1. Getting Started with OceanDataStore

NOC Near-Present Day Hackathon - Day 1¶

Tutorial 1. Getting Started with OceanDataStore¶

Contact:¶

Ollie Tooth (oliver.tooth@noc.ac.uk)

Background:¶

OceanDataStore is a Python library designed to streamline writing and accessing ocean model and observational data stored in the cloud.

In this first hands-on tutorial, we will demonstrate how to use the OceanDataCatalog API to explore the Near-Present-Day global ocean sea-ice simulations developed by the National Oceanography Centre as part of the Atlantic Climate and Environment Strategic Science (AtlantiS) programme.

We will cover:

How to use the OceanDataCatalog to explore Spatio-Temporal Access Catalogs (STAC) exposing available collections of ocean model & observational data stored in the JASMIN Object Store.
How to search the OceanDataCatalog by collection, variable name, standard name or platform.
How to open and subset Analysis-Ready Cloud-Optimised (ARCO) datasets as lazy xarray Datasets.

1.1 Basics¶

Let's begin by importing the OceanDataCatalog class from the OceanDataStore Python library:

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import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt

from OceanDataStore import OceanDataCatalog
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt

from OceanDataStore import OceanDataCatalog

Next, we need to create an instance of the OceanDataCatalog class to access the NOC ocean modelling Spatio-Temporal Access Catalog (STAC) named noc-stac:

Note that we have also provided a local copy of the noc-stac on both NOC Data Science Platforms at /groups/npd-workshop/noc-stac

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# Initialising the OceanDataCatalog using the `noc-stac` stored in the JASMIN cloud object store:
catalog = OceanDataCatalog(catalog_name="noc-stac")
# Initialising the OceanDataCatalog using the `noc-stac` stored in the JASMIN cloud object store:
catalog = OceanDataCatalog(catalog_name="noc-stac")

We can use the available_collections property to return the names (IDs) of all available dataset collections in the noc-stac:

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catalog.available_collections
catalog.available_collections

Out[3]:

['noc-rapid-evolution', 'noc-npd-jra55', 'noc-npd-era5']

Above, we can see three available collections corresponding to:
- Nested 1/4° (nominal horizontal resolution) global forced ocean sea-ice simulation performed for the CCROC RAPID-Evolution project.
- 1° & 1/4° global ocean sea-ice simulations of the NOC Near-Present Day JRA55-do collection (1976-2023).
- 1°, 1/4° & 1/12 ° global ocean sea ice simulation of the NOC Near-Present Day ERA-5 collection (1976-present).

Throughout the hackathon, we'll be focusing our analysis on the latest Near-Present Day ERA-5 collection since this will be continually updated to the near-present.
Now, let's use the .search() method to search the Near-Present Day ERA-5 collection for all ocean model outputs including the sea surface temperature (SST) standard variable name.

search()

We can search the root Catalog using any combination of the following parameters:
- collection : Activity Collection name (e.g., noc-npd-era5).
- platform : Platform Catalog name (e.g., gn).
- variable_name : Variable name contained in Item Asset (e.g., tos_con).
- standard_name : Standard variable name contained in Item Asset (e.g., sea_surface_temperature).
- item_name : Substring to filter Item IDs (e.g., domain).

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catalog.search(collection='noc-npd-era5', standard_name='sea_surface_temperature')
catalog.search(collection='noc-npd-era5', standard_name='sea_surface_temperature')

