
+# ==============================================================
+# run_create_ARMOR3D_P1M-m_monthly_climatology.slurm
+#
+# Description: SLURM script to create ARMOR3D REP monthly 
+# climatology datasets.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-06-09
+#
+# ==============================================================
+set -euo pipefail
+
+# -- Python Environment -- #
+# Activate miniconda environment:
+source .../miniforge3/bin/activate
+conda activate env_ods
+
+# -- Create ARMOR3D REP monthly climatology datasets -- #
+echo "In Progress: Creating ARMOR3D REP monthly climatology datasets..."
+
+python3 create_ARMOR3D_P1M-m_monthly_climatology.py
+
+echo "Completed: Created ARMOR3D REP monthly climatology datasets."
diff --git a/OceanDataStore/data/ARMOR3D/run_send_ARMOR3D_P1M-m_climatology_to_os.slurm b/OceanDataStore/data/ARMOR3D/run_send_ARMOR3D_P1M-m_climatology_to_os.slurm
new file mode 100755
index 00000000..5c0af2f7
--- /dev/null
+++ b/OceanDataStore/data/ARMOR3D/run_send_ARMOR3D_P1M-m_climatology_to_os.slurm
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH --job-name=send_armor3d_my_climatology
+#SBATCH --partition=compute
+#SBATCH --time=04:00:00
+#SBATCH --ntasks-per-core=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --ntasks-per-socket=32
+#SBATCH --nodes=1
+
+# ==============================================================
+# run_send_ARMOR3D_P1M-m_climatology_to_os.slurm
+#
+# Description: SLURM script to send the ARMOR3D P1M-m monthly 
+# climatology datasets to Icechunk repository.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-06-09
+#
+# ==============================================================
+set -euo pipefail
+
+# -- Python Environment -- #
+# Activate miniconda environment:
+source .../miniforge3/bin/activate
+conda activate env_ods
+
+# -- Send ARMOR3D P1M-m monthly climatology datasets to JASMIN OS -- #
+echo "In Progress: Sending ARMOR3D P1M-m monthly climatology to Icechunk..."
+
+python3 send_ARMOR3D_P1m-m_monthly_climatology_to_os.py
+
+echo "Completed: Sent ARMOR3D P1M-m monthly climatology to Icechunk."
diff --git a/OceanDataStore/data/ARMOR3D/run_send_ARMOR3D_P1M-m_monthly_to_os.slurm b/OceanDataStore/data/ARMOR3D/run_send_ARMOR3D_P1M-m_monthly_to_os.slurm
new file mode 100755
index 00000000..d4aff9cd
--- /dev/null
+++ b/OceanDataStore/data/ARMOR3D/run_send_ARMOR3D_P1M-m_monthly_to_os.slurm
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH --job-name=armor3d_my_monthly
+#SBATCH --partition=compute
+#SBATCH --time=04:00:00
+#SBATCH --ntasks-per-core=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --ntasks-per-socket=32
+#SBATCH --nodes=1
+
+# ==============================================================
+# run_send_ARMOR3D_P1M-m_monthly_to_os.slurm
+#
+# Description: SLURM script to send the ARMOR3D P1M-m 
+# monthly dataset to Icechunk repository.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-05-29
+#
+# ==============================================================
+set -euo pipefail
+
+# -- Python Environment -- #
+# Activate miniconda environment:
+source .../miniforge3/bin/activate
+conda activate env_ods
+
+# -- Send ARMOR3D P1M-m monthly to JASMIN OS -- #
+echo "In Progress: Sending ARMOR3D P1M-m monthly to Icechunk..."
+
+python3 send_ARMOR3D_P1m-m_monthly_to_os.py
+
+echo "Completed: Sent ARMOR3D P1M-m monthly to Icechunk."
diff --git a/OceanDataStore/data/ARMOR3D/run_update_ARMOR3D_P1m-m_monthly_to_os.slurm b/OceanDataStore/data/ARMOR3D/run_update_ARMOR3D_P1m-m_monthly_to_os.slurm
new file mode 100755
index 00000000..f95c3897
--- /dev/null
+++ b/OceanDataStore/data/ARMOR3D/run_update_ARMOR3D_P1m-m_monthly_to_os.slurm
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH --job-name=armor3d_monthly_update
+#SBATCH --partition=compute
+#SBATCH --time=01:00:00
+#SBATCH --ntasks-per-core=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --ntasks-per-socket=32
+#SBATCH --nodes=1
+
+# ==============================================================
+# run_update_ARMOR3D_P1m-m_monthly_to_os.slurm
+#
+# Description: SLURM script to update the ARMOR3D P1m-m 
+# monthly dataset in Icechunk repository.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-05-29
+#
+# ==============================================================
+set -euo pipefail
+
+# -- Python Environment -- #
+# Activate miniconda environment:
+source .../miniforge3/bin/activate
+conda activate env_ods
+
+# -- Update ARMOR3D P1m-m monthly in JASMIN OS -- #
+echo "In Progress: Updating ARMOR3D P1m-m monthly in Icechunk..."
+
+python3 update_ARMOR3D_P1m-m_monthly_to_os.py
+
+echo "Completed: Updated ARMOR3D P1m-m monthly in Icechunk."
diff --git a/OceanDataStore/data/ARMOR3D/send_ARMOR3D_P1m-m_monthly_climatology_to_os.py b/OceanDataStore/data/ARMOR3D/send_ARMOR3D_P1m-m_monthly_climatology_to_os.py
new file mode 100755
index 00000000..e8b884be
--- /dev/null
+++ b/OceanDataStore/data/ARMOR3D/send_ARMOR3D_P1m-m_monthly_climatology_to_os.py
@@ -0,0 +1,99 @@
+# =========================================================
+# send_ARMOR3D_P1m-m_monthly_climatology_to_os.py
+#
+# Script to write ARMOR3D monthly climatologies to
+# Icechunk repositories in JASMIN cloud object storage.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import logging
+
+import xarray as xr
+import zarr
+
+from OceanDataStore.cli import send_to_icechunk, initialise_logging
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    # ========== Initialise OceanDataStore Logging ========== #
+    initialise_logging()
+
+    # ========== Prepare Data ========== #
+    # Open ARMOR3D monthly climatology datasets:
+    filepaths = [
+        "/dssgfs01/scratch/otooth/npd_data/observations/ARMOR3D/climatology/ARMOR3D_global_1971_2000_monthly_climatology.zarr",
+        "/dssgfs01/scratch/otooth/npd_data/observations/ARMOR3D/climatology/ARMOR3D_global_1981_2010_monthly_climatology.zarr",
+        "/dssgfs01/scratch/otooth/npd_data/observations/ARMOR3D/climatology/ARMOR3D_global_1991_2020_monthly_climatology.zarr"
+        ]
+    
+    # Define start & end years of climatology periods:
+    start_years = [1971, 1981, 1991]
+    end_years = [2000, 2010, 2020]
+
+    # ========== Send to Icechunk Repository ========== #
+    bucket = "armor3d"
+    exists = False
+    store_credentials_json = ".../credentials/jasmin_os_credentials.json"
+    branch = "main"
+    variable_commits = True
+    config_kwargs = {
+            "temporary_directory":".../OceanDataStore/OceanDataStore/data/ARMOR3D/",
+            "local_directory":".../OceanDataStore/OceanDataStore/data/ARMOR3D/"
+        }
+    cluster_kwargs = {
+            "n_workers" : 30,
+            "threads_per_worker" : 1,
+            "memory_limit":"3GB"
+        }
+    
+    for filepath, start_yr, end_yr in zip(filepaths, start_years, end_years):
+        # Open ARMOR3D monthly climatology dataset:
+        ds = xr.open_dataset(filepath, engine='zarr')
+
+        # Optimise chunk sizes for spatial analysis:
+        for var in ds.data_vars:
+            if ds[var].ndim == 4:
+                ds[var] = ds[var].chunk({'month': 1, 'depth': 3, 'latitude': 689, 'longitude': 1440})
+                ds[var].encoding['chunks'] = (1, 3, 689, 1440)
+            elif ds[var].ndim == 3:
+                if "month" in ds[var].dims:
+                    ds[var] = ds[var].chunk({'month': 1, 'latitude': 1378, 'longitude': 2880})
+                    ds[var].encoding['chunks'] = (1, 1378, 2880)
+                elif "depth" in ds[var].dims:
+                    ds[var] = ds[var].chunk({'depth': 10, 'latitude': 1378, 'longitude': 2880})
+                    ds[var].encoding['chunks'] = (10, 1378, 2880)
+            elif (ds[var].ndim == 2):
+                if "latitude" in ds[var].dims and "longitude" in ds[var].dims:
+                    ds[var] = ds[var].chunk({'latitude': 1378, 'longitude': 2880})
+                    ds[var].encoding['chunks'] = (1378, 2880)
+            elif ds[var].ndim == 1:
+                ds[var] = ds[var].chunk({'depth': 50})
+                ds[var].encoding['chunks'] = (50,)
+
+        # Update variable encodings:
+        blosccodec = zarr.codecs.BloscCodec(cname="zstd", clevel=5, shuffle=zarr.codecs.BloscShuffle.shuffle)
+        for var in list(ds.data_vars) + list(ds.coords):
+            ds[var].encoding['compressors'] = [blosccodec]
+
+        # Define prefix and commit message based on climatology period:
+        prefix = f"armor3d_global_my_{start_yr}_{end_yr}_monthly_climatology"
+        commit_message = f"Added ARMOR3D Global monthly climatology ({start_yr}-{end_yr})."
+
+        send_to_icechunk(
+            file=ds.drop_vars("time"),
+            bucket=bucket,
+            object_prefix=prefix,
+            store_credentials_json=store_credentials_json,
+            exists=exists,
+            append_dim='month',
+            branch=branch,
+            commit_message=commit_message,
+            variable_commits=variable_commits,
+            dask_config_kwargs=config_kwargs,
+            dask_cluster_kwargs=cluster_kwargs,
+            )
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/ARMOR3D/send_ARMOR3D_P1m-m_monthly_to_os.py b/OceanDataStore/data/ARMOR3D/send_ARMOR3D_P1m-m_monthly_to_os.py
new file mode 100755
index 00000000..a044c26c
--- /dev/null
+++ b/OceanDataStore/data/ARMOR3D/send_ARMOR3D_P1m-m_monthly_to_os.py
@@ -0,0 +1,147 @@
+# =========================================================
+# send_EN4.2.2_analyses_g10_to_os.py
+#
+# Script to write EN.4.2.2 analyses to Icechunk repository
+# in JASMIN cloud object storage.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import logging
+
+import xarray as xr
+import zarr
+
+from OceanDataStore.cli import initialise_logging, send_to_icechunk
+from OceanDataStore.data.utils import (
+    compute_dx,
+    compute_dy,
+    compute_cell_area,
+    compute_cell_thickness,
+    compute_land_sea_mask,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    # ========== Initialise OceanDataStore Logging ========== #
+    initialise_logging()
+
+    # ========== Prepare Data ========== #
+    # Open complete ARMOR3D REP monthly climatology dataset:
+    filepath = "/dssgfs01/scratch/otooth/npd_data/observations/ARMOR3D/armor-3d_rep_monthly_NA_*.zarr"
+    ds = xr.open_mfdataset(filepath, compat="no_conflicts", data_vars="all", engine="zarr")
+    logging.info("-> Completed: Opened ARMOR-3D REP monthly climatology dataset from Zarr stores.")
+
+    # Rename variables to standard names:
+    ds = ds.rename({"to": "thetao",
+                    "so": "so",
+                    })
+
+    # Update global attributes:
+    ds.attrs.clear()
+
+    ds = ds.assign_attrs({
+        "Conventions": "CF-1.0",
+        "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD.",
+        "description": "Multi Observation Global Ocean ARMOR3D multi-year reprocessed temperature salinity, sea surface height, geostrophic current and mixed layer depth monthly timeseries on 1/8 degree regular grid and 50 depth levels.",
+        "source": "Numerical models: Multiple Linear Regression, Optimal Interpolation. In-situ observations: Copernicus In Situ TAC (including Argo, XBT, CTD and moorings) Copernicus Sea Level TAC, CNES-CLS22 Mean Dynamic Topography, OSTIA Sea Surface Temperature Analysis, Copernicus MOB TAC (Sea Surface Salinity), and World Ocean Atlas 2018 (WOA18).",
+        "dataset_type": "observation",
+        "product_type": "timeseries",
+        "product_version": "2.0",
+        "institution": "Copernicus Marine Service, Mercator Ocean International, France",
+        "citation": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). DOI: 10.48670/moi-00052 (Accessed on 21 04 2026).",
+        "references": "Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845-857. Mulet, S., M.-H. Rio, A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. Deep Sea Research Part II : Topical Studies in Oceanography, 77-80(0):70-81.",
+        "acknowledgement": "Generated using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00052.",
+        "license": "ARMOR3D data were obtained from https://doi.org/10.48670/moi-00052, and are provided under the Copernicus Marine Environment Monitoring Service Service Level Agreement (SLA) https://marine.copernicus.eu/user-corner/service-commitments-and-licence?pk_vid=42ac3e352be888641780994034c3bb6e",
+        "doi": "10.48670/moi-00052",
+        "platform": "gr",
+        "horizontal_grid_type": "regular rectilinear",
+        "horizontal_grid_resolution": "0.125 degree",
+        "vertical_grid_type": "z",
+        "vertical_grid_coordinate": "depth",
+        "vertical_grid_levels": 50,
+        "aggregation": "mean",
+        "aggregation_frequency": "monthly",
+        "status": "ongoing",
+        "update_frequency": "quarterly",
+        "bbox": "[-180.0, 180.0, -90.0, 90.0]",
+    })
+
+    # Add ancillary variables:
+    ds['mask'] = compute_land_sea_mask(ds['thetao'].isel(time=0, depth=0))
+    ds['dx'] = compute_dx(ds)
+    ds['dy'] = compute_dy(ds)
+    ds['cell_area'] = compute_cell_area(ds)
+    # Custom ancillary variables:
+    ds['cell_thickness'] = compute_cell_thickness(ds)
+    ds['cell_volume'] = ds['cell_thickness'] * ds['cell_area']
+
+    # Update attributes for custom ancillary variables:
+    ds['cell_volume'].attrs.update({
+        'long_name': "Grid-Cell Volume",
+        'standard_name': "cell_volume",
+        'units': "m3",
+    })
+
+    # ========== Send to Icechunk Repository ========== #
+    bucket = "armor3d"
+    exists = False
+    store_credentials_json = ".../credentials/jasmin_os_credentials.json"
+    branch = "main"
+    variable_commits = True
+    config_kwargs = {
+            "temporary_directory":".../OceanDataStore/OceanDataStore/data/ARMOR3D/",
+            "local_directory":".../OceanDataStore/OceanDataStore/data/ARMOR3D/"
+        }
+    cluster_kwargs = {
+            "n_workers" : 25,
+            "threads_per_worker" : 1,
+            "memory_limit":"4GB"
+        }
+    
+    # Optimise chunk sizes for spatial analysis:
+    for var in ds.data_vars:
+        if ds[var].ndim == 4:
+            ds[var] = ds[var].chunk({'time': 1, 'depth': 3, 'latitude': 689, 'longitude': 1440})
+            ds[var].encoding['chunks'] = (1, 3, 689, 1440)
+        elif ds[var].ndim == 3:
+            if "time" in ds[var].dims:
+                ds[var] = ds[var].chunk({'time': 1, 'latitude': 1378, 'longitude': 2880})
+                ds[var].encoding['chunks'] = (1, 1378, 2880)
+            elif "depth" in ds[var].dims:
+                ds[var] = ds[var].chunk({'depth': 10, 'latitude': 1378, 'longitude': 2880})
+                ds[var].encoding['chunks'] = (10, 1378, 2880)
+        elif (ds[var].ndim == 2):
+            if "latitude" in ds[var].dims and "longitude" in ds[var].dims:
+                ds[var] = ds[var].chunk({'latitude': 1378, 'longitude': 2880})
+                ds[var].encoding['chunks'] = (1378, 2880)
+        elif ds[var].ndim == 1:
+            ds[var] = ds[var].chunk({'depth': 50})
+            ds[var].encoding['chunks'] = (50,)
+
+    # Update variable encodings:
+    blosccodec = zarr.codecs.BloscCodec(cname="zstd", clevel=5, shuffle=zarr.codecs.BloscShuffle.shuffle)
+    for var in list(ds.data_vars) + list(ds.coords):
+        ds[var].encoding['compressors'] = [blosccodec]
+
+    # Define prefix and commit message based on period:
+    prefix = "armor3d_global_my_monthly"
+    commit_message = "Added ARMOR3D Global Monthly (1993-1999)."
+
+    send_to_icechunk(
+        file=ds.sel(time=slice("1993-01-01", "1999-12-31")),
+        bucket=bucket,
+        object_prefix=prefix,
+        store_credentials_json=store_credentials_json,
+        exists=exists,
+        append_dim='time',
+        branch=branch,
+        commit_message=commit_message,
+        variable_commits=variable_commits,
+        dask_config_kwargs=config_kwargs,
+        dask_cluster_kwargs=cluster_kwargs,
+        )
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/ARMOR3D/update_ARMOR3D_P1m-m_monthly_to_os.py b/OceanDataStore/data/ARMOR3D/update_ARMOR3D_P1m-m_monthly_to_os.py
new file mode 100755
index 00000000..3d500270
--- /dev/null
+++ b/OceanDataStore/data/ARMOR3D/update_ARMOR3D_P1m-m_monthly_to_os.py
@@ -0,0 +1,143 @@
+# =========================================================
+# update_EN4.2.2_analyses_g10_to_os.py
+#
+# Script to update EN.4.2.2 analyses in Icechunk repository
+# in JASMIN cloud object storage.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import logging
+
+import xarray as xr
+import zarr
+
+from OceanDataStore.cli import initialise_logging, update_icechunk
+from OceanDataStore.data.utils import (
+    compute_dx,
+    compute_dy,
+    compute_cell_area,
+    compute_cell_thickness,
+    compute_land_sea_mask,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    # ========== Initialise OceanDataStore Logging ========== #
+    initialise_logging()
+
+    # ========== Prepare Data ========== #
+    # Open complete ARMOR3D REP monthly climatology dataset:
+    filepath = "/dssgfs01/scratch/otooth/npd_data/observations/ARMOR3D/armor-3d_rep_monthly_NA_*.zarr"
+    ds = xr.open_mfdataset(filepath, compat="no_conflicts", data_vars="all", engine="zarr")
+    logging.info("-> Completed: Opened ARMOR-3D REP monthly climatology dataset from Zarr stores.")
+
+    # Rename variables to standard names:
+    ds = ds.rename({"to": "thetao",
+                    "so": "so",
+                    })
+
+    # Update global attributes:
+    ds.attrs.clear()
+
+    ds = ds.assign_attrs({
+        "Conventions": "CF-1.0",
+        "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD.",
+        "description": "Multi Observation Global Ocean ARMOR3D multi-year reprocessed temperature salinity, sea surface height, geostrophic current and mixed layer depth monthly timeseries on 1/8 degree regular grid and 50 depth levels.",
+        "source": "Numerical models: Multiple Linear Regression, Optimal Interpolation. In-situ observations: Copernicus In Situ TAC (including Argo, XBT, CTD and moorings) Copernicus Sea Level TAC, CNES-CLS22 Mean Dynamic Topography, OSTIA Sea Surface Temperature Analysis, Copernicus MOB TAC (Sea Surface Salinity), and World Ocean Atlas 2018 (WOA18).",
+        "dataset_type": "observation",
+        "product_type": "timeseries",
+        "product_version": "2.0",
+        "institution": "Copernicus Marine Service, Mercator Ocean International, France",
+        "citation": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). DOI: 10.48670/moi-00052 (Accessed on 21 04 2026).",
+        "references": "Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845-857. Mulet, S., M.-H. Rio, A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. Deep Sea Research Part II : Topical Studies in Oceanography, 77-80(0):70-81.",
+        "acknowledgement": "Generated using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00052.",
+        "license": "ARMOR3D data were obtained from https://doi.org/10.48670/moi-00052, and are provided under the Copernicus Marine Environment Monitoring Service Service Level Agreement (SLA) https://marine.copernicus.eu/user-corner/service-commitments-and-licence?pk_vid=42ac3e352be888641780994034c3bb6e",
+        "doi": "10.48670/moi-00052",
+        "platform": "gr",
+        "horizontal_grid_type": "regular rectilinear",
+        "horizontal_grid_resolution": "0.125 degree",
+        "vertical_grid_type": "z",
+        "vertical_grid_coordinate": "depth",
+        "vertical_grid_levels": 50,
+        "aggregation": "mean",
+        "aggregation_frequency": "monthly",
+        "status": "ongoing",
+        "update_frequency": "quarterly",
+        "bbox": "[-180.0, 180.0, -90.0, 90.0]",
+    })
+
+    # Add ancillary variables:
+    ds['mask'] = compute_land_sea_mask(ds['thetao'].isel(time=0, depth=0))
+    ds['dx'] = compute_dx(ds)
+    ds['dy'] = compute_dy(ds)
+    ds['cell_area'] = compute_cell_area(ds)
+    # Custom ancillary variables:
+    ds['cell_thickness'] = compute_cell_thickness(ds)
+    ds['cell_volume'] = ds['cell_thickness'] * ds['cell_area']
+
+    # Update attributes for custom ancillary variables:
+    ds['cell_volume'].attrs.update({
+        'long_name': "Grid-Cell Volume",
+        'standard_name': "cell_volume",
+        'units': "m3",
+    })
+
+    # ========== Send to Icechunk Repository ========== #
+    bucket = "armor3d"
+    store_credentials_json = ".../credentials/jasmin_os_credentials.json"
+    branch = "main"
+    config_kwargs = {
+            "temporary_directory":".../OceanDataStore/OceanDataStore/data/ARMOR3D/",
+            "local_directory":".../OceanDataStore/OceanDataStore/data/ARMOR3D/"
+        }
+    cluster_kwargs = {
+            "n_workers" : 25,
+            "threads_per_worker" : 1,
+            "memory_limit":"4GB"
+        }
+    
+    # Optimise chunk sizes for spatial analysis:
+    for var in ds.data_vars:
+        if ds[var].ndim == 4:
+            ds[var] = ds[var].chunk({'time': 1, 'depth': 3, 'latitude': 689, 'longitude': 1440})
+            ds[var].encoding['chunks'] = (1, 3, 689, 1440)
+        elif ds[var].ndim == 3:
+            if "time" in ds[var].dims:
+                ds[var] = ds[var].chunk({'time': 1, 'latitude': 1378, 'longitude': 2880})
+                ds[var].encoding['chunks'] = (1, 1378, 2880)
+            elif "depth" in ds[var].dims:
+                ds[var] = ds[var].chunk({'depth': 10, 'latitude': 1378, 'longitude': 2880})
+                ds[var].encoding['chunks'] = (10, 1378, 2880)
+        elif (ds[var].ndim == 2):
+            if "latitude" in ds[var].dims and "longitude" in ds[var].dims:
+                ds[var] = ds[var].chunk({'latitude': 1378, 'longitude': 2880})
+                ds[var].encoding['chunks'] = (1378, 2880)
+        elif ds[var].ndim == 1:
+            ds[var] = ds[var].chunk({'depth': 50})
+            ds[var].encoding['chunks'] = (50,)
+
+    # Update variable encodings:
+    blosccodec = zarr.codecs.BloscCodec(cname="zstd", clevel=5, shuffle=zarr.codecs.BloscShuffle.shuffle)
+    for var in list(ds.data_vars) + list(ds.coords):
+        ds[var].encoding['compressors'] = [blosccodec]
+
+    # Define prefix and commit message based on period:
+    prefix = "armor3d_global_my_monthly"
+    commit_message = "Added ARMOR3D Global Monthly (2000-2024)."
+
+    update_icechunk(
+        file=ds.sel(time=slice("2000-01-01", "2024-12-31")),
+        bucket=bucket,
+        object_prefix=prefix,
+        store_credentials_json=store_credentials_json,
+        append_dim='time',
+        branch=branch,
+        commit_message=commit_message,
+        dask_config_kwargs=config_kwargs,
+        dask_cluster_kwargs=cluster_kwargs,
+        )
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/EN.4.2.2/create_EN4.2.2_analysis_g10_climatology.py b/OceanDataStore/data/EN.4.2.2/create_EN4.2.2_analysis_g10_climatology.py
new file mode 100755
index 00000000..75e55cc4
--- /dev/null
+++ b/OceanDataStore/data/EN.4.2.2/create_EN4.2.2_analysis_g10_climatology.py
@@ -0,0 +1,162 @@
+# =========================================================
+# create_EN4.2.2_analysis_g10_climatology.py
+#
+# Script to create EN.4.2.2 analyses climatologies for
+# climate normals (1971-2000, 1981-2010, 1991-2020) and
+# write to local netCDF files.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import logging
+
+import numpy as np
+import xarray as xr
+import zarr
+
+from OceanDataStore.cli import initialise_logging
+from OceanDataStore.data.utils import (
+    compute_cell_area,
+    compute_dx,
+    compute_dy,
+    compute_land_sea_mask,
+)
+
+logger = logging.getLogger(__name__)
+
+def main():
+    # ========== Initialize Logging and Print Banner ========== #
+    initialise_logging()
+
+    # ========== Prepare Data ========== #
+    # Open complete EN.4.2.2 analysis dataset:
+    filepath = "/dssgfs01/scratch/otooth/npd_data/observations/EN.4.2.2/EN.4.2.2.f.analysis.g10.*.nc"
+    ds = xr.open_mfdataset(filepath, combine="by_coords", data_vars="all", engine="netcdf4")
+    logging.info("-> Completed: Opened EN.4.2.2 analysis dataset from netCDF files.")
+
+    # Standardise coordinate dimension names:
+    ds = ds.rename({"lon": "longitude", "lat": "latitude"})
+
+    # Update longitude coordinates to be in the range [-180, 180]:
+    ds = ds.assign_coords(
+        longitude=((ds["longitude"] + 180) % 360) - 180
+    )
+    ds = ds.sortby("longitude")
+
+    # Rename variables to standard names:
+    ds = ds.rename({"temperature": "thetao",
+                    "salinity": "so",
+                    "temperature_uncertainty": "thetao_uncertainty",
+                    "salinity_uncertainty": "so_uncertainty",
+                    "temperature_observation_weights": "thetao_obs_weights",
+                    "salinity_observation_weights": "so_obs_weights"
+                    })
+    
+    # Update variable attributes:
+    ds["thetao"].attrs.update({
+        "long_name": "Potential Temperature",
+    })
+    ds["so"].attrs.update({
+        "long_name": "Practical Salinity",
+    })
+    ds["thetao_uncertainty"].attrs.update({
+        "long_name": "Potential Temperature Error Standard Deviation",
+    })
+    ds["so_uncertainty"].attrs.update({
+        "long_name": "Practical Salinity Error Standard Deviation",
+    })
+    ds["thetao_obs_weights"].attrs.update({
+        "long_name": "Potential Temperature Observation Weights",
+    })
+    ds["so_obs_weights"].attrs.update({
+        "long_name": "Practical Salinity Observation Weights",
+    })
+
+    # Update global attributes:
+    ds.attrs.clear()
+
+    ds = ds.assign_attrs({
+        "Conventions": "CF-1.0",
+        "title": "EN.4.2.2 ocean temperature and salinity monthly climatology.",
+        "description": "EN.4.2.2 quality controlled ocean temperature and salinity monthly climatology 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]",
+    })
+
+    logging.info("-> Completed: Updated EN.4.2.2 analysis CF-metadata.")
+
+    # -- Calculate climate normal monthly climatologies -- #
+    output_dir = "/dssgfs01/scratch/otooth/npd_data/observations/EN.4.2.2/climatology/"
+    start_years = [1971, 1981, 1991]
+    end_years = [2000, 2010, 2020]
+
+    for start_year, end_year in zip(start_years, end_years):
+        logging.info(f"In Progress: Calculating monthly climatology for {start_year}-{end_year} climate normal period...")
+        # Calculate monthly climatology for the specified period:
+        ds_climatology = ds.sel(time=slice(f'{start_year}-01', f'{end_year}-12')).groupby('time.month').mean()
+
+        # Add ancillary variables:
+        ds_climatology['mask'] = compute_land_sea_mask(ds['thetao'].isel(time=0, depth=0))
+        ds_climatology['dx'] = compute_dx(ds)
+        ds_climatology['dy'] = compute_dy(ds)
+        ds_climatology['cell_area'] = compute_cell_area(ds)
+        # Custom ancillary variables:
+        ds_climatology['cell_thickness'] = (ds_climatology['depth_bnds'].isel(bnds=1) - ds_climatology['depth_bnds'].isel(bnds=0)).isel(month=0)
+        ds_climatology['cell_volume'] = ds_climatology['cell_thickness'] * ds_climatology['cell_area']
+
+        # Update attributes for custom ancillary variables:
+        ds_climatology['cell_thickness'].attrs.update({
+            'long_name': "Grid-Cell Thickness",
+            'standard_name': "cell_thickness",
+            'units': "m",
+        })
+        ds_climatology['cell_volume'].attrs.update({
+            'long_name': "Grid-Cell Volume",
+            'standard_name': "cell_volume",
+            'units': "m3",
+        })
+
+        # Update time bounds to reflect climatological period:
+        ds_climatology['time_bnds'][:, 0] = np.arange(f'{start_year}-01', f'{start_year+1}-01', dtype='datetime64[M]')
+        ds_climatology['time_bnds'][:, 1] = np.arange(f'{end_year}-01', f'{end_year+1}-01', dtype='datetime64[M]')
+        logging.info(f"-> Completed: Calculated monthly climatology for {start_year}-{end_year} climate normal period.")
+
+        # Update title attribute to reflect climatological period:
+        ds_climatology.attrs['title'] = f"EN.4.2.2 ocean temperature and salinity monthly climatology ({start_year}-{end_year})."
+
+        ds_climatology.attrs['description'] = f"EN.4.2.2: quality controlled ocean temperature and salinity monthly climatology ({start_year}-{end_year}) from objective analyses with uncertainty estimates using Gouretski and Reseghetti (2010) corrections."
+
+        # Update variable encodings:
+        blosccodec = zarr.codecs.BloscCodec(cname="zstd", clevel=3, shuffle=zarr.codecs.BloscShuffle.shuffle)
+        for var in list(ds_climatology.data_vars) + list(ds_climatology.coords):
+            ds_climatology[var].encoding['compressors'] = [blosccodec]
+
+        # Write monthly climatology to netCDF:
+        output_filepath = f"{output_dir}EN.4.2.2.f.analysis.g10.{start_year}_{end_year}_monthly_climatology.nc"
+        ds_climatology.to_netcdf(output_filepath)
+        logging.info(f"-> Completed: Saved monthly climatology for {start_year}-{end_year} climate normal period to {output_filepath}.")
+
+    # -- Close EN.4.2.2 analysis datasets -- #
+    ds_climatology.close()
+    ds.close()
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/EN.4.2.2/download_EN4.2.2_analysis_g10_data.sh b/OceanDataStore/data/EN.4.2.2/download_EN4.2.2_analysis_g10_data.sh
new file mode 100755
index 00000000..cdf905ac
--- /dev/null
+++ b/OceanDataStore/data/EN.4.2.2/download_EN4.2.2_analysis_g10_data.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+
+# ----------------------------------------------------------------
+# download_EN4.2.2_analyses_g10_data.sh
+#
+# Description: Download the EN.4.2.2 analyses.g10 dataset from the
+# Met Office Hadley Centre EN.4.2.2 website:
+# http://www.metoffice.gov.uk/hadobs/en4
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-05-27
+# ----------------------------------------------------------------
+set -euo pipefail
+
+# --- Inputs --- #
+output_dir="/dssgfs01/scratch/otooth/npd_data/observations/EN.4.2.2/"
+
+# -- Defaults -- #
+base_url="http://www.metoffice.gov.uk/hadobs/en4/data/en4-2-1"
+
+# --- Main Script --- #
+echo "==================================================="
+echo "          Downloading EN.4.2.2 Analyses"
+echo "                     v0.1.0"
+echo "               Oliver J. Tooth, NOC"
+echo "==================================================="
+echo "In Progress: Downloading EN.4.2.2 analyses dataset..."
+# Iterate over years:
+for yr in {1990..2026}; do
+    # Construct URL for current year:
+    if [ $yr -ge 2021 ]; then
+        url="$base_url/EN.4.2.2.analyses.g10.${yr}.zip"
+    else 
+        url="$base_url/EN.4.2.2/EN.4.2.2.analyses.g10.${yr}.zip"
+    fi
+
+    # Download and unzip file if not in output directory:
+    nc_files=("${output_dir}/EN.4.2.2.f.analysis.g10.${yr}"*.nc)
+    filepath="$output_dir/$(basename $url)"
+    if [ ${#nc_files[@]} -ne 12 ]; then
+        wget -P $output_dir $url
+        echo "-> Completed: Downloaded $filepath."
+
+        unzip "$filepath" -d $output_dir
+        echo "-> Completed: Unzipped $filepath."
+    else
+        echo "-> Skipping Download: NetCDF files for ${yr} already exist in $output_dir."
+    fi
+done
+
+echo "======================================="
diff --git a/OceanDataStore/data/EN.4.2.2/run_send_EN4.2.2_analysis_g10_climatology_to_os.slurm b/OceanDataStore/data/EN.4.2.2/run_send_EN4.2.2_analysis_g10_climatology_to_os.slurm
new file mode 100755
index 00000000..e6cce063
--- /dev/null
+++ b/OceanDataStore/data/EN.4.2.2/run_send_EN4.2.2_analysis_g10_climatology_to_os.slurm
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH --job-name=en4.2.2_analysis_g10_climatology
+#SBATCH --partition=test
+#SBATCH --time=00:20:00
+#SBATCH --ntasks-per-core=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --ntasks-per-socket=32
+#SBATCH --nodes=1
+
+# ==============================================================
+# run_send_EN4.2.2_analysis_g10_climatology_to_os.slurm
+#
+# Description: SLURM script to send the EN.4.2.2 analysis g10 
+# climatology datasets to Icechunk repository.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-05-29
+#
+# ==============================================================
+set -euo pipefail
+
+# -- Python Environment -- #
+# Activate miniconda environment:
+source .../miniforge3/bin/activate
+conda activate env_ods
+
+# -- Send EN.4.2.2.analysis.g10 climatology datasets to JASMIN OS -- #
+echo "In Progress: Sending EN.4.2.2.analysis.g10 climatology to Icechunk..."
+
+python3 send_EN4.2.2_analysis_g10_monthly_climatology_to_os.py
+
+echo "Completed: Sent EN.4.2.2.analysis.g10 climatology to Icechunk."
diff --git a/OceanDataStore/data/EN.4.2.2/run_send_EN4.2.2_analysis_g10_monthly_to_os.slurm b/OceanDataStore/data/EN.4.2.2/run_send_EN4.2.2_analysis_g10_monthly_to_os.slurm
new file mode 100755
index 00000000..82c3ad6f
--- /dev/null
+++ b/OceanDataStore/data/EN.4.2.2/run_send_EN4.2.2_analysis_g10_monthly_to_os.slurm
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH --job-name=en4.2.2_analysis_g10_monthly
+#SBATCH --partition=compute
+#SBATCH --time=01:00:00
+#SBATCH --ntasks-per-core=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --ntasks-per-socket=32
+#SBATCH --nodes=1
+
+# ==============================================================
+# run_send_EN4.2.2_analysis_g10_monthly_to_os.slurm
+#
+# Description: SLURM script to send the EN.4.2.2 analysis g10 
+# monthly datasets to Icechunk repository.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-05-29
+#
+# ==============================================================
+set -euo pipefail
+
+# -- Python Environment -- #
+# Activate miniconda environment:
+source .../miniforge3/bin/activate
+conda activate env_ods
+
+# -- Send EN.4.2.2.analysis.g10 monthly to JASMIN OS -- #
+echo "In Progress: Sending EN.4.2.2.analysis.g10 monthly to Icechunk..."
+
+python3 send_EN4.2.2_analysis_g10_monthly_to_os.py
+
+echo "Completed: Sent EN.4.2.2.analysis.g10 monthly to Icechunk."
diff --git a/OceanDataStore/data/EN.4.2.2/run_update_EN4.2.2_analysis_g10_monthly_to_os.slurm b/OceanDataStore/data/EN.4.2.2/run_update_EN4.2.2_analysis_g10_monthly_to_os.slurm
new file mode 100755
index 00000000..516362b0
--- /dev/null
+++ b/OceanDataStore/data/EN.4.2.2/run_update_EN4.2.2_analysis_g10_monthly_to_os.slurm
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH --job-name=en4.2.2_analysis_g10_monthly
+#SBATCH --partition=compute
+#SBATCH --time=01:00:00
+#SBATCH --ntasks-per-core=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --ntasks-per-socket=32
+#SBATCH --nodes=1
+
+# ==============================================================
+# run_update_EN4.2.2_analysis_g10_monthly_to_os.slurm
+#
+# Description: SLURM script to update the EN.4.2.2 analysis g10 
+# monthly datasets in Icechunk repository.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-05-29
+#
+# ==============================================================
+set -euo pipefail
+
+# -- Python Environment -- #
+# Activate miniconda environment:
+source .../miniforge3/bin/activate
+conda activate env_ods
+
+# -- Update EN.4.2.2.analysis.g10 monthly in JASMIN OS -- #
+echo "In Progress: Updating EN.4.2.2.analysis.g10 monthly in Icechunk..."
+
+python3 update_EN4.2.2_analysis_g10_monthly_to_os.py
+
+echo "Completed: Updated EN.4.2.2.analysis.g10 monthly in Icechunk."
diff --git a/OceanDataStore/data/EN.4.2.2/send_EN4.2.2_analysis_g10_monthly_climatology_to_os.py b/OceanDataStore/data/EN.4.2.2/send_EN4.2.2_analysis_g10_monthly_climatology_to_os.py
new file mode 100755
index 00000000..13f680e2
--- /dev/null
+++ b/OceanDataStore/data/EN.4.2.2/send_EN4.2.2_analysis_g10_monthly_climatology_to_os.py
@@ -0,0 +1,76 @@
+# =========================================================
+# send_EN4.2.2_analysis_g10_climatology_to_os.py
+#
+# Script to write EN.4.2.2 analysis climatologies to
+# Icechunk repositories in JASMIN cloud object storage.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import logging
+
+import xarray as xr
+
+from OceanDataStore.cli import send_to_icechunk, initialise_logging
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    # ========== Initialise OceanDataStore Logging ========== #
+    initialise_logging()
+
+    # ========== Prepare Data ========== #
+    # Open EN.4.2.2 analysis climatology datasets:
+    filepaths = [
+        "/dssgfs01/scratch/otooth/npd_data/observations/EN.4.2.2/climatology/EN.4.2.2.f.analysis.g10.1971_2000_monthly_climatology.nc",
+        "/dssgfs01/scratch/otooth/npd_data/observations/EN.4.2.2/climatology/EN.4.2.2.f.analysis.g10.1981_2010_monthly_climatology.nc",
+        "/dssgfs01/scratch/otooth/npd_data/observations/EN.4.2.2/climatology/EN.4.2.2.f.analysis.g10.1991_2020_monthly_climatology.nc"
+        ]
+    
+    # Define start & end years of climatology periods:
+    start_years = [1971, 1981, 1991]
+    end_years = [2000, 2010, 2020]
+
+    # ========== Send to Icechunk Repository ========== #
+    bucket = "en4.2.2"
+    exists = False
+    store_credentials_json = ".../credentials/jasmin_os_credentials.json"
+    branch = "main"
+    variable_commits = True
+    config_kwargs = {
+            "temporary_directory":".../OceanDataStore/OceanDataStore/data/EN.4.2.2/",
+            "local_directory":".../OceanDataStore/OceanDataStore/data/EN.4.2.2/"
+        }
+    cluster_kwargs = {
+            "n_workers" : 10,
+            "threads_per_worker" : 1,
+            "memory_limit":"3GB"
+        }
+    
+    for filepath, start_yr, end_yr in zip(filepaths, start_years, end_years):
+        # Open EN.4.2.2 analysis climatology dataset:
+        ds = xr.open_dataset(filepath, engine='netcdf4')
+
+        # Optimise chunk sizes for spatial analysis:
+        ds = ds.chunk({'month': 1, 'depth': 20, 'latitude': 173, 'longitude': 360})
+
+        # Define prefix and commit message based on climatology period:
+        prefix = f"en4.2.2_analysis_g10_{start_yr}_{end_yr}_monthly_climatology"
+        commit_message = f"Added EN.4.2.2.analysis.g10 climatology ({start_yr}-{end_yr})."