            * Item ID: noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y
              Title: T1y Icechunk repository
              Description: **Annual mean global ocean physics outputs defined at NEMO model T-points.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y')`
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1m
              Title: T1m Icechunk repository
              Description: **Monthly mean global ocean physics outputs defined at NEMO model T-points.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1m')`
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T1y_3d
              Title: T1y_3d Icechunk repository
              Description: **Annual mean global ocean physics outputs defined at NEMO model T-points.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T1y_3d')`
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T1m_3d
              Title: T1m_3d Icechunk repository
              Description: **Monthly mean global ocean physics outputs defined at NEMO model T-points.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T1m_3d')`
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T5d_3d
              Title: T5d_3d Icechunk repository
              Description: **5-day mean global ocean physics outputs defined at NEMO model T-points.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T5d_3d')`
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/T1y_3d
              Title: T1y_3d Icechunk repository
              Description: **Annual mean global ocean physics outputs defined at NEMO model T-points.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/T1y_3d')`
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-06-30T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/T1m_3d
              Title: T1m_3d Icechunk repository
              Description: **Monthly mean global ocean physics outputs defined at NEMO model T-points.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/T1m_3d')`
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-06-30T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/T5d_3d
              Title: T5d_3d Icechunk repository
              Description: **5-day mean global ocean physics outputs defined at NEMO model T-points.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/T5d_3d')`
              Platform: gn
              Start Date: 1990-01-01T00:00:00Z
              End Date: 2025-06-30T00:00:00Z

The search operation returns a summary of every STAC Item which meets the criteria of our search, in this case containing a variable with the CF-compliant standard name sea_surface_temperature.
Once we have performed a search operation on our catalog, we can also use the available_items property to return the names (IDs) of the available STAC Items resulting from our search.

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catalog.available_items
catalog.available_items

Out[5]:

['noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y',
 'noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1m',
 'noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T1y_3d',
 'noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T1m_3d',
 'noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T5d_3d',
 'noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/T1y_3d',
 'noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/T1m_3d',
 'noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/T5d_3d']

Note that each time we perform a search the available_items property of our catalog will be updated to reflect the results of our latest search.

Now, let's take a closer look at the first Item in our search results. This corresponds to the annual-mean T-grid variables (T1y) output by the 1° NPD eORCA1 ERA5v1 simulation (npd-eorca1-era5v1) stored on their native NEMO model grid (gn).
By looking under the properties/variable_standard_names tab, we can see that sea_surface_temperature is the 64th variable in this dataset and is named tos_con (see properties/variables tab).

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catalog.Items[0]
catalog.Items[0]

Out[6]:

type "Feature"
stac_version "1.1.0"
stac_extensions[] 0 items
id "noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y"
geometry
- type "Polygon"
- coordinates[] 1 items
  - 0[] 5 items
    