+
+        send_to_icechunk(
+            file=ds,
+            bucket=bucket,
+            object_prefix=prefix,
+            store_credentials_json=store_credentials_json,
+            exists=exists,
+            append_dim='month',
+            branch=branch,
+            commit_message=commit_message,
+            variable_commits=variable_commits,
+            dask_config_kwargs=config_kwargs,
+            dask_cluster_kwargs=cluster_kwargs,
+            )
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/EN.4.2.2/send_EN4.2.2_analysis_g10_monthly_to_os.py b/OceanDataStore/data/EN.4.2.2/send_EN4.2.2_analysis_g10_monthly_to_os.py
new file mode 100755
index 00000000..66c2198a
--- /dev/null
+++ b/OceanDataStore/data/EN.4.2.2/send_EN4.2.2_analysis_g10_monthly_to_os.py
@@ -0,0 +1,165 @@
+# =========================================================
+# send_EN4.2.2_analyses_g10_to_os.py
+#
+# Script to write EN.4.2.2 analyses to Icechunk repository
+# in JASMIN cloud object storage.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import logging
+
+import xarray as xr
+import zarr
+
+from OceanDataStore.cli import initialise_logging, send_to_icechunk
+from OceanDataStore.data.utils import (
+    compute_cell_area,
+    compute_dx,
+    compute_dy,
+    compute_land_sea_mask,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    # ========== Initialise OceanDataStore Logging ========== #
+    initialise_logging()
+
+    # ========== Prepare Data ========== #
+    # Open EN.4.2.2 analyses dataset:
+    filepath = "/dssgfs01/scratch/otooth/npd_data/observations/EN.4.2.2/EN.4.2.2.f.analysis.g10.19*.nc"
+    ds = xr.open_mfdataset(filepath, combine="by_coords", data_vars="all", engine="netcdf4")
+
+    # Standardise coordinate dimension names:
+    ds = ds.rename({"lon": "longitude", "lat": "latitude"})
+
+    # Update longitude coordinates to be in the range [-180, 180]:
+    ds = ds.assign_coords(
+        longitude=((ds["longitude"] + 180) % 360) - 180
+    )
+    ds = ds.sortby("longitude")
+
+    # Rename variables to standard names:
+    ds = ds.rename({"temperature": "thetao",
+                    "salinity": "so",
+                    "temperature_uncertainty": "thetao_uncertainty",
+                    "salinity_uncertainty": "so_uncertainty",
+                    "temperature_observation_weights": "thetao_obs_weights",
+                    "salinity_observation_weights": "so_obs_weights"
+                    })
+    
+    # Update variable attributes:
+    ds["thetao"].attrs.update({
+        "long_name": "Potential Temperature",
+    })
+    ds["so"].attrs.update({
+        "long_name": "Practical Salinity",
+    })
+    ds["thetao_uncertainty"].attrs.update({
+        "long_name": "Potential Temperature Error Standard Deviation",
+    })
+    ds["so_uncertainty"].attrs.update({
+        "long_name": "Practical Salinity Error Standard Deviation",
+    })
+    ds["thetao_obs_weights"].attrs.update({
+        "long_name": "Potential Temperature Observation Weights",
+    })
+    ds["so_obs_weights"].attrs.update({
+        "long_name": "Practical Salinity Observation Weights",
+    })
+
+    # Update global attributes:
+    ds.attrs.clear()
+
+    ds = ds.assign_attrs({
+        "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]",
+    })
+
+    # Add ancillary variables:
+    ds['mask'] = compute_land_sea_mask(ds['thetao'].isel(time=0, depth=0))
+    ds['dx'] = compute_dx(ds)
+    ds['dy'] = compute_dy(ds)
+    ds['cell_area'] = compute_cell_area(ds)
+
+    # Custom ancillary variables:
+    ds['cell_thickness'] = (ds['depth_bnds'].isel(bnds=1) - ds['depth_bnds'].isel(bnds=0)).isel(time=0)
+    ds['cell_volume'] = ds['cell_thickness'] * ds['cell_area']
+
+    # Update attributes for custom ancillary variables:
+    ds['cell_thickness'].attrs.update({
+        'long_name': "Grid-Cell Thickness",
+        'standard_name': "cell_thickness",
+        'units': "m",
+    })
+    ds['cell_volume'].attrs.update({
+        'long_name': "Grid-Cell Volume",
+        'standard_name': "cell_volume",
+        'units': "m3",
+    })
+
+    # ========== Send to Icechunk Repository ========== #
+    bucket = "en4.2.2"
+    prefix = "en4.2.2_analysis_g10_monthly"
+    exists = False
+    store_credentials_json = ".../credentials/jasmin_os_credentials.json"
+    branch = "main"
+    commit_message = "Added EN.4.2.2.analysis.g10 monthly (1950-01-1999-12)."
+    variable_commits = True
+    config_kwargs = {
+            "temporary_directory":".../OceanDataStore/OceanDataStore/data/EN.4.2.2/",
+            "local_directory":".../OceanDataStore/OceanDataStore/data/EN.4.2.2/"
+        }
+    cluster_kwargs = {
+            "n_workers" : 25,
+            "threads_per_worker" : 1,
+            "memory_limit":"3GB"
+        }
+    
+    # Optimise chunk sizes for spatial analysis:
+    ds = ds.chunk({'time': 1, 'depth': 20, 'latitude': 173, 'longitude': 360})
+
+    # Update variable encodings:
+    blosccodec = zarr.codecs.BloscCodec(cname="zstd", clevel=3, shuffle=zarr.codecs.BloscShuffle.shuffle)
+    for var in list(ds.data_vars) + list(ds.coords):
+        ds[var].encoding['compressors'] = [blosccodec]
+
+    send_to_icechunk(
+        file=ds,
+        bucket=bucket,
+        object_prefix=prefix,
+        store_credentials_json=store_credentials_json,
+        exists=exists,
+        append_dim='time',
+        branch=branch,
+        commit_message=commit_message,
+        variable_commits=variable_commits,
+        dask_config_kwargs=config_kwargs,
+        dask_cluster_kwargs=cluster_kwargs,
+        )
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/EN.4.2.2/update_EN4.2.2_analysis_g10_monthly_to_os.py b/OceanDataStore/data/EN.4.2.2/update_EN4.2.2_analysis_g10_monthly_to_os.py
new file mode 100755
index 00000000..a26e1c8a
--- /dev/null
+++ b/OceanDataStore/data/EN.4.2.2/update_EN4.2.2_analysis_g10_monthly_to_os.py
@@ -0,0 +1,161 @@
+# =========================================================
+# update_EN4.2.2_analyses_g10_to_os.py
+#
+# Script to update EN.4.2.2 analyses in Icechunk repository
+# in JASMIN cloud object storage.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import logging
+
+import xarray as xr
+import zarr
+
+from OceanDataStore.cli import initialise_logging, update_icechunk
+from OceanDataStore.data.utils import (
+    compute_cell_area,
+    compute_dx,
+    compute_dy,
+    compute_land_sea_mask,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    # ========== Initialise OceanDataStore Logging ========== #
+    initialise_logging()
+
+    # ========== Prepare Data ========== #
+    # Open EN.4.2.2 analyses dataset:
+    filepath = "/dssgfs01/scratch/otooth/npd_data/observations/EN.4.2.2/EN.4.2.2.f.analysis.g10.20*.nc"
+    ds = xr.open_mfdataset(filepath, combine="by_coords", data_vars="all", engine="netcdf4")
+
+    # Standardise coordinate dimension names:
+    ds = ds.rename({"lon": "longitude", "lat": "latitude"})
+
+    # Update longitude coordinates to be in the range [-180, 180]:
+    ds = ds.assign_coords(
+        longitude=((ds["longitude"] + 180) % 360) - 180
+    )
+    ds = ds.sortby("longitude")
+
+    # Rename variables to standard names:
+    ds = ds.rename({"temperature": "thetao",
+                    "salinity": "so",
+                    "temperature_uncertainty": "thetao_uncertainty",
+                    "salinity_uncertainty": "so_uncertainty",
+                    "temperature_observation_weights": "thetao_obs_weights",
+                    "salinity_observation_weights": "so_obs_weights"
+                    })
+    
+    # Update variable attributes:
+    ds["thetao"].attrs.update({
+        "long_name": "Potential Temperature",
+    })
+    ds["so"].attrs.update({
+        "long_name": "Practical Salinity",
+    })
+    ds["thetao_uncertainty"].attrs.update({
+        "long_name": "Potential Temperature Error Standard Deviation",
+    })
+    ds["so_uncertainty"].attrs.update({
+        "long_name": "Practical Salinity Error Standard Deviation",
+    })
+    ds["thetao_obs_weights"].attrs.update({
+        "long_name": "Potential Temperature Observation Weights",
+    })
+    ds["so_obs_weights"].attrs.update({
+        "long_name": "Practical Salinity Observation Weights",
+    })
+
+    # Update global attributes:
+    ds.attrs.clear()
+
+    ds = ds.assign_attrs({
+        "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]",
+    })
+
+    # Add ancillary variables:
+    ds['mask'] = compute_land_sea_mask(ds['thetao'].isel(time=0, depth=0))
+    ds['dx'] = compute_dx(ds)
+    ds['dy'] = compute_dy(ds)
+    ds['cell_area'] = compute_cell_area(ds)
+
+    # Custom ancillary variables:
+    ds['cell_thickness'] = (ds['depth_bnds'].isel(bnds=1) - ds['depth_bnds'].isel(bnds=0)).isel(time=0)
+    ds['cell_volume'] = ds['cell_thickness'] * ds['cell_area']
+
+    # Update attributes for custom ancillary variables:
+    ds['cell_thickness'].attrs.update({
+        'long_name': "Grid-Cell Thickness",
+        'standard_name': "cell_thickness",
+        'units': "m",
+    })
+    ds['cell_volume'].attrs.update({
+        'long_name': "Grid-Cell Volume",
+        'standard_name': "cell_volume",
+        'units': "m3",
+    })
+
+    # ========== Send to Icechunk Repository ========== #
+    bucket = "en4.2.2"
+    prefix = "en4.2.2_analysis_g10_monthly"
+    store_credentials_json = ".../credentials/jasmin_os_credentials.json"
+    branch = "main"
+    commit_message = "Added EN.4.2.2.analysis.g10 monthly (2000-01-2026-03)."
+    config_kwargs = {
+            "temporary_directory":".../OceanDataStore/OceanDataStore/data/EN.4.2.2/",
+            "local_directory":".../OceanDataStore/OceanDataStore/data/EN.4.2.2/"
+        }
+    cluster_kwargs = {
+            "n_workers" : 25,
+            "threads_per_worker" : 1,
+            "memory_limit":"3GB"
+        }
+    
+    # Optimise chunk sizes for spatial analysis:
+    ds = ds.chunk({'time': 1, 'depth': 20, 'latitude': 173, 'longitude': 360})
+
+    # Update variable encodings:
+    blosccodec = zarr.codecs.BloscCodec(cname="zstd", clevel=3, shuffle=zarr.codecs.BloscShuffle.shuffle)
+    for var in list(ds.data_vars) + list(ds.coords):
+        ds[var].encoding['compressors'] = [blosccodec]
+
+    update_icechunk(
+        file=ds,
+        bucket=bucket,
+        object_prefix=prefix,
+        store_credentials_json=store_credentials_json,
+        append_dim='time',
+        branch=branch,
+        commit_message=commit_message,
+        dask_config_kwargs=config_kwargs,
+        dask_cluster_kwargs=cluster_kwargs,
+        )
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/NSIDC/download_NSIDC_monthly_1979_2025_data.sh b/OceanDataStore/data/NSIDC/download_NSIDC_monthly_1979_2025_data.sh
new file mode 100755
index 00000000..c21f24ec
--- /dev/null
+++ b/OceanDataStore/data/NSIDC/download_NSIDC_monthly_1979_2025_data.sh
@@ -0,0 +1,54 @@
+#!/bin/bash
+
+# ----------------------------------------------------------------
+# download_NSIDC_monthly_1979_2025_data.sh
+#
+# Description: Download the National Snow & Ice Data Centre (NSIDC)
+# Sea Ice Index version 4 sea ice extent & concentration GeoTiff
+# files from 1979 to 2025.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-05-27
+# ----------------------------------------------------------------
+set -euo pipefail
+
+# --- Inputs --- #
+# Define hemisphere to download data for (options: "north" or "south"):
+hemisphere="north"
+
+# Define output directory for downloaded files:
+output_dir="/dssgfs01/scratch/otooth/npd_data/observations/NSIDC/"$hemisphere"/"
+
+# Single year download:
+single_year=True
+# Define year to download if single_year is True:
+year=2025
+
+# -- Defaults -- #
+# Default URL prefix:
+url_prefix="https://noaadata.apps.nsidc.org/NOAA/G02135/"$hemisphere"/monthly/geotiff"
+
+# -- Main Script -- #
+echo "==================================================="
+echo "          Downloading NSIDC Sea Ice Index"
+echo "                     v4.0"
+echo "               Oliver J. Tooth, NOC"
+echo "==================================================="
+echo "In Progress: Downloading NSIDC Sea Ice Index dataset..."
+
+mkdir -p $output_dir
+cd $output_dir
+
+# Download monthly sea ice extent & concentration files from 1979 to 2025:
+for month in 01_Jan 02_Feb 03_Mar 04_Apr 05_May 06_Jun 07_Jul 08_Aug 09_Sep 10_Oct 11_Nov 12_Dec
+do 
+    if [ "$single_year" = True ]; then
+        echo "Downloading NSIDC $year Sea Ice Conc. GeoTiffs for: $month"
+        wget -r -nd --no-check-certificate --reject "index.html*" -np -e robots=off $url_prefix/$month/ -A "*_${year}*_v4.0.tif"
+    else
+        echo "Downloading NSIDC 1979-2025 Sea Ice Conc. GeoTiffs for: $month"
+        wget -r -nd --no-check-certificate --reject "index.html*" -np -e robots=off $url_prefix/$month/
+    fi
+done
+
+echo "-> Completed: Downloaded NSIDC" $hemisphere "Sea Ice Extent & Concentration GeoTiffs"
diff --git a/OceanDataStore/data/NSIDC/process_NSIDC_SSI_Antarctic_data.py b/OceanDataStore/data/NSIDC/process_NSIDC_SSI_Antarctic_data.py
new file mode 100755
index 00000000..4acdbecc
--- /dev/null
+++ b/OceanDataStore/data/NSIDC/process_NSIDC_SSI_Antarctic_data.py
@@ -0,0 +1,130 @@
+"""
+process_NSIDC_SSI_Antarctic_data.py
+
+Description: Python script to post-process NSIDC Sea Ice Index
+data for the Antarctic 1978-2025, including sea ice concentration,
+extent and total area.
+
+Created by: Ollie Tooth (oliver.tooth@noc.ac.uk)
+Created on: 2025-02-21
+"""
+# -- Import Python packages -- #
+import logging
+from datetime import datetime
+from glob import glob
+
+import numpy as np
+import xarray as xr
+
+from OceanDataStore.cli import initialise_logging
+
+
+# -- Define Utility Functions -- #
+def get_datetimes_from_filenames(file_list):
+    # Extract filenames from paths:
+    filenames = [file.split('/')[-1] for file in file_list]
+
+    # Convert filenames date str to datetime:
+    datetimes = np.array([datetime(year=int(file[2:6]), month=int(file[6:8]), day=15) for file in filenames])
+
+    return datetimes
+
+
+def main():
+    # ========== Initialize Logging and Print Banner ========== #
+    initialise_logging()
+
+    logging.info('In Progress: Post-Processing NSIDC Sea Ice Index Antarctic Observations...')
+
+    # ========== Load NSIDC Ancillary Data ========== #
+    # Define filepath to ancillary data:
+    anc_fpath = "/dssgfs01/scratch/otooth/npd_data/observations/NSIDC/ancillary/NSIDC0771_LatLon_PS_S25km_v1.0.nc"
+    # Open NSIDC ancillary data as dataset:
+    ds_si = xr.open_dataset(anc_fpath)
+
+    # Define filepath to NSIDC ancillary file - grid cell area:
+    area_fpath = "/dssgfs01/scratch/otooth/npd_data/observations/NSIDC/ancillary/NSIDC0771_CellArea_PS_S25km_v1.0.nc"
+    # Open NSIDC grid cell area:
+    ds_area = xr.open_dataset(area_fpath)
+    logging.info("-> Completed: Loaded NSIDC ancillary data and grid cell area.")
+
+    # ========== Load NSIDC Monthly Data ========== #
+    # Define directory path:
+    dir_path = "/dssgfs01/scratch/otooth/npd_data/observations/NSIDC/antarctic/"
+
+    # Get the list of files in the directory:
+    file_paths = glob(f"{dir_path}*.tif")
+    file_paths.sort()
+
+    # Retrieve sea ice mask & concentration files:
+    version_str = "v4.0"  # Options: "v3.0" or "v4.0"
+    mask_files = [f for f in file_paths if f"extent_{version_str}.tif" in f]
+    conc_files = [f for f in file_paths if f"concentration_{version_str}.tif" in f]
+
+    # ========== Post-Process Sea Ice Mask Data ========== #
+    # Define the time dimension:
+    time_simask = xr.DataArray(data=get_datetimes_from_filenames(file_list=mask_files), dims='time', name='time')
+    # Load and concatenate all sea ice mask GeoTIFFs:
+    simask = xr.concat([xr.open_dataset(i) for i in mask_files], dim=time_simask)
+    logging.info("-> Completed: Loaded NSIDC sea ice mask and concentration GeoTIFF files.")
+
+    # Sea Ice Mask is defined by [1: sea ice, 0: ocean]:
+    # Values greater than 1 (missing or land) are set to NaN:
+    ds_si['simask'] = xr.where(simask['band_data'] > 1, np.nan, simask['band_data']).squeeze(drop=True)
+    ds_si["simask"].attrs = {'units': '1', "long_name": "Sea Ice Mask", "standard_name": "sea_ice_mask", "comment": "1 = sea ice, 0 = ocean"}
+
+    # ========== Post-Process Sea Ice Concentration Data ========== #
+    # Define the time dimension:
+    time_siconc = xr.DataArray(data=get_datetimes_from_filenames(file_list=conc_files), dims='time', name='time')
+    # Load and concatenate all sea ice extent GeoTIFFs:
+    siconc = xr.concat([xr.open_dataset(i) for i in conc_files], dim=time_siconc)
+    logging.info("-> Completed: Loaded NSIDC sea ice concentration GeoTIFF files.")
+
+    # Sea Ice Area Fraction:
+    # Note concentration percentage is scaled by 10 -> requires division by 1000.
+    # Values greater than 1 (missing or land) are set to NaN:
+    ds_si['siconc'] = xr.where(siconc['band_data'] > 1000, np.nan, siconc['band_data']).squeeze(drop=True) / 1000
+    ds_si['siconc'].attrs = {'units': '1', 'long_name': 'Sea Ice Area Fraction', 'standard_name': 'sea_ice_area_fraction', "comment": "0 = ocean, 0.01-0.15 = statistically insignificant, > 0.15 = sea ice"}
+
+    # ========== Calculate sea ice area (m2) ========== #
+    ds_si['cell_area'] = ds_area['cell_area']
+    ds_si['cell_area'].attrs = {'units': 'm2', 'long_name': 'Grid-Cell Area for Sea Ice Variables', "standard_name": "cell_area"}
+
+    ds_si['siextent'] = (ds_si['cell_area']*ds_si['simask']).sum(dim=['x', 'y'])
+    ds_si['siextent'].attrs = {'units': 'm2', 'long_name': 'Total Area Where Sea Ice Area Fraction Exceeds 15%', 'standard_name': 'sea_ice_extent'}
+
+    # ========== Update Coordinates ========== #
+    ds_si.coords['lon'] = ds_si['longitude']
+    ds_si.coords['lat'] = ds_si['latitude']
+    # Drop auxiliary variables:
+    ds_si = ds_si.drop_vars(["spatial_ref", "crs", "longitude", "latitude"])
+    # Rename coordinates:
+    ds_si = ds_si.rename({'lon': 'longitude', 'lat': 'latitude'})
+
+    # ========== Update attributes to ensure CF-compliance: ========== #
+    ds_si['x'].attrs = {'standard_name': 'projection_x_coordinate', 'long_name': 'x coordinate of projection', 'units': 'meters'}
+    ds_si['y'].attrs = {'standard_name': 'projection_y_coordinate', 'long_name': 'y coordinate of projection', 'units': 'meters'}
+    ds_si['longitude'].attrs = {'standard_name': 'longitude', 'long_name': 'Longitude', 'units': 'degrees_east'}
+    ds_si['latitude'].attrs = {'standard_name': 'latitude', 'long_name': 'Latitude', 'units': 'degrees_north'}
+    ds_si['simask'].attrs.pop("valid_range", None)
+
+    # ========== Save NSIDC Sea Ice Index Dataset ========== #
+    # Update variable encodings:
+    for var in ds_si.variables:
+        if ds_si[var].dtype == 'float64':
+            ds_si[var].encoding['missing_value'] = None
+            ds_si[var].encoding['_FillValue'] = None
+
+    # Define output filepath:
+    out_fpath = "/dssgfs01/scratch/otooth/npd_data/observations/NSIDC/NSIDC_Sea_Ice_Index_v4_Antarctic_combined_1978_2025.nc"
+    # Save dataset to netCDF file:
+    logging.info(f'In Progress: Saving NSIDC Sea Ice Index Antarctic Observations to netCDF file: {out_fpath}...')
+    ds_si.to_netcdf(out_fpath, unlimited_dims='time')
+    # Close files associated with datasets:
+    ds_si.close()
+    ds_area.close()
+
+    logging.info(f'Completed: Saved NSIDC Sea Ice Index Antarctic Observations to netCDF file: {out_fpath}.')
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/OceanDataStore/data/NSIDC/process_NSIDC_SSI_Arctic_data.py b/OceanDataStore/data/NSIDC/process_NSIDC_SSI_Arctic_data.py
new file mode 100755
index 00000000..33aad7a5
--- /dev/null
+++ b/OceanDataStore/data/NSIDC/process_NSIDC_SSI_Arctic_data.py
@@ -0,0 +1,129 @@
+"""
+process_NSIDC_SSI_Arctic_data.py
+
+Description: Python script to post-process NSIDC Sea Ice Index
+data for the Arctic 1978-2025, including sea ice concentration,
+extent and total area.
+
+Created by: Ollie Tooth (oliver.tooth@noc.ac.uk)
+Created on: 2025-02-21
+"""
+# -- Import Python packages -- #
+import logging
+from datetime import datetime
+from glob import glob
+
+import numpy as np
+import xarray as xr
+
+from OceanDataStore.cli import initialise_logging
+
+
+# -- Define Utility Functions -- #
+def get_datetimes_from_filenames(file_list):
+    # Extract filenames from paths:
+    filenames = [file.split('/')[-1] for file in file_list]
+
+    # Convert filenames date str to datetime:
+    datetimes = np.array([datetime(year=int(file[2:6]), month=int(file[6:8]), day=15) for file in filenames])
+
+    return datetimes
+
+def main():
+    # ========== Initialize Logging and Print Banner ========== #
+    initialise_logging()
+
+    logging.info('In Progress: Post-Processing NSIDC Sea Ice Index Arctic Observations...')
+
+    # ========== Load NSIDC Ancillary Data ========== #
+    # Define filepath to ancillary data:
+    anc_fpath = "/dssgfs01/scratch/otooth/npd_data/observations/NSIDC/ancillary/NSIDC0771_LatLon_PS_N25km_v1.0.nc"
+    # Open NSIDC ancillary data as dataset:
+    ds_si = xr.open_dataset(anc_fpath)
+
+    # Define filepath to NSIDC ancillary file - grid cell area:
+    area_fpath = "/dssgfs01/scratch/otooth/npd_data/observations/NSIDC/ancillary/NSIDC0771_CellArea_PS_N25km_v1.0.nc"
+    # Open NSIDC grid cell area:
+    ds_area = xr.open_dataset(area_fpath)
+    logging.info("-> Completed: Loaded NSIDC ancillary data and grid cell area.")
+
+    # ========== Load NSIDC Monthly Data ========== #
+    # Define directory path:
+    dir_path = "/dssgfs01/scratch/otooth/npd_data/observations/NSIDC/arctic/"
+
+    # Get the list of files in the directory:
+    file_paths = glob(f"{dir_path}*.tif")
+    file_paths.sort()
+
+    # Retrieve sea ice mask & concentration files:
+    version_str = "v4.0"  # Options: "v3.0" or "v4.0"
+    mask_files = [f for f in file_paths if f"extent_{version_str}.tif" in f]
+    conc_files = [f for f in file_paths if f"concentration_{version_str}.tif" in f]
+
+    # ========== Post-Process Sea Ice Mask Data ========== #
+    # Define the time dimension:
+    time_simask = xr.DataArray(data=get_datetimes_from_filenames(file_list=mask_files), dims='time', name='time')
+    # Load and concatenate all sea ice mask GeoTIFFs:
+    simask = xr.concat([xr.open_dataset(i) for i in mask_files], dim=time_simask)
+    logging.info("-> Completed: Loaded NSIDC sea ice mask and concentration GeoTIFF files.")
+
+    # Sea Ice Mask is defined by [1: sea ice, 0: ocean]:
+    # Values greater than 1 (missing or land) are set to NaN:
+    ds_si['simask'] = xr.where(simask['band_data'] > 1, np.nan, simask['band_data']).squeeze(drop=True)
+    ds_si["simask"].attrs = {'units': '1', "long_name": "Sea Ice Mask", "standard_name": "sea_ice_mask", "comment": "1 = sea ice, 0 = ocean"}
+
+    # ========== Post-Process Sea Ice Concentration Data ========== #
+    # Define the time dimension:
+    time_siconc = xr.DataArray(data=get_datetimes_from_filenames(file_list=conc_files), dims='time', name='time')
+    # Load and concatenate all sea ice extent GeoTIFFs:
+    siconc = xr.concat([xr.open_dataset(i) for i in conc_files], dim=time_siconc)
+    logging.info("-> Completed: Loaded NSIDC sea ice concentration GeoTIFF files.")
+
+    # Sea Ice Area Fraction:
+    # Note concentration percentage is scaled by 10 -> requires division by 1000.
+    # Values greater than 1 (missing or land) are set to NaN:
+    ds_si['siconc'] = xr.where(siconc['band_data'] > 1000, np.nan, siconc['band_data']).squeeze(drop=True) / 1000
+    ds_si['siconc'].attrs = {'units': '1', 'long_name': 'Sea Ice Area Fraction', 'standard_name': 'sea_ice_area_fraction', "comment": "0 = ocean, 0.01-0.15 = statistically insignificant, > 0.15 = sea ice"}
+
+    # ========== Calculate sea ice area (m2) ========== #
+    ds_si['cell_area'] = ds_area['cell_area']
+    ds_si['cell_area'].attrs = {'units': 'm2', 'long_name': 'Grid-Cell Area for Sea Ice Variables', "standard_name": "cell_area"}
+
+    ds_si['siextent'] = (ds_si['cell_area']*ds_si['simask']).sum(dim=['x', 'y'])
+    ds_si['siextent'].attrs = {'units': 'm2', 'long_name': 'Total Area Where Sea Ice Area Fraction Exceeds 15%', 'standard_name': 'sea_ice_extent'}
+
+    # ========== Update Coordinates ========== #
+    ds_si.coords['lon'] = ds_si['longitude']
+    ds_si.coords['lat'] = ds_si['latitude']
+    # Drop auxiliary variables:
+    ds_si = ds_si.drop_vars(["spatial_ref", "crs", "longitude", "latitude"])
+    # Rename coordinates:
+    ds_si = ds_si.rename({'lon': 'longitude', 'lat': 'latitude'})
+
+    # ========== Update attributes to ensure CF-compliance: ========== #
+    ds_si['x'].attrs = {'standard_name': 'projection_x_coordinate', 'long_name': 'x coordinate of projection', 'units': 'meters'}
+    ds_si['y'].attrs = {'standard_name': 'projection_y_coordinate', 'long_name': 'y coordinate of projection', 'units': 'meters'}
+    ds_si['longitude'].attrs = {'standard_name': 'longitude', 'long_name': 'Longitude', 'units': 'degrees_east'}
+    ds_si['latitude'].attrs = {'standard_name': 'latitude', 'long_name': 'Latitude', 'units': 'degrees_north'}
+    ds_si['simask'].attrs.pop("valid_range", None)
+
+    # ========== Save NSIDC Sea Ice Index Dataset ========== #
+    # Update variable encodings:
+    for var in ds_si.variables:
+        if ds_si[var].dtype == 'float64':
+            ds_si[var].encoding['missing_value'] = None
+            ds_si[var].encoding['_FillValue'] = None
+
+    # Define output filepath:
+    out_fpath = "/dssgfs01/scratch/otooth/npd_data/observations/NSIDC/NSIDC_Sea_Ice_Index_v4_Arctic_combined_1978_2025.nc"
+    # Save dataset to netCDF file:
+    logging.info(f"-> Saving NSIDC Sea Ice Index Arctic Observations to netCDF file: {out_fpath}...")
+    ds_si.to_netcdf(out_fpath, unlimited_dims='time')
+    # Close files associated with datasets:
+    ds_si.close()
+    ds_area.close()
+
+    logging.info(f'Completed: Saved NSIDC Sea Ice Index Arctic Observations to netCDF file: {out_fpath}.')
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/OceanDataStore/data/NSIDC/run_send_NSIDC_v4.0_to_OS.slurm b/OceanDataStore/data/NSIDC/run_send_NSIDC_v4.0_to_OS.slurm
new file mode 100755
index 00000000..35fc7034
--- /dev/null
+++ b/OceanDataStore/data/NSIDC/run_send_NSIDC_v4.0_to_OS.slurm
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH --job-name=xfer_NSIDC_sea_ice_index_v4.0
+#SBATCH --partition=test
+#SBATCH --time=00:20:00
+#SBATCH --ntasks-per-core=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --ntasks-per-socket=32
+#SBATCH --nodes=1
+
+# ================================================================
+# run_send_NSIDC_v4.0_to_OS.slurm
+#
+# Description: SLURM script to send the NSIDC sea ice observations
+# data to the JASMIN Object Store.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-06-05
+#
+# ================================================================
+set -euo pipefail
+
+# -- Python Environment -- #
+# Activate miniconda environment:
+source .../miniforge3/bin/activate
+conda activate env_ods
+
+# -- Send NSIDC Sea Ice Index v4.0 to JASMIN OS -- #
+echo "In Progress: Sending NSIDC Sea Ice Index v4.0 to Icechunk..."
+
+python3 send_NSIDC_SII_v4.0_to_os.py
+
+echo "Completed: Sent NSIDC Sea Ice Index v4.0 to Icechunk."
diff --git a/OceanDataStore/data/NSIDC/send_NSIDC_SII_v4.0_to_os.py b/OceanDataStore/data/NSIDC/send_NSIDC_SII_v4.0_to_os.py
new file mode 100755
index 00000000..7b14ed03
--- /dev/null
+++ b/OceanDataStore/data/NSIDC/send_NSIDC_SII_v4.0_to_os.py
@@ -0,0 +1,140 @@
+# =========================================================
+# send_NSIDC_SII_v4.0_to_os.py
+#
+# Script to write NSIDC Sea Ice Index version 4.0 to
+# Icechunk repositories in JASMIN cloud object storage.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import logging
+
+import xarray as xr
+import zarr
+
+from OceanDataStore.cli import send_to_icechunk, initialise_logging
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    # ========== Initialise OceanDataStore Logging ========== #
+    initialise_logging()
+
+    # ========== Prepare Data ========== #
+    # Open NSIDC Sea Ice Index v4.0 datasets:
+    ds_si_arctic = xr.open_dataset("/dssgfs01/scratch/otooth/npd_data/observations/NSIDC/NSIDC_Sea_Ice_Index_v4_Arctic_combined_1978_2025.nc")
+
+    ds_si_antarctic = xr.open_dataset("/dssgfs01/scratch/otooth/npd_data/observations/NSIDC/NSIDC_Sea_Ice_Index_v4_Antarctic_combined_1978_2025.nc")
+
+    # Optimise chunk sizes for spatial analysis:
+    ds_si_arctic = ds_si_arctic.chunk({'time': 12, 'y': 448, 'x': 304})
+    ds_si_antarctic = ds_si_antarctic.chunk({'time': 12, 'y': 332, 'x': 316})
+
+    # Update variable encodings:
+    blosccodec = zarr.codecs.BloscCodec(cname="zstd", clevel=3, shuffle=zarr.codecs.BloscShuffle.shuffle)
+    for var in list(ds_si_arctic.data_vars) + list(ds_si_arctic.coords):
+        ds_si_arctic[var].encoding['compressors'] = [blosccodec]
+    for var in list(ds_si_antarctic.data_vars) + list(ds_si_antarctic.coords):
+        ds_si_antarctic[var].encoding['compressors'] = [blosccodec]
+
+    # Update global CF-metadata attributes:
+    ds_si_arctic.attrs.clear()
+    ds_si_arctic = ds_si_arctic.assign_attrs({
+        "Conventions": "CF-1.6",
+        "title": "NSIDC Sea Ice Index, Version 4 - Arctic",
+        "description": "NSIDC Sea Ice Index version 4.0 - Arctic sea ice area fraction, sea ice extent and total sea ice area timeseries.",
+        "source": "Satellite observations: Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data (GSFC). AMSR2 Daily Polar Gridded Sea Ice Concentrations (AMSR2).",
+        "dataset_type": "observation",
+        "product_type": "timeseries",
+        "product_version" : "1.0",
+        "institution": "National Snow and Ice Data Center; Cooperative Institute for Research in Environmental Sciences; University of Colorado at Boulder; Boulder, CO",
+        "citation": "Fetterer, F., Knowles, K., Meier, W. N., Savoie, M., Windnagel, A. K. & Stafford, T. (2025). Sea Ice Index. (G02135, Version 4). [Data Set]. Boulder, Colorado USA. National Snow and Ice Data Center. https://doi.org/10.7265/a98x-0f50. Date Accessed 05-29-2026.",
+        "references": "Windnagel, A., Stafford, T., Fetterer, F., Meier, W. (2025). Sea Ice Index Version 4 Analysis. NSIDC Special Report 28. Boulder CO, USA: National Snow and Ice Data Center.",
+        "acknowledgement": "These data are produced and supported by the NASA National Snow and Ice Data Center Distributed Active Archive Center.",
+        "license": "NSIDC Sea Ice Index, Version 4 data were obtained from https://nsidc.org/data/g02135/versions/4 and are provided under a U.S. Government Works License https://www.usa.gov/government-works",
+        "doi": "10.7265/a98x-0f50",
+        "platform": "gn",
+        "horizontal_grid_type": "curvilinear",
+        "horizontal_grid_resolution": "25 km",
+        "aggregation": "mean",
+        "aggregation_frequency": "monthly",
+        "status": "ongoing",
+        "update_frequency": "quarterly",
+        "bbox": "[-180.0, 180.0, 30.98, 90.0]",
+    })
+
+    ds_si_antarctic.attrs.clear()
+    ds_si_antarctic = ds_si_antarctic.assign_attrs({
+        "Conventions": "CF-1.6",
+        "title": "NSIDC Sea Ice Index, Version 4 - Antarctic",
+        "description": "NSIDC Sea Ice Index, Version 4 - Antarctic sea ice area fraction, sea ice extent and total sea ice area timeseries.",
+        "source": "Satellite observations: Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data (GSFC). AMSR2 Daily Polar Gridded Sea Ice Concentrations (AMSR2).",
+        "dataset_type": "observation",
+        "product_type": "timeseries",
+        "product_version" : "1.0",
+        "institution": "National Snow and Ice Data Center; Cooperative Institute for Research in Environmental Sciences; University of Colorado at Boulder; Boulder, CO",
+        "citation": "Fetterer, F., Knowles, K., Meier, W. N., Savoie, M., Windnagel, A. K. & Stafford, T. (2025). Sea Ice Index. (G02135, Version 4). [Data Set]. Boulder, Colorado USA. National Snow and Ice Data Center. https://doi.org/10.7265/a98x-0f50. Date Accessed 05-29-2026.",
+        "references": "Windnagel, A., Stafford, T., Fetterer, F., Meier, W. (2025). Sea Ice Index Version 4 Analysis. NSIDC Special Report 28. Boulder CO, USA: National Snow and Ice Data Center.",
+        "acknowledgement": "These data are produced and supported by the NASA National Snow and Ice Data Center Distributed Active Archive Center.",
+        "license": "NSIDC Sea Ice Index, Version 4 data were obtained from https://nsidc.org/data/g02135/versions/4 and are provided under a U.S. Government Works License https://www.usa.gov/government-works",
+        "doi": "10.7265/a98x-0f50",
+        "platform": "gn",
+        "horizontal_grid_type": "curvilinear",
+        "horizontal_grid_resolution": "25 km",
+        "aggregation": "mean",
+        "aggregation_frequency": "monthly",
+        "status": "ongoing",
+        "update_frequency": "quarterly",
+        "bbox": "[-180.0, 180.0, -90.0, -39.23089]",
+    })
+
+    # ========== Send to Icechunk Repository ========== #
+    bucket = "nsidc"
+    exists = False
+    store_credentials_json = ".../credentials/jasmin_os_credentials.json"
+    branch = "main"
+    variable_commits = True
+    config_kwargs = {
+            "temporary_directory":".../OceanDataStore/OceanDataStore/data/NSIDC/",
+            "local_directory":".../OceanDataStore/OceanDataStore/data/NSIDC/"
+        }
+    cluster_kwargs = {
+            "n_workers" : 10,
+            "threads_per_worker" : 1,
+            "memory_limit":"3GB"
+        }
+
+    # -- Sea Ice Index v4.0 - Arctic -- #
+    send_to_icechunk(
+        file=ds_si_arctic,
+        bucket=bucket,
+        object_prefix="nsidc_sea_ice_index_v4_arctic_monthly",
+        store_credentials_json=store_credentials_json,
+        exists=exists,
+        append_dim='time',
+        attrs=ds_si_arctic.attrs,
+        branch=branch,
+        commit_message="Added NSIDC Sea Ice Index version 4 - Arctic (1978-01-2025-12).",
+        variable_commits=variable_commits,
+        dask_config_kwargs=config_kwargs,
+        dask_cluster_kwargs=cluster_kwargs,
+        )
+    
+    # -- Sea Ice Index v4.0 - Antarctic -- #
+    send_to_icechunk(
+        file=ds_si_antarctic,
+        bucket=bucket,
+        object_prefix="nsidc_sea_ice_index_v4_antarctic_monthly",
+        store_credentials_json=store_credentials_json,
+        exists=exists,
+        append_dim='time',
+        attrs=ds_si_antarctic.attrs,
+        branch=branch,
+        commit_message="Added NSIDC Sea Ice Index version 4 - Antarctic (1978-01-2025-12).",
+        variable_commits=variable_commits,
+        dask_config_kwargs=config_kwargs,
+        dask_cluster_kwargs=cluster_kwargs,
+        )
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/OISST/run_send_OISSTv2_daily_climatology_to_os.slurm b/OceanDataStore/data/OISST/run_send_OISSTv2_daily_climatology_to_os.slurm
new file mode 100755
index 00000000..aa9181cc
--- /dev/null
+++ b/OceanDataStore/data/OISST/run_send_OISSTv2_daily_climatology_to_os.slurm
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH --job-name=oisstv2_daily_climatology
+#SBATCH --partition=test
+#SBATCH --time=00:20:00
+#SBATCH --ntasks-per-core=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --ntasks-per-socket=32
+#SBATCH --nodes=1
+
+# ==============================================================
+# run_send_OISSTv2_daily_climatology_to_os.slurm
+#
+# Description: SLURM script to send the OISSTv2.1 daily 
+# climatology datasets to Icechunk repository.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-06-09
+#
+# ==============================================================
+set -euo pipefail
+
+# -- Python Environment -- #
+# Activate miniconda environment:
+source .../miniforge3/bin/activate
+conda activate env_ods
+
+# -- Send OISSTv2.1 daily climatology datasets to JASMIN OS -- #
+echo "In Progress: Sending OISSTv2.1 daily climatology to Icechunk..."