    0[] 2 items
    
    0 -180.0
    
    1 -90.0
    
    1[] 2 items
    
    0 180.0
    
    1 -90.0
    
    2[] 2 items
    
    0 180.0
    
    1 90.0
    
    3[] 2 items
    
    0 -180.0
    
    1 90.0
    
    4[] 2 items
    
    0 -180.0
    
    1 -90.0
bbox[] 4 items
- 0 -180.0
- 1 -90.0
- 2 180.0
- 3 90.0
properties
- title "T1y Icechunk repository"
- description "**Annual mean global ocean physics outputs defined at NEMO model T-points.** **OceanDataCatalog Access:** `catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y')`"
- variant "r1i1c1f1"
- platform "gn"
- variables[] 74 items
  - 0 "empmr"
  - 1 "e3t"
  - 2 "hfempds"
  - 3 "hfds"
  - 4 "fsitherm"
  - 5 "hflx_rnf"
  - 6 "hfns"
  - 7 "hfrainds"
  - 8 "hfls"
  - 9 "ficeberg"
  - 10 "friver"
  - 11 "berg_latent_heat_flux"
  - 12 "mlotst"
  - 13 "hfss"
  - 14 "hfto"
  - 15 "hfsr"
  - 16 "hfevapds"
  - 17 "deptht_bounds"
  - 18 "mlotstmax"
  - 19 "evs"
  - 20 "mlotstsq"
  - 21 "ocontempdiff"
  - 22 "osaltpmdiff"
  - 23 "osaltdiff"
  - 24 "mlotstmin"
  - 25 "ocontempmint"
  - 26 "rlntds"
  - 27 "ocontempadvect"
  - 28 "osalttend"
  - 29 "rsdo"
  - 30 "prsn"
  - 31 "osaltadvect"
  - 32 "ocontemptend"
  - 33 "pbo"
  - 34 "sbt_con"
  - 35 "snow_ai_cea"
  - 36 "sbs_abs"
  - 37 "rsntds"
  - 38 "sfdsi"
  - 39 "so_abs"
  - 40 "ocontemppmdiff"
  - 41 "sohfcisf"
  - 42 "somixhgt"
  - 43 "sos_abs"
  - 44 "sosafldo"
  - 45 "soicecov"
  - 46 "somint_abs"
  - 47 "sohflisf"
  - 48 "rsdoabsorb"
  - 49 "snowpre"
  - 50 "strd_atf_li"
  - 51 "sowaflup"
  - 52 "sowflisf"
  - 53 "strd_bbl_li"
  - 54 "thetao_con"
  - 55 "somxl010"
  - 56 "time_centered_bounds"
  - 57 "strd_evd_li"
  - 58 "somxzint1"
  - 59 "ttrd_evd_li"
  - 60 "ttrd_atf_li"
  - 61 "sowindsp"
  - 62 "time_counter_bounds"
  - 63 "tos_con"
  - 64 "tossq_con"
  - 65 "tnpeo"
  - 66 "ttrd_bbl_li"
  - 67 "sossq_abs"
  - 68 "ttrd_qns_li"
  - 69 "zossq"
  - 70 "vohflisf"
  - 71 "zos"
  - 72 "vowflisf"
  - 73 "vohfcisf"
- variable_standard_names[] 74 items
  - 0 "water_flux_out_of_sea_ice_and_sea_water"
  - 1 "cell_thickness"
  - 2 "ocean_surface_downward_heat_flux_from_E-P"
  - 3 "ocean_surface_downward_total_heat_flux"
  - 4 "fsitherm"
  - 5 "temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water"
  - 6 "surface_net_downward_non_solar_heat_flux"
  - 7 "temperature_flux_due_to_rain_expressed_as_heat_flux_into_sea_water"
  - 8 "ocean_surface_downward_latent_heat_flux"
  - 9 "water_flux_into_sea_water_from_icebergs"
  - 10 "water_flux_into_sea_water_from_rivers"
  - 11 "latent_heat_flux_from_icebergs"
  - 12 "ocean_mixed_layer_thickness_defined_by_sigma_theta"
  - 13 "ocean_surface_downward_sensible_heat_flux"
  - 14 "surface_net_downward_total_heat_flux"
  - 15 "surface_net_downward_solar_heat_flux"
  - 16 "temperature_flux_due_to_evaporation_expressed_as_heat_flux_out_of_sea_water"
  - 17 "deptht_bounds"
  - 18 "ocean_mixed_layer_thickness_defined_by_sigma_theta"
  - 19 "water_evaporation_flux"
  - 20 "square_of_ocean_mixed_layer_thickness_defined_by_sigma_theta"
  - 21 "ocontempdiff"
  - 22 "osaltpmdiff"
  - 23 "osaltdiff"
  - 24 "ocean_mixed_layer_thickness_defined_by_sigma_theta"
  - 25 "integral_wrt_depth_of_product_of_density_and_conservative_temperature"
  - 26 "ocean_surface_net_downward_longwave_heat_flux"
  - 27 "ocontempadvect"
  - 28 "osalttend"
  - 29 "downwelling_shortwave_flux_in_sea_water"
  - 30 "snowfall_flux"
  - 31 "osaltadvect"
  - 32 "ocontemptend"
  - 33 "sea_water_pressure_at_sea_floor"
  - 34 "sbt_con"
  - 35 "snowfall_flux"
  - 36 "sbs_abs"
  - 37 "ocean_surface_net_downward_shortwave_heat_flux"
  - 38 "downward_sea_ice_basal_salt_flux"
  - 39 "sea_water_absolute_salinity"
  - 40 "ocontemppmdiff"
  - 41 ""
  - 42 "ocean_mixed_layer_thickness_defined_by_vertical_tracer_diffusivity"