+
+python3 send_OISSTv2_daily_climatology_to_os.py
+
+echo "Completed: Sent OISSTv2.1 daily climatology to Icechunk."
diff --git a/OceanDataStore/data/OISST/run_send_OISSTv2_monthly_climatology_to_os.slurm b/OceanDataStore/data/OISST/run_send_OISSTv2_monthly_climatology_to_os.slurm
new file mode 100755
index 00000000..22ba0250
--- /dev/null
+++ b/OceanDataStore/data/OISST/run_send_OISSTv2_monthly_climatology_to_os.slurm
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH --job-name=oisstv2_monthly_climatology
+#SBATCH --partition=test
+#SBATCH --time=00:20:00
+#SBATCH --ntasks-per-core=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --ntasks-per-socket=32
+#SBATCH --nodes=1
+
+# ==============================================================
+# run_send_OISSTv2_monthly_climatology_to_os.slurm
+#
+# Description: SLURM script to send the OISSTv2.1 monthly 
+# climatology datasets to Icechunk repository.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-06-09
+#
+# ==============================================================
+set -euo pipefail
+
+# -- Python Environment -- #
+# Activate miniconda environment:
+source .../miniforge3/bin/activate
+conda activate env_ods
+
+# -- Send OISSTv2.1 monthly climatology datasets to JASMIN OS -- #
+echo "In Progress: Sending OISSTv2.1 monthly climatology to Icechunk..."
+
+python3 send_OISSTv2_monthly_climatology_to_os.py
+
+echo "Completed: Sent OISSTv2.1 monthly climatology to Icechunk."
diff --git a/OceanDataStore/data/OISST/run_send_OISSTv2_monthly_to_os.slurm b/OceanDataStore/data/OISST/run_send_OISSTv2_monthly_to_os.slurm
new file mode 100755
index 00000000..253bf6a7
--- /dev/null
+++ b/OceanDataStore/data/OISST/run_send_OISSTv2_monthly_to_os.slurm
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH --job-name=oisstv2_monthly
+#SBATCH --partition=test
+#SBATCH --time=00:20:00
+#SBATCH --ntasks-per-core=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --ntasks-per-socket=32
+#SBATCH --nodes=1
+
+# ==============================================================
+# run_send_OISSTv2_monthly_to_os.slurm
+#
+# Description: SLURM script to send the OISSTv2.1 monthly 
+# time-series dataset to Icechunk repository.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-06-09
+#
+# ==============================================================
+set -euo pipefail
+
+# -- Python Environment -- #
+# Activate miniconda environment:
+source .../miniforge3/bin/activate
+conda activate env_ods
+
+# -- Send OISSTv2.1 monthly time-series datasets to JASMIN OS -- #
+echo "In Progress: Sending OISSTv2.1 monthly time-series to Icechunk..."
+
+python3 send_OISSTv2_monthly_to_os.py
+
+echo "Completed: Sent OISSTv2.1 monthly time-series to Icechunk."
diff --git a/OceanDataStore/data/OISST/send_OISSTv2_daily_climatology_to_os.py b/OceanDataStore/data/OISST/send_OISSTv2_daily_climatology_to_os.py
new file mode 100755
index 00000000..671a1da6
--- /dev/null
+++ b/OceanDataStore/data/OISST/send_OISSTv2_daily_climatology_to_os.py
@@ -0,0 +1,151 @@
+# =========================================================
+# send_OISSTv2_daily_climatology_to_os.py
+#
+# Script to write OISST v2.1 long-term daily climatologies
+# to Icechunk repositories in JASMIN cloud object storage.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import logging
+
+import numpy as np
+import xarray as xr
+import zarr
+
+from OceanDataStore.cli import initialise_logging, send_to_icechunk
+from OceanDataStore.data.utils import (
+    compute_cell_area,
+    compute_dx,
+    compute_dy,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    # ========== Initialise OceanDataStore Logging ========== #
+    initialise_logging()
+
+    # ========== Send to Icechunk Repository ========== #
+    bucket = "oisst"
+    exists = False
+    store_credentials_json = ".../credentials/jasmin_os_credentials.json"
+    branch = "main"
+    variable_commits = True
+
+    # Define climatology period:
+    start_yr = 1991
+    end_yr = 2020
+    
+    logging.info(f"In Progress: Sending OISSTv2.1 daily climatology for {start_yr}-{end_yr} to Icechunk...")
+    # Open OISSTv2 dataset:
+    filepaths = [f"/dssgfs01/scratch/otooth/npd_data/observations/OISST/icec.day.mean.ltm.{start_yr}-{end_yr}.nc",
+                 f"/dssgfs01/scratch/otooth/npd_data/observations/OISST/sst.day.mean.ltm.{start_yr}-{end_yr}.nc"
+                ]
+    ds = xr.merge([xr.open_dataset(filepath, decode_times=False).drop_vars("valid_yr_count") for filepath in filepaths], compat="no_conflicts")
+    # Open OISSTv2 land-sea mask dataset:
+    ds_mask = xr.open_dataset("http://psl.noaa.gov/thredds/dodsC/Datasets/noaa.oisst.v2.highres/lsmask.oisst.nc", decode_times=False)
+    ds_mask = ds_mask.squeeze(drop=True).rename({"lon": "longitude", "lat": "latitude", "lsmask": "mask"})
+    ds_mask = ds_mask.assign_coords(
+        longitude=((ds_mask["longitude"] + 180) % 360) - 180
+    )
+
+    # Standardise coordinate dimension names:
+    ds = ds.rename({"lon": "longitude", "lat": "latitude", "time": "day"})
+
+    # Update longitude coordinates to be in the range [-180, 180]:
+    ds = ds.assign_coords(
+        longitude=((ds["longitude"] + 180) % 360) - 180
+    )
+    ds = ds.sortby("longitude")
+
+    # Add day of year coordinate (1-365):
+    ds = ds.assign_coords(
+        day=np.arange(1, 366)
+    )
+
+    # Rename variables to standard names:
+    ds = ds.rename({"sst": "tos",
+                    "icec": "siconc",
+                    "climatology_bounds": "time_bnds",
+                    })
+
+    # Add standard names and units:
+    ds["tos"].attrs["standard_name"] = "sea_surface_temperature"
+    ds["siconc"].attrs["standard_name"] = "sea_ice_area_fraction"
+    ds["siconc"].attrs["units"] = "1"
+
+    # Add OISSTv2 land mask:
+    ds["mask"] = ds_mask["mask"]
+    ds["mask"].attrs.clear()
+    ds["mask"] = ds["mask"].assign_attrs({'long_name': "Land-Sea Binary Mask",
+                                          "standard_name": "sea_binary_mask",
+                                          "comment": "1 = sea, 0 = land"
+                                          })
+
+    # Add horizontal grid cell area:
+    ds['dx'] = compute_dx(ds)
+    ds['dy'] = compute_dy(ds)
+    ds['cell_area'] = compute_cell_area(ds)
+
+    # Update time bounds to reflect climatological period:
+    ds['time_bnds'] = ds['time_bnds'].astype('datetime64[ns]')
+    ds['time_bnds'].data[:, 0] = (np.datetime64(f'{start_yr}-01', 'M') + (np.timedelta64(1, 'D') * np.arange(ds['day'].size))).astype('datetime64[ns]')
+    ds['time_bnds'].data[:, 1] = (np.datetime64(f'{end_yr}-01', 'M') + (np.timedelta64(1, 'D') * np.arange(ds['day'].size))).astype('datetime64[ns]')
+    ds.time_bnds.attrs.clear()
+
+    # Update global attributes:
+    ds.attrs.clear()
+    ds = ds.assign_attrs({
+        "Conventions": "CF-1.5",
+        "title": f"NOAA OISSTv2.1 Daily Climatology ({start_yr}-{end_yr})",
+        "description": f"NOAA 1/4° Daily Optimum Interpolation Sea Surface Temperature (OISST) version 2.1 daily sea surface temperature and sea ice fraction climatology ({start_yr}-{end_yr}).",
+        "source": "Numerical models: Optimal Interpolation. In-situ observations: ICOADS-D R3.0.2, Argo GDAC. Satellite observations: Advanced Very High Resolution Radiometer (AVHRR).",
+        "dataset_type": "observation",
+        "product_type": "climatology",
+        "product_version": "2.1",
+        "institution": "NOAA National Centers for Environmental Information (NCEI)",
+        "citation": "Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H.-M. Zhang, 2021: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1, Journal of Climate, 34, 2923-2939. doi: 10.1175/JCLI-D-20-0166.1",
+        "references": "Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H.-M. Zhang, 2020: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1, Journal of Climate, 34, 2923-2939. doi: 10.1175/JCLI-D-20-0166.1. Banzon, V., Smith, T. M., Chin, T. M., Liu, C., and Hankins, W., 2016: A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies. Earth Syst. Sci. Data, 8, 165-176, doi:10.5194/essd-8-165-2016. Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. Journal of Climate, 20, 5473-5496, doi:10.1175/JCLI-D-14-00293.1",
+        "acknowledgement": "NOAA OI SST V2 High Resolution Dataset data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at https://psl.noaa.gov.",
+        "license": "OISST v2.1 data were obtained from https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html and are provided under a Creative Commons CC0 1.0 Universal License https://creativecommons.org/publicdomain/zero/1.0/",
+        "doi": "10.1175/JCLI-D-20-0166.1",
+        "platform": "gr",
+        "horizontal_grid_type": "regular rectilinear",
+        "horizontal_grid_resolution": "0.25 degree",
+        "aggregation": "mean",
+        "aggregation_frequency": "daily",
+        "status": "completed",
+        "update_frequency": "None",
+        "bbox": "[-180.0, 180.0, -90.0, 90.0]",
+    })
+
+    # Optimise chunk sizes for spatial analysis:
+    ds = ds.chunk({'day': 5, 'latitude': 720, 'longitude': 1440})
+
+    # Update variable encodings:
+    blosccodec = zarr.codecs.BloscCodec(cname="zstd", clevel=3, shuffle=zarr.codecs.BloscShuffle.shuffle)
+    for var in list(ds.data_vars) + list(ds.coords):
+        ds[var].encoding['compressors'] = [blosccodec]
+
+    # Define prefix and commit message based on climatology period:
+    prefix = f"oisst_v2.1_{start_yr}_{end_yr}_daily_climatology"
+    commit_message = f"Added OISSTv2.1 Sea Surface Temperature Climatology ({start_yr}-{end_yr})."
+
+    send_to_icechunk(
+        file=ds,
+        bucket=bucket,
+        object_prefix=prefix,
+        store_credentials_json=store_credentials_json,
+        exists=exists,
+        append_dim='day',
+        branch=branch,
+        commit_message=commit_message,
+        variable_commits=variable_commits,
+        dask_config_kwargs=None,
+        dask_cluster_kwargs=None,
+        )
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/OISST/send_OISSTv2_monthly_climatology_to_os.py b/OceanDataStore/data/OISST/send_OISSTv2_monthly_climatology_to_os.py
new file mode 100755
index 00000000..08923f94
--- /dev/null
+++ b/OceanDataStore/data/OISST/send_OISSTv2_monthly_climatology_to_os.py
@@ -0,0 +1,150 @@
+# =========================================================
+# send_OISSTv2_monthly_climatology_to_os.py
+#
+# Script to write OISST v2.1 long-term monthly climatologies
+# to Icechunk repositories in JASMIN cloud object storage.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import logging
+
+import numpy as np
+import xarray as xr
+import zarr
+
+from OceanDataStore.cli import initialise_logging, send_to_icechunk
+from OceanDataStore.data.utils import (
+    compute_cell_area,
+    compute_dx,
+    compute_dy,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    # ========== Initialise OceanDataStore Logging ========== #
+    initialise_logging()
+
+    # ========== Send to Icechunk Repository ========== #
+    bucket = "oisst"
+    exists = False
+    store_credentials_json = ".../credentials/jasmin_os_credentials.json"
+    branch = "main"
+    variable_commits = True
+
+    # Define climatology period:
+    start_yr = 1991
+    end_yr = 2020
+    
+    logging.info(f"In Progress: Sending OISSTv2.1 monthly climatology for {start_yr}-{end_yr} to Icechunk...")
+    # Open OISSTv2 dataset:
+    filepaths = [f"/dssgfs01/scratch/otooth/npd_data/observations/OISST/icec.mon.ltm.{start_yr}-{end_yr}.nc",
+                 f"/dssgfs01/scratch/otooth/npd_data/observations/OISST/sst.mon.ltm.{start_yr}-{end_yr}.nc"
+                ]
+    ds = xr.merge([xr.open_dataset(filepath, decode_times=False).drop_vars("valid_yr_count") for filepath in filepaths], compat="no_conflicts")
+    # Open OISSTv2 land-sea mask dataset:
+    ds_mask = xr.open_dataset("http://psl.noaa.gov/thredds/dodsC/Datasets/noaa.oisst.v2.highres/lsmask.oisst.nc", decode_times=False)
+    ds_mask = ds_mask.squeeze(drop=True).rename({"lon": "longitude", "lat": "latitude", "lsmask": "mask"})
+    ds_mask = ds_mask.assign_coords(
+        longitude=((ds_mask["longitude"] + 180) % 360) - 180
+    )
+
+    # Standardise coordinate dimension names:
+    ds = ds.rename({"lon": "longitude", "lat": "latitude", "time": "month"})
+
+    # Update longitude coordinates to be in the range [-180, 180]:
+    ds = ds.assign_coords(
+        longitude=((ds["longitude"] + 180) % 360) - 180
+    )
+    ds = ds.sortby("longitude")
+
+    # Add month of year coordinate (1-12):
+    ds = ds.assign_coords(
+        month=np.arange(1, 13)
+    )
+
+    # Rename variables to standard names:
+    ds = ds.rename({"sst": "tos",
+                    "icec": "siconc",
+                    "climatology_bounds": "time_bnds",
+                    })
+
+    # Add standard names and units:
+    ds["tos"].attrs["standard_name"] = "sea_surface_temperature"
+    ds["siconc"].attrs["standard_name"] = "sea_ice_area_fraction"
+    ds["siconc"].attrs["units"] = "1"
+
+    # Add OISSTv2 land mask:
+    ds["mask"] = ds_mask["mask"]
+    ds["mask"].attrs.clear()
+    ds["mask"] = ds["mask"].assign_attrs({'long_name': "Land-Sea Binary Mask",
+                                          "standard_name": "sea_binary_mask",
+                                          "comment": "1 = sea, 0 = land"
+                                          })
+
+    # Add horizontal grid cell area:
+    ds['dx'] = compute_dx(ds)
+    ds['dy'] = compute_dy(ds)
+    ds['cell_area'] = compute_cell_area(ds)
+
+    # Update time bounds to reflect climatological period:
+    ds['time_bnds'] = ds['time_bnds'].astype('datetime64[ns]')
+    ds['time_bnds'].data[:, 0] = (np.datetime64(f'{start_yr}-01', 'M') + (np.timedelta64(1, 'M') * np.arange(ds['month'].size))).astype('datetime64[ns]')
+    ds['time_bnds'].data[:, 1] = (np.datetime64(f'{end_yr}-01', 'M') + (np.timedelta64(1, 'M') * np.arange(ds['month'].size))).astype('datetime64[ns]')
+    ds.time_bnds.attrs.clear()
+
+    # Update global attributes:
+    ds.attrs.clear()
+    ds = ds.assign_attrs({
+        "Conventions": "CF-1.5",
+        "title": f"NOAA OISSTv2.1 Monthly Climatology ({start_yr}-{end_yr})",
+        "description": f"NOAA 1/4° Monthly Optimum Interpolation Sea Surface Temperature (OISST) version 2.1 monthly sea surface temperature and sea ice fraction climatology ({start_yr}-{end_yr}).",
+        "source": "Numerical models: Optimal Interpolation. In-situ observations: ICOADS-D R3.0.2, Argo GDAC. Satellite observations: Advanced Very High Resolution Radiometer (AVHRR).",
+        "dataset_type": "observation",
+        "product_type": "climatology",
+        "product_version": "2.1",
+        "institution": "NOAA National Centers for Environmental Information (NCEI)",
+        "citation": "Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H.-M. Zhang, 2021: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1, Journal of Climate, 34, 2923-2939. doi: 10.1175/JCLI-D-20-0166.1",
+        "references": "Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H.-M. Zhang, 2020: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1, Journal of Climate, 34, 2923-2939. doi: 10.1175/JCLI-D-20-0166.1. Banzon, V., Smith, T. M., Chin, T. M., Liu, C., and Hankins, W., 2016: A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies. Earth Syst. Sci. Data, 8, 165-176, doi:10.5194/essd-8-165-2016. Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. Journal of Climate, 20, 5473-5496, doi:10.1175/JCLI-D-14-00293.1",
+        "acknowledgement": "NOAA OI SST V2 High Resolution Dataset data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at https://psl.noaa.gov.",
+        "license": "OISST v2.1 data were obtained from https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html and are provided under a Creative Commons CC0 1.0 Universal License https://creativecommons.org/publicdomain/zero/1.0/",
+        "doi": "10.1175/JCLI-D-20-0166.1",
+        "platform": "gr",
+        "horizontal_grid_type": "regular rectilinear",
+        "horizontal_grid_resolution": "0.25 degree",
+        "aggregation": "mean",
+        "aggregation_frequency": "monthly",
+        "status": "completed",
+        "update_frequency": "None",
+        "bbox": "[-180.0, 180.0, -90.0, 90.0]",
+    })
+
+    # Optimise chunk sizes for spatial analysis:
+    ds = ds.chunk({'month': 1, 'latitude': 720, 'longitude': 1440})
+
+    # Update variable encodings:
+    blosccodec = zarr.codecs.BloscCodec(cname="zstd", clevel=3, shuffle=zarr.codecs.BloscShuffle.shuffle)
+    for var in list(ds.data_vars) + list(ds.coords):
+        ds[var].encoding['compressors'] = [blosccodec]
+
+    # Define prefix and commit message based on climatology period:
+    prefix = f"oisst_v2.1_{start_yr}_{end_yr}_monthly_climatology"
+    commit_message = f"Added OISSTv2.1 Sea Surface Temperature Climatology ({start_yr}-{end_yr})."
+
+    send_to_icechunk(
+        file=ds,
+        bucket=bucket,
+        object_prefix=prefix,
+        store_credentials_json=store_credentials_json,
+        exists=exists,
+        append_dim='month',
+        branch=branch,
+        commit_message=commit_message,
+        variable_commits=variable_commits,
+        dask_config_kwargs=None,
+        dask_cluster_kwargs=None,
+        )
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/OISST/send_OISSTv2_monthly_to_os.py b/OceanDataStore/data/OISST/send_OISSTv2_monthly_to_os.py
new file mode 100755
index 00000000..901bd607
--- /dev/null
+++ b/OceanDataStore/data/OISST/send_OISSTv2_monthly_to_os.py
@@ -0,0 +1,145 @@
+# =========================================================
+# send_OISSTv2_monthly_climatology_to_os.py
+#
+# Script to write OISST v2.1 long-term monthly climatologies
+# to Icechunk repositories in JASMIN cloud object storage.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import logging
+
+import numpy as np
+import xarray as xr
+import zarr
+
+from OceanDataStore.cli import initialise_logging, send_to_icechunk
+from OceanDataStore.data.utils import (
+    compute_cell_area,
+    compute_dx,
+    compute_dy,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    # ========== Initialise OceanDataStore Logging ========== #
+    initialise_logging()
+
+    # ========== Send to Icechunk Repository ========== #
+    bucket = "oisst"
+    exists = False
+    store_credentials_json = ".../credentials/jasmin_os_credentials.json"
+    branch = "main"
+    variable_commits = True
+    
+    logging.info("In Progress: Sending OISSTv2.1 monthly mean time series to Icechunk...")
+    # Open OISSTv2 dataset:
+    filepaths = ["/dssgfs01/scratch/otooth/npd_data/observations/OISST/icec.mon.mean.nc",
+                 "/dssgfs01/scratch/otooth/npd_data/observations/OISST/sst.mon.mean.nc"
+                ]
+    ds = xr.merge([xr.open_dataset(filepath, decode_times=False) for filepath in filepaths], compat="no_conflicts")
+    # Open OISSTv2 land-sea mask dataset:
+    ds_mask = xr.open_dataset("http://psl.noaa.gov/thredds/dodsC/Datasets/noaa.oisst.v2.highres/lsmask.oisst.nc", decode_times=False)
+    ds_mask = ds_mask.squeeze(drop=True).rename({"lon": "longitude", "lat": "latitude", "lsmask": "mask"})
+    ds_mask = ds_mask.assign_coords(
+        longitude=((ds_mask["longitude"] + 180) % 360) - 180
+    )
+
+    # Standardise coordinate dimension names:
+    ds = ds.rename({"lon": "longitude", "lat": "latitude"})
+
+    # Update longitude coordinates to be in the range [-180, 180]:
+    ds = ds.assign_coords(
+        longitude=((ds["longitude"] + 180) % 360) - 180
+    )
+    ds = ds.sortby("longitude")
+
+    # Update time coordinate to be datetime objects:
+    ds = ds.assign_coords(time=(np.datetime64('1800-01-01', 'D') + (np.timedelta64(1, 'D') * ds['time'])).astype('datetime64[ns]'))
+    ds['time'].attrs.pop('units', None)
+
+    # Rename variables to standard names:
+    ds = ds.rename({"sst": "tos",
+                    "icec": "siconc",
+                    })
+
+    # Add standard names and units:
+    ds["tos"].attrs["standard_name"] = "sea_surface_temperature"
+    ds["siconc"].attrs["standard_name"] = "sea_ice_area_fraction"
+    ds["siconc"].attrs["units"] = "1"
+
+    # Add OISSTv2 land mask:
+    ds["mask"] = ds_mask["mask"]
+    ds["mask"].attrs.clear()
+    ds["mask"] = ds["mask"].assign_attrs({'long_name': "Land-Sea Binary Mask",
+                                          "standard_name": "sea_binary_mask",
+                                          "comment": "1 = sea, 0 = land"
+                                          })
+
+    # Add horizontal grid cell area:
+    ds["dx"] = compute_dx(ds)
+    ds["dy"] = compute_dy(ds)
+    ds['cell_area'] = compute_cell_area(ds)
+
+    # Add Northern and Southern Hemisphere sea ice area timeseries:
+    ds['siarea_NH'] = (ds['siconc'].where(ds['latitude'] > 0) * ds['cell_area']).sum(dim=['latitude', 'longitude'])
+    ds['siarea_NH'].attrs = {'long_name': 'Total Northern Hemisphere Sea Ice Area', 'standard_name': 'sea_ice_area', 'units': 'm2'}
+
+    ds['siarea_SH'] = (ds['siconc'].where(ds['latitude'] < 0) * ds['cell_area']).sum(dim=['latitude', 'longitude'])
+    ds['siarea_SH'].attrs = {'long_name': 'Total Southern Hemisphere Sea Ice Area', 'standard_name': 'sea_ice_area', 'units': 'm2'}
+
+    # Update global attributes:
+    ds.attrs.clear()
+    ds = ds.assign_attrs({
+        "Conventions": "CF-1.5",
+        "title": "NOAA OISSTv2.1 Monthly Timeseries",
+        "description": "NOAA 1/4° Monthly Optimum Interpolation Sea Surface Temperature (OISST) version 2.1 monthly sea surface temperature and sea ice fraction timeseries.",
+        "source": "Numerical models: Optimal Interpolation. In-situ observations: ICOADS-D R3.0.2, Argo GDAC. Satellite observations: Advanced Very High Resolution Radiometer (AVHRR).",
+        "dataset_type": "observation",
+        "product_type": "timeseries",
+        "product_version": "2.1",
+        "institution": "NOAA National Centers for Environmental Information (NCEI)",
+        "citation": "Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H.-M. Zhang, 2021: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1, Journal of Climate, 34, 2923-2939. doi: 10.1175/JCLI-D-20-0166.1",
+        "references": "Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H.-M. Zhang, 2020: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1, Journal of Climate, 34, 2923-2939. doi: 10.1175/JCLI-D-20-0166.1. Banzon, V., Smith, T. M., Chin, T. M., Liu, C., and Hankins, W., 2016: A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies. Earth Syst. Sci. Data, 8, 165-176, doi:10.5194/essd-8-165-2016. Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. Journal of Climate, 20, 5473-5496, doi:10.1175/JCLI-D-14-00293.1",
+        "acknowledgement": "NOAA OI SST V2 High Resolution Dataset data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at https://psl.noaa.gov.",
+        "license": "OISST v2.1 data were obtained from https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html and are provided under a Creative Commons CC0 1.0 Universal License https://creativecommons.org/publicdomain/zero/1.0/",
+        "doi": "10.1175/JCLI-D-20-0166.1",
+        "platform": "gr",
+        "horizontal_grid_type": "regular rectilinear",
+        "horizontal_grid_resolution": "0.25 degree",
+        "aggregation": "mean",
+        "aggregation_frequency": "monthly",
+        "status": "ongoing",
+        "update_frequency": "quarterly",
+        "bbox": "[-180.0, 180.0, -90.0, 90.0]",
+    })
+
+    # Optimise chunk sizes for spatial analysis:
+    ds = ds.chunk({'time': 1, 'latitude': 720, 'longitude': 1440})
+
+    # Update variable encodings:
+    blosccodec = zarr.codecs.BloscCodec(cname="zstd", clevel=3, shuffle=zarr.codecs.BloscShuffle.shuffle)
+    for var in list(ds.data_vars) + list(ds.coords):
+        ds[var].encoding['compressors'] = [blosccodec]
+
+    # Define prefix and commit message based on climatology period:
+    prefix = "oisst_v2.1_monthly"
+    commit_message = "Added OISSTv2.1 Sea Surface Temperature & Sea Ice Fraction Monthly Timeseries (1981-2026)."
+
+    send_to_icechunk(
+        file=ds,
+        bucket=bucket,
+        object_prefix=prefix,
+        store_credentials_json=store_credentials_json,
+        exists=exists,
+        append_dim='time',
+        branch=branch,
+        commit_message=commit_message,
+        variable_commits=variable_commits,
+        dask_config_kwargs=None,
+        dask_cluster_kwargs=None,
+        )
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/WOA23/download_WOA23_climatology.sh b/OceanDataStore/data/WOA23/download_WOA23_climatology.sh
new file mode 100755
index 00000000..c70bfe20
--- /dev/null
+++ b/OceanDataStore/data/WOA23/download_WOA23_climatology.sh
@@ -0,0 +1,41 @@
+#!/bin/bash
+
+# ----------------------------------------------------------------
+# download_WOA23_climatology.sh
+#
+# Description: Download the World Ocean Atlas 2023 30-year climatologies
+# using URLs provided by the National Centers for Environmental Information.
+# WOA23 provides Climate Normals for temperature and salinity at 0.25 degree
+# resolution as 30-year averages for validating models.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-05-27
+# ----------------------------------------------------------------
+set -euo pipefail
+
+echo "==================================================="
+echo "          Downloading WOA23 Climatologies"
+echo "                     v0.1.0"
+echo "               Oliver J. Tooth, NOC"
+echo "==================================================="
+echo "In Progress: Downloading WOA23 Upper 1500m Climatologies..."
+for i in {00..16}; do echo $i; wget https://www.ncei.noaa.gov/data/oceans/woa/WOA23/DATA/temperature/netcdf/decav71A0/0.25/woa23_decav71A0_t${i}_04.nc ; done
+
+for i in {00..16}; do echo $i; wget https://www.ncei.noaa.gov/data/oceans/woa/WOA23/DATA/temperature/netcdf/decav81B0/0.25/woa23_decav81B0_t${i}_04.nc ; done
+
+for i in {00..16}; do echo $i; wget https://www.ncei.noaa.gov/data/oceans/woa/WOA23/DATA/temperature/netcdf/decav91C0/0.25/woa23_decav91C0_t${i}_04.nc ; done
+echo "Completed: Downloaded Upper 1500m WOA23 temperature climatologies"
+
+echo "In Progress: Downloading WOA23 Upper 1500m Salinity Climatologies..."
+for i in {00..16}; do echo $i; wget https://www.ncei.noaa.gov/data/oceans/woa/WOA23/DATA/salinity/netcdf/decav71A0/0.25/woa23_decav71A0_s${i}_04.nc ; done
+
+for i in {00..16}; do echo $i; wget https://www.ncei.noaa.gov/data/oceans/woa/WOA23/DATA/salinity/netcdf/decav81B0/0.25/woa23_decav81B0_s${i}_04.nc ; done
+
+for i in {00..16}; do echo $i; wget https://www.ncei.noaa.gov/data/oceans/woa/WOA23/DATA/salinity/netcdf/decav91C0/0.25/woa23_decav91C0_s${i}_04.nc ; done
+echo "Completed: Downloaded Upper 1500m WOA23 salinity climatologies"
+
+# Winter average of full time period, covering full ocean depth:
+echo "In Progress: Downloading WOA23 Full Depth Climatologies..."
+wget https://www.ncei.noaa.gov/data/oceans/woa/WOA23/DATA/temperature/netcdf/decav/0.25/woa23_decav_t13_04.nc
+wget https://www.ncei.noaa.gov/data/oceans/woa/WOA23/DATA/salinity/netcdf/decav/0.25/woa23_decav_s13_04.nc
+echo "Completed: Downloaded WOA23 Full Depth Climatologies"
diff --git a/OceanDataStore/data/WOA23/run_send_WOA23_annual_climatology_to_os.slurm b/OceanDataStore/data/WOA23/run_send_WOA23_annual_climatology_to_os.slurm
new file mode 100755
index 00000000..b9847e06
--- /dev/null
+++ b/OceanDataStore/data/WOA23/run_send_WOA23_annual_climatology_to_os.slurm
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH --job-name=woa23_annual_climatology
+#SBATCH --partition=compute
+#SBATCH --time=01:00:00
+#SBATCH --ntasks-per-core=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --ntasks-per-socket=32
+#SBATCH --nodes=1
+
+# ==============================================================
+# run_send_WOA23_annual_climatology_to_os.slurm
+#
+# Description: SLURM script to send the WOA23 Annual Climatology
+# datasets to Icechunk repository.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-06-05
+#
+# ==============================================================
+set -euo pipefail
+
+# -- Python Environment -- #
+# Activate miniconda environment:
+source .../miniforge3/bin/activate
+conda activate env_ods
+
+# -- Send WOA23 annual climatology to JASMIN OS -- #
+echo "In Progress: Sending WOA23 Annual Climatologies to Icechunk..."
+
+python3 send_WOA23_annual_climatology_to_os.py
+
+echo "Completed: Sent WOA23 Annual Climatologies to Icechunk."
diff --git a/OceanDataStore/data/WOA23/run_send_WOA23_monthly_climatology_to_os.slurm b/OceanDataStore/data/WOA23/run_send_WOA23_monthly_climatology_to_os.slurm
new file mode 100755
index 00000000..78d3f1bd
--- /dev/null
+++ b/OceanDataStore/data/WOA23/run_send_WOA23_monthly_climatology_to_os.slurm
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH --job-name=woa23_monthly_climatology
+#SBATCH --partition=compute
+#SBATCH --time=01:00:00
+#SBATCH --ntasks-per-core=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --ntasks-per-socket=32
+#SBATCH --nodes=1
+
+# ==============================================================
+# run_send_WOA23_monthly_climatology_to_os.slurm
+#
+# Description: SLURM script to send the WOA23 Monthly Climatology
+# datasets to Icechunk repository.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# Created On: 2026-06-06
+#
+# ==============================================================
+set -euo pipefail
+
+# -- Python Environment -- #
+# Activate miniconda environment:
+source .../miniforge3/bin/activate
+conda activate env_ods
+
+# -- Send WOA23 monthly climatology to JASMIN OS -- #
+echo "In Progress: Sending WOA23 Monthly Climatologies to Icechunk..."
+
+python3 send_WOA23_monthly_climatology_to_os.py
+
+echo "Completed: Sent WOA23 Monthly Climatologies to Icechunk."
diff --git a/OceanDataStore/data/WOA23/send_WOA23_annual_climatology_to_os.py b/OceanDataStore/data/WOA23/send_WOA23_annual_climatology_to_os.py
new file mode 100755
index 00000000..44ef3efa
--- /dev/null
+++ b/OceanDataStore/data/WOA23/send_WOA23_annual_climatology_to_os.py
@@ -0,0 +1,263 @@
+# =========================================================
+# send_WOA23_annual_climatology_to_os.py
+#
+# Script to write WOA23 annual climatologies to Icechunk
+# repository in JASMIN cloud object storage.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import logging
+
+import numpy as np
+import pandas as pd
+import xarray as xr
+import zarr
+
+from OceanDataStore.cli import initialise_logging, send_to_icechunk
+from OceanDataStore.data.utils import (
+    compute_cell_area,
+    compute_dx,
+    compute_dy,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    # ========== Initialise OceanDataStore Logging ========== #
+    initialise_logging()
+    logging.info("In Progress: Sending WOA23 Annual Climatologies to JASMIN Object Store.")
+
+    # ========== Prepare Data ========== #
+    filedir = "/dssgfs01/scratch/otooth/npd_data/observations/WOA2023"
+    # Define file paths for WOA23 annual climatologies:
+    salinity_paths = [f"{filedir}/woa23_decav71A0_s00_04.nc",
+                      f"{filedir}/woa23_decav81B0_s00_04.nc",
+                      f"{filedir}/woa23_decav91C0_s00_04.nc",
+                      ]
+    temperature_paths = [f"{filedir}/woa23_decav71A0_t00_04.nc",
+                         f"{filedir}/woa23_decav81B0_t00_04.nc",
+                         f"{filedir}/woa23_decav91C0_t00_04.nc"
+                         ]
+
+    # Define year bounds for each WOA23 annual climatology:
+    year_bound_start = [1971, 1981, 1991]
+    year_bound_end = [2000, 2010, 2020]
+
+    for temp_path, sal_path, start_year, end_year in zip(temperature_paths, salinity_paths, year_bound_start, year_bound_end):
+        logging.info(f"-> In Progress: Preparing WOA23 {start_year}-{end_year} annual climatology...")
+        # Open WOA23 annual climatologies dataset:
+        ds_s = xr.open_dataset(sal_path, decode_times=False)
+        ds_t = xr.open_dataset(temp_path, decode_times=False)
+
+        ds = xr.merge([ds_s, ds_t], compat='no_conflicts').squeeze(drop=True)
+        ds['climatology_bounds'].values = np.array([np.datetime64(f'{start_year}-01-01', 'ns'), np.datetime64(f'{end_year}-12-31', 'ns')])
+
+        ds = ds.rename({"lon": "longitude",
+                        "lat": "latitude",
+                        "climatology_bounds": "time_bnds"
+                        })
+
+        # Use OceanDataStore standard variable names:
+        for var in ds.data_vars:
+            if var.startswith("t_"):
+                ds = ds.rename({var: var.replace("t_", "thetao_")})
+            elif var.startswith("s_"):
+                ds = ds.rename({var: var.replace("s_", "so_")})
+
+        # Add ancillary variables:
+        ds['dx'] = compute_dx(ds)
+        ds['dy'] = compute_dy(ds)
+        ds['cell_area'] = compute_cell_area(ds)
+
+        # Custom ancillary variables:
+        ds['cell_thickness'] = ds['depth_bnds'].isel(nbounds=1) - ds['depth_bnds'].isel(nbounds=0)
+        ds['cell_volume'] = ds['cell_thickness'] * ds['cell_area']
+
+        # Update attributes for custom ancillary variables:
+        ds['cell_thickness'].attrs.update({
+            'long_name': "Grid-Cell Thickness",
+            'standard_name': "cell_thickness",
+            'units': "m",
+        })
+        ds['cell_volume'].attrs.update({
+            'long_name': "Grid-Cell Volume",
+            'standard_name': "cell_volume",
+            'units': "m3",
+        })
+        logging.info(f"Completed: Prepared WOA23 {start_year}-{end_year} annual climatology dataset with ancillary variables.")
+
+        # ========== Prepare Ancillary Data ========== #
+        # Open WOA23 land sea mask:
+        df_mask = pd.read_table("/dssgfs01/working/otooth/Software/OceanDataStore/OceanDataStore/data/WOA23/data/landsea_04.msk",
+                                delimiter=',',
+                                header=0
+                                )
+        # Define level of sea floor (i.e. bottom standard level) at each grid cell:
+        ds['bottom_level'] = xr.full_like(ds['cell_area'], fill_value=np.nan).squeeze(drop=True)
+        ds['bottom_level'].data = df_mask['Bottom_Standard_Level'].values.reshape(720, 1440)
+        ds['bottom_level'].name = "bottom_level"
+        ds['bottom_level'].attrs = {"standard_name": "model_level_number_at_sea_floor",
+                            "long_name": "Model Level Number at Sea Floor",
+                            "units": "1"}
+        # Define land sea mask (1 for ocean grid cells, 0 for land grid cells):
+        ds['mask'] = ds['bottom_level'] > 1
+        ds['mask'].name = "land_binary_mask"
+        ds["mask"] = ds["mask"].assign_attrs({'long_name': "Land-Sea Binary Mask",
+                                              "standard_name": "sea_binary_mask",
+                                              "comment": "1 = sea, 0 = land"
+                                              })
+        logging.info("Completed: Prepared land sea mask & bottom level variables.")
+        
+        # Open WOA23 basin mask:
+        df_basin = pd.read_table("/dssgfs01/working/otooth/Software/OceanDataStore/OceanDataStore/data/WOA23/data/basinmask_04.msk",
+                                delimiter=',',
+                                header=0
+                                )
+        # Define basin mask (integer values for each ocean basin, NaN for land grid cells):
+        ds['basin_mask'] = xr.full_like(ds['cell_area'], fill_value=np.nan).squeeze(drop=True)
+        for basin, longitude, latitude in df_basin[["Basin_0m", "Longitude", "Latitude"]].itertuples(index=False):
+            ds['basin_mask'].loc[dict(latitude=latitude, longitude=longitude)] = basin
+
+        ds['basin_mask'].attrs = {
+            "standard_name": "ocean_basin_mask",
+            "long_name": "Ocean Basin Mask",
+            "basin_name": {
+            1: "Atlantic Ocean",
+            2: "Pacific Ocean",
+            3: "Indian Ocean",
+            4: "Mediterranean Sea",
+            5: "Baltic Sea",
+            6: "Black Sea",
+            7: "Red Sea",
+            8: "Persian Gulf",
+            9: "Hudson Bay",
+            10: "Southern Ocean",
+            11: "Arctic Ocean",
+            12: "Sea of Japan",
+            13: "Kara Sea",
+            14: "Sulu Sea",
+            15: "Baffin Bay",
+            16: "East Mediterranean",
+            17: "West Mediterranean",
+            18: "Sea of Okhotsk",
+            19: "Banda Sea",
+            20: "Caribbean Sea",
+            21: "Andaman Basin",
+            22: "North Caribbean",
+            23: "Gulf of Mexico",
+            24: "Beaufort Sea",
+            25: "South China Sea",
+            26: "Barents Sea",
+            27: "Celebes Sea",
+            28: "Aleutian Basin",
+            29: "Fiji Basin",
+            30: "North American Basin",
+            31: "West European Basin",
+            32: "Southeast Indian Basin",
+            33: "Coral Sea",
+            34: "East Indian Basin",
+            35: "Central Indian Basin",
+            36: "Southwest Atlantic Basin",
+            37: "Southeast Atlantic Basin",
+            38: "Southeast Pacific Basin",
+            39: "Guatemala Basin",
+            40: "East Caroline Basin",
+            41: "Marianas Basin",
+            42: "Philippine Sea",
+            43: "Arabian Sea",
+            44: "Chile Basin",
+            45: "Somali Basin",
+            46: "Mascarene Basin",
+            47: "Crozet Basin",
+            48: "Guinea Basin",
+            49: "Brazil Basin",
+            50: "Argentine Basin",
+            51: "Tasman Sea",
+            52: "Atlantic Indian Basin",
+            53: "Caspian Sea",
+            54: "Sulu Sea II",
+            55: "Venezuela Basin",
+            56: "Bay of Bengal",
+            57: "Java Sea",
+            58: "East Indian Atlantic Basin",
+            59: "Chiloe",
+            60: "Bransfield Strait",
+            }
+        }
+        logging.info("Completed: Prepared ocean basin mask variable.")
+
+        # ========== CF Attributes ========== #
+        ds.attrs.clear()
+        ds = ds.assign_attrs({
+            "Conventions": "CF-1.6",
+            "title": f"World Ocean Atlas 2023 temperature and salinity annual climatology ({start_year}-{end_year}).",
+            "description": f"World Ocean Atlas 2023 (WOA23) temperature and salinity annual climatology for the global ocean from objectively analysed, quality controlled in-situ profile data ({start_year}-{end_year}).",
+            "source": "Numerical models: Objective Analysis. In-situ observations: World Ocean Database (WOD).",
+            "dataset_type": "observation",
+            "product_type": "climatology",
+            "product_version": "1.0",
+            "institution": "NOAA National Centers for Environmental Information (NCEI)",
+            "citation": "Reagan, James R.; Boyer, Tim P.; García, Hernán E.; Locarnini, Ricardo A.; Baranova, Olga K.; Bouchard, Courtney; Cross, Scott L.; Mishonov, Alexey V.; Paver, Christopher R.; Seidov, Dan; Wang, Zhankun; Dukhovskoy, Dmitry (2023). World Ocean Atlas 2023 (NCEI Accession 0270533). https://www.ncei.noaa.gov/archive/accession/0270533. In Reagan, James R.; Boyer, Tim P.; García, Hernán E.; Locarnini, Ricardo A.; Baranova, Olga K.; Bouchard, Courtney; Cross, Scott L.; Mishonov, Alexey V.; Paver, Christopher R.; Seidov, Dan; Wang, Zhankun; Dukhovskoy, Dmitry (2023). World Ocean Atlas 2023. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/va26-hv25. Accessed 06-05-2026.",
+            "references": "Locarnini, R.A., A.V. Mishonov, O.K. Baranova, J.R. Reagan, T.P. Boyer, D. Seidov, Z. Wang, H.E. Garcia, C. Bouchard, S.L. Cross, C.R. Paver, and D. Dukhovskoy (2024). World Ocean Atlas 2023, Volume 1: Temperature. A. Mishonov Technical Editor, NOAA Atlas NESDIS 89. https://doi.org/10.25923/54bh-1613. Reagan, J.R., D. Seidov, Z. Wang, D. Dukhovskoy, T.P. Boyer, R.A. Locarnini, O.K. Baranova, A.V. Mishonov, H.E. Garcia, C. Bouchard, S.L. Cross, and C.R. Paver (2023). World Ocean Atlas 2023, Volume 2: Salinity. A. Mishonov Technical Editor, NOAA Atlas NESDIS 90, https://doi.org/10.25923/70qt-9574.",
+            "acknowledgement": "None",
+            "license": "World Ocean Atlas 2023 data were obtained from https://www.ncei.noaa.gov/access/world-ocean-atlas-2023/ and are provided under a Creative Commons CC0 1.0 Universal License https://creativecommons.org/publicdomain/zero/1.0/",
+            "doi": "https://doi.org/10.25921/va26-hv25",
+            "platform": "gr",
+            "horizontal_grid_type": "regular rectilinear",
+            "horizontal_grid_resolution": "0.25 degree",
+            "vertical_grid_type": "z",
+            "vertical_grid_coordinate": "depth",
+            "vertical_grid_levels": 57,
+            "aggregation": "mean",
+            "aggregation_frequency": "annual",
+            "status": "completed",
+            "update_frequency": "None",
+            "bbox": "[-180.0, 180.0, -90.0, 90.0]",
+        })
+        logging.info(f"Completed: Added CF-compliant global attributes to WOA23 {start_year}-{end_year} annual climatology dataset.")
+
+        # ========== Send to Icechunk Repository ========== #
+        logging.info(f"In Progress: Sending WOA23 {start_year}-{end_year} annual climatology to Icechunk Repository...")
+        bucket = "woa23"
+        prefix = f"woa23_{start_year}_{end_year}_annual_climatology"
+        exists = False
+        store_credentials_json = ".../credentials/jasmin_os_credentials.json"
+        branch = "main"
+        commit_message = f"Added WOA23 {start_year}-{end_year} annual climatology."
+        variable_commits = True
+        config_kwargs = {
+                "temporary_directory":".../OceanDataStore/OceanDataStore/data/WOA23/",
+                "local_directory":".../OceanDataStore/OceanDataStore/data/WOA23/"
+            }
+        cluster_kwargs = {
+                "n_workers" : 25,
+                "threads_per_worker" : 1,
+                "memory_limit":"3GB"
+            }
+        
+        # Optimise chunk sizes for spatial analysis:
+        ds = ds.chunk({'longitude': 1440, 'latitude': 720, 'depth': 5})
+    
+        # Update variable encodings:
+        blosccodec = zarr.codecs.BloscCodec(cname="zstd", clevel=3, shuffle=zarr.codecs.BloscShuffle.shuffle)
+        for var in list(ds.data_vars) + list(ds.coords):
+            ds[var].encoding['compressors'] = [blosccodec]
+
+        send_to_icechunk(
+            file=ds,
+            bucket=bucket,
+            object_prefix=prefix,
+            store_credentials_json=store_credentials_json,
+            exists=exists,
+            branch=branch,
+            commit_message=commit_message,
+            variable_commits=variable_commits,
+            dask_config_kwargs=config_kwargs,
+            dask_cluster_kwargs=cluster_kwargs,
+            )
+
+        logging.info(f"Completed: Sent WOA23 {start_year}-{end_year} annual climatology to Icechunk Repository.")
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/WOA23/send_WOA23_monthly_climatology_to_os.py b/OceanDataStore/data/WOA23/send_WOA23_monthly_climatology_to_os.py
new file mode 100755
index 00000000..4f29f12a
--- /dev/null
+++ b/OceanDataStore/data/WOA23/send_WOA23_monthly_climatology_to_os.py
@@ -0,0 +1,292 @@
+# =========================================================
+# send_WOA23_monthly_climatology_to_os.py
+#
+# Script to write WOA23 monthly climatologies to Icechunk
+# repository in JASMIN cloud object storage.
+#
+# Created By: Ollie Tooth (oliver.tooth@noc.ac.uk)
+# =========================================================
+import glob
+import logging
+
+import numpy as np
+import pandas as pd
+import xarray as xr
+import zarr
+
+from OceanDataStore.cli import initialise_logging, send_to_icechunk
+from OceanDataStore.data.utils import (
+    compute_cell_area,
+    compute_dx,
+    compute_dy,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# -- Utility Functions -- #
+def get_filepaths(file_prefix:str, file_directory:str) -> list:
+    """
+    Get filepaths for World Ocean Atlas 2023 climate normals
+    in-situ temperature and practical salinity data. 
+
+    Parameters
+    ----------
+    file_prefix : str
+        Prefix of the WOA23 filepaths to be returned.
+    file_directory : str
+        Directory containing the WOA23 files.
+
+    Returns
+    -------
+    filepaths : list
+        List of WOA23 filepaths for practical salinity & in-situ temperature.
+    """
+    # Define salinity and temperature filenames:
+    salinity_filename = f"{file_prefix}_s*.nc"
+    temperature_filename = f"{file_prefix}_t*.nc"
+
+    # Collect filepaths for salinity and temperature data:
+    filepaths_sal = sorted(glob.glob(f"{file_directory}/{salinity_filename}"))
+    filepaths_temp = sorted(glob.glob(f"{file_directory}/{temperature_filename}"))
+
+    return filepaths_sal, filepaths_temp
+
+
+def main():
+    # ========== Initialise OceanDataStore Logging ========== #
+    initialise_logging()
+    logging.info("In Progress: Sending WOA23 Monthly Climatologies to JASMIN Object Store.")
+
+    # ========== Prepare Data ========== #
+    filedir = "/dssgfs01/scratch/otooth/npd_data/observations/WOA2023"
+    salinity_paths, temperature_paths = [], []
+    for prefix in ['woa23_decav71A0', 'woa23_decav81B0', 'woa23_decav91C0']:
+        # Define file paths for WOA23 monthly climatologies:
+        filepaths_sal, filepaths_temp = get_filepaths(file_directory=filedir, file_prefix=prefix)
+        salinity_paths.append(filepaths_sal[1:13])
+        temperature_paths.append(filepaths_temp[1:13])
+
+    # Define year bounds for each WOA23 monthly climatology:
+    year_bound_start = [1971, 1981, 1991]
+    year_bound_end = [2000, 2010, 2020]
+
+    for temp_path, sal_path, start_year, end_year in zip(temperature_paths, salinity_paths, year_bound_start, year_bound_end):
+        logging.info(f"-> In Progress: Preparing WOA23 {start_year}-{end_year} monthly climatology...")
+        # Open WOA23 monthly climatologies dataset:
+        ds_s = xr.open_mfdataset(sal_path, decode_times=False, data_vars='all')
+        ds_t = xr.open_mfdataset(temp_path, decode_times=False, data_vars='all')
+
+        ds = xr.merge([ds_s, ds_t], compat='no_conflicts').squeeze(drop=True)
+        ds = ds.rename({'time': 'month'}).assign_coords({'month': np.arange(1, 13)})
+        ds['climatology_bounds'].data = np.array([[np.datetime64(f'{start_year}-{month:02d}', 'M'), np.datetime64(f'{end_year}-{month:02d}', 'M')] for month in range(1, 13)])
+
+        ds = ds.rename({"lon": "longitude",
+                        "lat": "latitude",
+                        "climatology_bounds": "time_bnds"
+                        })
+
+        # Use OceanDataStore standard variable names:
+        for var in ds.data_vars:
+            if var.startswith("t_"):
+                ds = ds.rename({var: var.replace("t_", "thetao_")})
+            elif var.startswith("s_"):
+                ds = ds.rename({var: var.replace("s_", "so_")})
+
+        # Add ancillary variables:
+        ds['dx'] = compute_dx(ds)
+        ds['dy'] = compute_dy(ds)
+        ds['cell_area'] = compute_cell_area(ds)
+
+        # Custom ancillary variables:
+        ds['cell_thickness'] = ds['depth_bnds'].isel(nbounds=1) - ds['depth_bnds'].isel(nbounds=0)
+        ds['cell_volume'] = ds['cell_thickness'] * ds['cell_area']
+
+        # Update attributes for custom ancillary variables:
+        ds['cell_thickness'].attrs.update({
+            'long_name': "Grid-Cell Thickness",
+            'standard_name': "cell_thickness",
+            'units': "m",
+        })
+        ds['cell_volume'].attrs.update({
+            'long_name': "Grid-Cell Volume",
+            'standard_name': "cell_volume",
+            'units': "m3",
+        })
+        logging.info(f"Completed: Prepared WOA23 {start_year}-{end_year} monthly climatology dataset with ancillary variables.")
+
+        # ========== Prepare Ancillary Data ========== #
+        # Open WOA23 land sea mask:
+        df_mask = pd.read_table("/dssgfs01/working/otooth/Software/OceanDataStore/OceanDataStore/data/WOA23/data/landsea_04.msk",
+                                delimiter=',',
+                                header=0
+                                )
+        # Define level of sea floor (i.e. bottom standard level) at each grid cell:
+        ds['bottom_level'] = xr.full_like(ds['cell_area'], fill_value=np.nan).squeeze(drop=True)
+        ds['bottom_level'].data = df_mask['Bottom_Standard_Level'].values.reshape(720, 1440)
+        ds['bottom_level'].name = "bottom_level"
+        ds['bottom_level'].attrs = {"standard_name": "model_level_number_at_sea_floor",
+                                    "long_name": "Model Level Number at Sea Floor",
+                                    "units": "1"
+                                    }
+        # Define land sea mask (1 for ocean grid cells, 0 for land grid cells):
+        ds['mask'] = ds['bottom_level'] > 1
+        ds['mask'].name = "sea_binary_mask"
+        ds["mask"] = ds["mask"].assign_attrs({'long_name': "Land-Sea Binary Mask",
+                                              "standard_name": "sea_binary_mask",
+                                              "comment": "1 = sea, 0 = land"
+                                              })
+        logging.info("Completed: Prepared land sea mask & bottom level variables.")
+        
+        # Open WOA23 basin mask:
+        df_basin = pd.read_table("/dssgfs01/working/otooth/Software/OceanDataStore/OceanDataStore/data/WOA23/data/basinmask_04.msk",
+                                delimiter=',',
+                                header=0
+                                )
+        # Define basin mask (integer values for each ocean basin, NaN for land grid cells):
+        ds['basin_mask'] = xr.full_like(ds['cell_area'], fill_value=np.nan).squeeze(drop=True)
+        for basin, longitude, latitude in df_basin[["Basin_0m", "Longitude", "Latitude"]].itertuples(index=False):
+            ds['basin_mask'].loc[dict(latitude=latitude, longitude=longitude)] = basin
+
+        ds['basin_mask'].attrs = {
+            "standard_name": "ocean_basin_mask",
+            "long_name": "Ocean Basin Mask",
+            "basin_name": {
+            1: "Atlantic Ocean",
+            2: "Pacific Ocean",
+            3: "Indian Ocean",
+            4: "Mediterranean Sea",
+            5: "Baltic Sea",
+            6: "Black Sea",
+            7: "Red Sea",
+            8: "Persian Gulf",
+            9: "Hudson Bay",
+            10: "Southern Ocean",
+            11: "Arctic Ocean",
+            12: "Sea of Japan",
+            13: "Kara Sea",
+            14: "Sulu Sea",
+            15: "Baffin Bay",
+            16: "East Mediterranean",
+            17: "West Mediterranean",
+            18: "Sea of Okhotsk",
+            19: "Banda Sea",
+            20: "Caribbean Sea",
+            21: "Andaman Basin",
+            22: "North Caribbean",
+            23: "Gulf of Mexico",
+            24: "Beaufort Sea",
+            25: "South China Sea",
+            26: "Barents Sea",
+            27: "Celebes Sea",
+            28: "Aleutian Basin",
+            29: "Fiji Basin",
+            30: "North American Basin",
+            31: "West European Basin",
+            32: "Southeast Indian Basin",
+            33: "Coral Sea",
+            34: "East Indian Basin",
+            35: "Central Indian Basin",
+            36: "Southwest Atlantic Basin",
+            37: "Southeast Atlantic Basin",
+            38: "Southeast Pacific Basin",
+            39: "Guatemala Basin",
+            40: "East Caroline Basin",
+            41: "Marianas Basin",
+            42: "Philippine Sea",
+            43: "Arabian Sea",
+            44: "Chile Basin",
+            45: "Somali Basin",
+            46: "Mascarene Basin",
+            47: "Crozet Basin",
+            48: "Guinea Basin",
+            49: "Brazil Basin",
+            50: "Argentine Basin",
+            51: "Tasman Sea",
+            52: "Atlantic Indian Basin",
+            53: "Caspian Sea",
+            54: "Sulu Sea II",
+            55: "Venezuela Basin",
+            56: "Bay of Bengal",
+            57: "Java Sea",
+            58: "East Indian Atlantic Basin",
+            59: "Chiloe",
+            60: "Bransfield Strait",
+            }
+        }
+        logging.info("Completed: Prepared ocean basin mask variable.")
+
+        # ========== CF Attributes ========== #
+        ds.attrs.clear()
+        ds = ds.assign_attrs({
+            "Conventions": "CF-1.6",
+            "title": f"World Ocean Atlas 2023 temperature and salinity monthly climatology ({start_year}-{end_year}).",
+            "description": f"World Ocean Atlas 2023 (WOA23) temperature and salinity monthly climatology for the global ocean from objectively analysed, quality controlled in-situ profile data ({start_year}-{end_year}).",
+            "source": "Numerical models: Objective Analysis. In-situ observations: World Ocean Database (WOD).",
+            "dataset_type": "observation",
+            "product_type": "climatology",
+            "product_version": "1.0",
+            "institution": "NOAA National Centers for Environmental Information (NCEI)",
+            "citation": "Reagan, James R.; Boyer, Tim P.; García, Hernán E.; Locarnini, Ricardo A.; Baranova, Olga K.; Bouchard, Courtney; Cross, Scott L.; Mishonov, Alexey V.; Paver, Christopher R.; Seidov, Dan; Wang, Zhankun; Dukhovskoy, Dmitry (2023). World Ocean Atlas 2023 (NCEI Accession 0270533). https://www.ncei.noaa.gov/archive/accession/0270533. In Reagan, James R.; Boyer, Tim P.; García, Hernán E.; Locarnini, Ricardo A.; Baranova, Olga K.; Bouchard, Courtney; Cross, Scott L.; Mishonov, Alexey V.; Paver, Christopher R.; Seidov, Dan; Wang, Zhankun; Dukhovskoy, Dmitry (2023). World Ocean Atlas 2023. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/va26-hv25. Accessed 06-05-2026.",
+            "references": "Locarnini, R.A., A.V. Mishonov, O.K. Baranova, J.R. Reagan, T.P. Boyer, D. Seidov, Z. Wang, H.E. Garcia, C. Bouchard, S.L. Cross, C.R. Paver, and D. Dukhovskoy (2024). World Ocean Atlas 2023, Volume 1: Temperature. A. Mishonov Technical Editor, NOAA Atlas NESDIS 89. https://doi.org/10.25923/54bh-1613. Reagan, J.R., D. Seidov, Z. Wang, D. Dukhovskoy, T.P. Boyer, R.A. Locarnini, O.K. Baranova, A.V. Mishonov, H.E. Garcia, C. Bouchard, S.L. Cross, and C.R. Paver (2023). World Ocean Atlas 2023, Volume 2: Salinity. A. Mishonov Technical Editor, NOAA Atlas NESDIS 90, https://doi.org/10.25923/70qt-9574.",
+            "acknowledgement": "None",
+            "license": "World Ocean Atlas 2023 data were obtained from https://www.ncei.noaa.gov/access/world-ocean-atlas-2023/ and are provided under a Creative Commons CC0 1.0 Universal License https://creativecommons.org/publicdomain/zero/1.0/",
+            "doi": "https://doi.org/10.25921/va26-hv25",
+            "platform": "gr",
+            "horizontal_grid_type": "regular rectilinear",
+            "horizontal_grid_resolution": "0.25 degree",
+            "vertical_grid_type": "z",
+            "vertical_grid_coordinate": "depth",
+            "vertical_grid_levels": 57,
+            "aggregation": "mean",
+            "aggregation_frequency": "monthly",
+            "status": "completed",
+            "update_frequency": "None",
+            "bbox": "[-180.0, 180.0, -90.0, 90.0]",
+        })
+        logging.info(f"Completed: Added CF-compliant global attributes to WOA23 {start_year}-{end_year} monthly climatology dataset.")
+
+        # ========== Send to Icechunk Repository ========== #
+        logging.info(f"In Progress: Sending WOA23 {start_year}-{end_year} monthly climatology to Icechunk Repository...")
+        bucket = "woa23"
+        prefix = f"woa23_{start_year}_{end_year}_monthly_climatology"
+        exists = False
+        store_credentials_json = ".../credentials/jasmin_os_credentials.json"
+        branch = "main"
+        commit_message = f"Added WOA23 {start_year}-{end_year} monthly climatology."
+        variable_commits = True
+        config_kwargs = {
+                "temporary_directory":".../OceanDataStore/OceanDataStore/data/WOA23/",
+                "local_directory":".../OceanDataStore/OceanDataStore/data/WOA23/"
+            }
+        cluster_kwargs = {
+                "n_workers" : 25,
+                "threads_per_worker" : 1,
+                "memory_limit":"3GB"
+            }
+        
+        # Optimise chunk sizes for spatial analysis:
+        ds = ds.chunk({'longitude': 1440, 'latitude': 720, 'depth': 5, 'month': 1})
+
+        # Update variable encodings:
+        blosccodec = zarr.codecs.BloscCodec(cname="zstd", clevel=3, shuffle=zarr.codecs.BloscShuffle.shuffle)
+        for var in list(ds.data_vars) + list(ds.coords):
+            ds[var].encoding['compressors'] = [blosccodec]
+
+        send_to_icechunk(
+            file=ds,
+            bucket=bucket,
+            object_prefix=prefix,
+            store_credentials_json=store_credentials_json,
+            exists=exists,
+            branch=branch,
+            commit_message=commit_message,
+            variable_commits=variable_commits,
+            dask_config_kwargs=config_kwargs,
+            dask_cluster_kwargs=cluster_kwargs,
+            )
+
+        logging.info(f"Completed: Sent WOA23 {start_year}-{end_year} monthly climatology to Icechunk Repository.")
+
+if __name__ == "__main__":
+    main()
diff --git a/OceanDataStore/data/utils.py b/OceanDataStore/data/utils.py
new file mode 100644
index 00000000..37adc0f4
--- /dev/null
+++ b/OceanDataStore/data/utils.py
@@ -0,0 +1,506 @@
+"""
+utils.py
+
+Description: Utility functions for processing gridded ocean data.
+
+Contact: Ollie Tooth (oliver.tooth@noc.ac.uk)
+"""
+# == Import Python packages == #
+import json
+
+import icechunk
+import numpy as np
+import xarray as xr
+import zarr
+
+
+# == Utility Functions == #
+def compute_gc_distance(
+        lat1: xr.DataArray,
+        lon1: xr.DataArray,
+        lat2: xr.DataArray,
+        lon2: xr.DataArray
+) -> xr.DataArray:
+    """
+    Calculate the Great-Circle distance between two sets of
+    geographical points on the Earth's surface.
+
+    Parameters:
+    -----------
+    lat1 : xr.DataArray
+        Latitude of the first set of points (degrees).
+    lon1 : xr.DataArray
+        Longitude of the first set of points (degrees).
+    lat2 : xr.DataArray
+        Latitude of the second set of points (degrees).
+    lon2 : xr.DataArray
+        Longitude of the second set of points (degrees).
+    
+    Returns:
+    --------
+    dist : xr.DataArray
+        Great-circle distance between the two sets
+        of points (meters).
+    
+    """
+    # Define the radius of the Earth in meters:
+    re = 6371000
+
+    # Convert latitudes and longitudes from degrees to radians:
+    lon1, lat1, lon2, lat2 = map(np.deg2rad, [lon1, lat1, lon2, lat2])
+    dlat = lat2 - lat1
+    dlon = lon2 - lon1
+
+    # Calculate the great-circle distance between points:
+    dist = (2*re*np.arcsin(np.sqrt(
+            np.sin(dlat/2)**2 +
+            (np.cos(lat1) *
+            np.cos(lat2) *
+            np.sin(dlon/2)**2)
+            )))
+
+    return dist
+
+
+def compute_dx(
+        ds: xr.Dataset,
+        ) -> xr.DataArray:
+    """
+    Calculate zonal length of each grid cell in meters.
+    
+    The length is calculated using the latitude and longitude coordinates
+    of the input dataset assuming a uniform regular grid.
+
+    Parameters:
+    -----------
+    ds : xr.Dataset
+        Input dataset containing 'latitude' and 'longitude' coordinates.
+
+    Returns:
+    --------
+    xr.DataArray
+        DataArray representing the zonal length of each grid cell.
+    """
+    # -- Validate Input -- #
+    if not isinstance(ds, xr.Dataset):
+        raise TypeError("Input must be an xarray Dataset.")
+    if 'latitude' not in ds.coords or 'longitude' not in ds.coords:
+        raise ValueError("Input dataset must contain 'latitude' and 'longitude' coordinates.")
+
+    # -- Calculate Grid Cell Length -- #
+    # Infer horizontal resolution for uniform grid:
+    dlon = ds['longitude'].diff(dim="longitude").mean().values
+
+    if (ds['longitude'].ndim == 1) and (ds['latitude'].ndim == 1):
+        # Define 2-dimensional longitude and latitude arrays for grid cell centers:
+        lon = np.repeat(ds['longitude'].values[np.newaxis, :], len(ds['latitude']), axis=0)
+        lat = np.repeat(ds['latitude'].values[:, np.newaxis], len(ds['longitude']), axis=1)
+    else:
+        # Use existing 2-dimensional longitude and latitude arrays:
+        lon = ds['longitude'].values
+        lat = ds['latitude'].values
+
+    # Calculate zonal and meridional grid cell dimensions:
+    dx = compute_gc_distance(lon1=lon - dlon / 2, lat1=lat, lon2=lon + dlon / 2, lat2=lat)
+
+    # Define dx DataArray with CF-compliant metadata:
+    dx = xr.DataArray(
+        data=dx,
+        dims=('latitude', 'longitude'),
+        coords={'latitude': ds['latitude'], 'longitude': ds['longitude']},
+        name='dx',
+        attrs={
+            'long_name': 'Grid-Cell Zonal Length',
+            'standard_name': 'cell_x_length',
+            'units': 'm',
+        },
+    )
+
+    return dx
+
+
+def compute_dy(
+        ds: xr.Dataset,
+        ) -> xr.DataArray:
+    """
+    Calculate meridional length of each grid cell in meters.
+    
+    The length is calculated using the latitude and longitude coordinates
+    of the input dataset assuming a uniform regular grid.
+
+    Parameters:
+    -----------
+    ds : xr.Dataset
+        Input dataset containing 'latitude' and 'longitude' coordinates.
+
+    Returns:
+    --------
+    xr.DataArray
+        DataArray representing the meridional length of each grid cell.
+    """
+    # -- Validate Input -- #
+    if not isinstance(ds, xr.Dataset):
+        raise TypeError("Input must be an xarray Dataset.")
+    if 'latitude' not in ds.coords or 'longitude' not in ds.coords:
+        raise ValueError("Input dataset must contain 'latitude' and 'longitude' coordinates.")
+
+    # -- Calculate Grid Cell Length -- #
+    # Infer horizontal resolution for uniform grid:
+    dlat = ds['latitude'].diff(dim="latitude").mean().values
+
+    if (ds['longitude'].ndim == 1) and (ds['latitude'].ndim == 1):
+        # Define 2-dimensional longitude and latitude arrays for grid cell centers:
+        lon = np.repeat(ds['longitude'].values[np.newaxis, :], len(ds['latitude']), axis=0)
+        lat = np.repeat(ds['latitude'].values[:, np.newaxis], len(ds['longitude']), axis=1)
+    else:
+        # Use existing 2-dimensional longitude and latitude arrays:
+        lon = ds['longitude'].values
+        lat = ds['latitude'].values
+
+    # Calculate zonal and meridional grid cell dimensions:
+    dy = compute_gc_distance(lon1=lon, lat1=lat - dlat / 2, lon2=lon, lat2=lat + dlat / 2)
+
+    # Define dy DataArray with CF-compliant metadata:
+    dy = xr.DataArray(
+        data=dy,
+        dims=('latitude', 'longitude'),
+        coords={'latitude': ds['latitude'], 'longitude': ds['longitude']},
+        name='dy',
+        attrs={
+            'long_name': 'Grid-Cell Meridional Length',
+            'standard_name': 'cell_y_length',
+            'units': 'm',
+        },
+    )
+
+    return dy
+
+
+def compute_cell_area(
+        ds: xr.Dataset,
+        ) -> xr.DataArray:
+    """
+    Calculate horizontal area of each grid cell in square meters.
+    
+    The area is calculated using the latitude and longitude coordinates
+    of the input dataset assuming a uniform regular grid.
+
+    Parameters:
+    -----------
+    ds : xr.Dataset
+        Input dataset containing 'latitude' and 'longitude' coordinates.
+
+    Returns:
+    --------
+    xr.DataArray
+        DataArray representing the horizontal area of each grid cell.
+    """
+    # -- Validate Input -- #
+    if not isinstance(ds, xr.Dataset):
+        raise TypeError("Input must be an xarray Dataset.")
+    if 'latitude' not in ds.coords or 'longitude' not in ds.coords:
+        raise ValueError("Input dataset must contain 'latitude' and 'longitude' coordinates.")
+
+    # -- Calculate Grid Cell Area -- #
+    # Infer horizontal resolution for uniform grid:
+    dlon = ds['longitude'].diff(dim="longitude").mean().values
+    dlat = ds['latitude'].diff(dim="latitude").mean().values
+
+    if (ds['longitude'].ndim == 1) and (ds['latitude'].ndim == 1):
+        # Define 2-dimensional longitude and latitude arrays for grid cell centers:
+        lon = np.repeat(ds['longitude'].values[np.newaxis, :], len(ds['latitude']), axis=0)
+        lat = np.repeat(ds['latitude'].values[:, np.newaxis], len(ds['longitude']), axis=1)
+    else:
+        # Use existing 2-dimensional longitude and latitude arrays:
+        lon = ds['longitude'].values
+        lat = ds['latitude'].values
+
+    # Calculate zonal and meridional grid cell dimensions:
+    dx = compute_gc_distance(lon1=lon - dlon / 2, lat1=lat, lon2=lon + dlon / 2, lat2=lat)
+    dy = compute_gc_distance(lon1=lon, lat1=lat - dlat / 2, lon2=lon, lat2=lat + dlat / 2)
+
+    # Define cell_area DataArray with CF-compliant metadata:
+    cell_area = xr.DataArray(
+        data=dx*dy,
+        dims=('latitude', 'longitude'),
+        coords={'latitude': ds['latitude'], 'longitude': ds['longitude']},
+        name='cell_area',
+        attrs={
+            'long_name': 'Grid-Cell Area',
+            'standard_name': 'cell_area',
+            'units': 'm2',
+        },
+    )
+
+    return cell_area
+
+
+def compute_cell_thickness(
+        ds: xr.Dataset,
+    ) -> xr.DataArray:
+    """
+    Calculate vertical thickness of each grid cell in meters.
+
+    Cell thickness is calculated using the depth coordinates of the input dataset assuming a regular grid in the vertical dimension.
+
+    Parameters:
+    -----------
+    ds : xr.Dataset
+        Input dataset containing 'depth' coordinates.
+
+    Returns:
+    --------
+    xr.DataArray
+        Vertical thickness of each grid cell.
+    """
+    # -- Validate Input -- #
+    if not isinstance(ds, xr.Dataset):
+        raise TypeError("Input must be an xarray Dataset.")
+    if 'depth' not in ds.coords:
+        raise ValueError("Input dataset must contain 'depth' coordinates.")
+    depth = ds['depth'].data
+
+    # Check that depth is 1-dimensional:
+    if depth.ndim != 1:
+        raise ValueError("Input depth DataArray must be 1-dimensional.")
+
+    # Find interfaces between vertical levels:
+    interfaces = 0.5 * (depth[:-1] + depth[1:])
+    # Use sea surface as top boundary:
+    top = 0.0
+    # Extrapolate bottom boundary:
+    bottom = depth[-1] + (depth[-1] - interfaces[-1])
+    edges = np.concatenate([[top], interfaces, [bottom]])
+
+    # Define cell_thickness DataArray with CF-compliant metadata:
+    cell_thickness = xr.DataArray(
+        data=np.diff(edges),
+        dims=('depth',),
+        coords={'depth': depth},
+        name='cell_thickness',
+        attrs={
+            'long_name': 'Grid-Cell Thickness',
+            'standard_name': 'cell_thickness',
+            'units': 'm',
+        },
+    )
+
+    return cell_thickness
+
+def compute_land_sea_mask(
+        da: xr.DataArray,
+        ) -> xr.DataArray:
+    """
+    Calculate land-sea mask from a variable DataArray.
+    
+    The resulting mask is defined as follows:
+    * 1 -> ocean grid point
+    * 0 -> land grid point
+
+    Parameters:
+    -----------
+    da : xr.DataArray
+        Input variable DataArray containing NaN values on land points.
+
+    Returns:
+    --------
+    xr.DataArray
+        Land-sea mask.
+    """
+    # -- Validate Input -- #
+    if not isinstance(da, xr.DataArray):
+        raise TypeError("Input must be an xarray DataArray.")
+    if da.ndim != 2:
+        raise ValueError("Input DataArray must be 2-dimensional.")
+    
+    # -- Calculate Land-Sea Mask -- #
+    # Define land-sea mask:
+    mask = xr.where(np.isnan(da), 0, 1)
+
+    # Add CF-compliant metadata to the mask:
+    mask.attrs['long_name'] = "Land-Sea Binary Mask"
+    mask.attrs['standard_name'] = "sea_binary_mask"
+    mask.attrs['comment'] = " 1 = sea, 0 = land"
+
+    return mask
+
+
+def update_icechunk_global_attrs(
+        credentials_filepath: str,
+        bucket: str,
+        prefix: str,
+        attrs: dict,
+        commit_message: str,
+        branch: str='main',
+        region: str='us-east-1',
+        force_path_style: bool=True,
+) -> str:
+    """
+    Update global attributes of existing Icechunk store via a new
+    commit.
+
+    Expects Icechunk S3 storage at a custom endpoint (e.g., JASMIN OS).
+
+    Parameters:
+    -----------
+    credentials_filepath : str
+        Filepath to JSON file containing Icechunk S3 storage credentials.
+    bucket : str
+        Name of the S3 bucket where the Icechunk store is located.
+    prefix : str
+        Prefix (path) within the S3 bucket where the Icechunk store is located.
+    attrs : dict
+        Dictionary of global attributes to update in the root group of the Icechunk store.
+    commit_message : str
+        Commit message describing the update to the Icechunk store.
+    branch : str, optional
+        Branch of the Icechunk repository to update (default: 'main').
+    region : str, optional
+        AWS region where the S3 bucket is located (default: 'us-east-1').
+    force_path_style : bool, optional
+        Whether to force path-style access for S3 (default: True).
+
+    Returns:
+    --------
+    str
+        Snapshot ID of new commit.
+    """
+    # -- Validate Input -- #
+    if not isinstance(credentials_filepath, str):
+        raise TypeError("credentials_filepath must be a string.")
+    if not isinstance(bucket, str):
+        raise TypeError("bucket must be a string.")
+    if not isinstance(prefix, str):
+        raise TypeError("prefix must be a string.")
+    if not isinstance(attrs, dict):
+        raise TypeError("attributes must be a dictionary.")
+    if not isinstance(commit_message, str):
+        raise TypeError("commit_message must be a string.")
+    if not isinstance(branch, str):
+        raise TypeError("branch must be a string.")
+    if not isinstance(region, str):
+        raise TypeError("region must be a string.")
+    if not isinstance(force_path_style, bool):
+        raise TypeError("force_path_style must be a boolean.")
+
+    # -- Update Icechunk Global Attributes -- #
+    # Load Icechunk S3 storage credentials from JSON file:
+    store_credentials = json.load(open(credentials_filepath, 'r'))
+
+    # Define Icechunk storage:
+    storage = icechunk.s3_storage(
+    bucket=bucket,
+    prefix=prefix,
+    region=region,
+    access_key_id=store_credentials['token'],
+    secret_access_key=store_credentials['secret'],
+    endpoint_url=store_credentials['endpoint_url'],
+    force_path_style=force_path_style,
+    )
+
+    # Open Icechunk repository & start read-only session on main branch:
+    repo = icechunk.Repository.open(storage=storage)
+    print(f"Opened Icechunk repository at s3://{bucket}/{prefix} on branch '{branch}'")
+
+    # Open a writable session on root group:
+    session = repo.writable_session(branch=branch)
+    root = zarr.open_group(session.store)
+    # Update global attributes & commit changes to repo:
+    root.attrs.update(attrs)
+    print(f"Updated global attributes via new commit on branch '{branch}' with commit message -> '{commit_message}'")
+    
+    return session.commit(message=commit_message)
+
+
+def update_icechunk_variable_attrs(
+        credentials_filepath: str,
+        bucket: str,
+        prefix: str,
+        vars: list[str],
+        attrs: list[dict],
+        commit_message: str,
+        branch: str='main',
+        region: str='us-east-1',
+        force_path_style: bool=True,
+) -> str:
+    """
+    Update variable attributes of existing Icechunk store via a new
+    commit.
+
+    Expects Icechunk S3 storage at a custom endpoint (e.g., JASMIN OS).
+
+    Parameters:
+    -----------
+    credentials_filepath : str
+        Filepath to JSON file containing Icechunk S3 storage credentials.
+    bucket : str
+        Name of the S3 bucket where the Icechunk store is located.
+    prefix : str
+        Prefix (path) within the S3 bucket where the Icechunk store is located.
+    vars : list[str]
+        List of variable names whose attributes are to be updated.
+    attrs : list[dict]
+        List of dictionaries containing attributes to update for each variable.
+    commit_message : str
+        Commit message describing the update to the Icechunk store.
+    branch : str, optional
+        Branch of the Icechunk repository to update (default: 'main').
+    region : str, optional
+        AWS region where the S3 bucket is located (default: 'us-east-1').
+    force_path_style : bool, optional
+        Whether to force path-style access for S3 (default: True).
+
+    Returns:
+    --------
+    str
+        Snapshot ID of new commit.
+    """
+    # -- Validate Input -- #
+    if not isinstance(credentials_filepath, str):
+        raise TypeError("credentials_filepath must be a string.")
+    if not isinstance(bucket, str):
+        raise TypeError("bucket must be a string.")
+    if not isinstance(prefix, str):
+        raise TypeError("prefix must be a string.")
+    if not isinstance(vars, list):
+        raise TypeError("vars must be a list.")
+    if not isinstance(attrs, list):
+        raise TypeError("attributes must be a list.")
+    if not isinstance(commit_message, str):
+        raise TypeError("commit_message must be a string.")
+    if not isinstance(branch, str):
+        raise TypeError("branch must be a string.")
+    if not isinstance(region, str):
+        raise TypeError("region must be a string.")
+    if not isinstance(force_path_style, bool):
+        raise TypeError("force_path_style must be a boolean.")
+
+    # -- Update Icechunk Global Attributes -- #
+    # Load Icechunk S3 storage credentials from JSON file:
+    store_credentials = json.load(open(credentials_filepath, 'r'))
+
+    # Define Icechunk storage:
+    storage = icechunk.s3_storage(
+    bucket=bucket,
+    prefix=prefix,
+    region=region,
+    access_key_id=store_credentials['token'],
+    secret_access_key=store_credentials['secret'],
+    endpoint_url=store_credentials['endpoint_url'],
+    force_path_style=force_path_style,
+    )
+
+    # Open Icechunk repository & start read-only session on main branch:
+    repo = icechunk.Repository.open(storage=storage)
+    print(f"Opened Icechunk repository at s3://{bucket}/{prefix} on branch '{branch}'")
+
+    # Open a writable session on root group:
+    session = repo.writable_session(branch=branch)
+    root = zarr.open_group(session.store)
+    # Update variable attributes & commit changes to repo:
+    for var, attr in zip(vars, attrs):
+        root[var].attrs.update(attr)
+
+    print(f"Updated variable attributes via new commit on branch '{branch}' with commit message -> '{commit_message}'")
+    
+    return session.commit(message=commit_message)