  - 43 "sea_surface_salinity"
  - 44 "salt_flux_into_sea_water"
  - 45 "sea_ice_area_fraction"
  - 46 "integral_wrt_depth_of_product_of_density_and_absolute_salinity"
  - 47 ""
  - 48 "rsdoabsorb"
  - 49 "snowfall_flux"
  - 50 "strd_atf_li"
  - 51 "water_flux_out_of_sea_ice_and_sea_water"
  - 52 ""
  - 53 "strd_bbl_li"
  - 54 "sea_water_conservative_temperature"
  - 55 "ocean_mixed_layer_thickness_defined_by_sigma_theta"
  - 56 "time_centered_bounds"
  - 57 "strd_evd_li"
  - 58 "ocean_mixed_layer_thickness_defined_by_sigma_theta"
  - 59 "ttrd_evd_li"
  - 60 "ttrd_atf_li"
  - 61 "wind_speed"
  - 62 "time_counter_bounds"
  - 63 "sea_surface_temperature"
  - 64 "square_of_sea_surface_temperature"
  - 65 "tnpeo"
  - 66 "ttrd_bbl_li"
  - 67 "square_of_sea_surface_Salinity"
  - 68 "ttrd_qns_li"
  - 69 "square_of_sea_surface_height_above_geoid"
  - 70 ""
  - 71 "sea_surface_height_above_geoid"
  - 72 ""
  - 73 ""
- dimensions[] 5 items
  - 0 "time_counter"
  - 1 "y"
  - 2 "x"
  - 3 "deptht"
  - 4 "axis_nbounds"
- operation "mean"
- operation_frequency "annual"
- ocean_component "NEMO v4.2.2"
- sea_ice_component "SI3 v4.0"
- biogeochemistry_component None
- atmosphere_component None
- status "ongoing"
- update_frequency "quarterly"
- latest_data_update "2026-03-22T21:41:49.874907"
- start_datetime "1976-01-01T00:00:00Z"
- end_datetime "2025-07-31T00:00:00Z"
- datetime "2000-01-01T00:00:00Z"
links[] 3 items
- 0
  - rel "root"
  - href "https://noc-msm-o.s3-ext.jc.rl.ac.uk/oceandatastore/noc-stac/catalog.json"
  - type "application/json"
  - title "NOC STAC Catalog"
- 1
  - rel "self"
  - href "https://noc-msm-o.s3-ext.jc.rl.ac.uk/oceandatastore/noc-stac/noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y/noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y.json"
  - type "application/json"
- 2
  - rel "parent"
  - href "https://noc-msm-o.s3-ext.jc.rl.ac.uk/oceandatastore/noc-stac/noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/catalog.json"
  - type "application/json"
  - title "eORCA1 ERA5v1 NPD: Global Native Model Grid Catalog"
assets
- T1y
  - href "https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-era5v1/T1y"
  - type "application/vnd.zarr+icechunk"
  - title "eORCA1 ERA5v1 NPD: T1y Icechunk repository"
  - description "**Annual mean global ocean physics outputs defined at NEMO model T-points.** **OceanDataCatalog Access:** `catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y')`"
  - endpoint_url "https://noc-msm-o.s3-ext.jc.rl.ac.uk"
  - bucket "npd-eorca1-era5v1"
  - prefix "T1y"
  - variant "r1i1c1f1"
  - anonymous True
collection "noc-npd-era5"

Next, let's open a subset (1980-1990) of the annual-mean SST data as an xarray.Dataset by using the .open_dataset() method.

open_dataset()

We can open the Asset associated with a given STAC Items ID as a lazy xarray.Dataset by passing the following arguments to the .open_dataset() method:
- id : Identifier of STAC Items (e.g., noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y).
- variable_names : List of variable names to be parsed from the dataset (e.g., ['tos_con', 'sos_abs']). Default is to return all variables.
- start_datetime : Start datetime used to subset the dataset. Should be a string in ISO format (e.g., "1976-01-01T00:00:00Z"). Default is to use the Items start_datetime.
- end_datetime : End datetime used to subset the dataset. Should be a string in ISO format (e.g., "2024-12-31T00:00:00Z"). Default is to use the Items end_datetime.
- bbox : Spatial bounding box used to subset the dataset. Should be a list of four floats representing the bounding box in the format: (min_lon, min_lat, max_lon, max_lat). Default is to use the Items bbox.

Here, we use the .id attribute of the first Items returned by our latest search and pass datetime strings (in the format 'YYYY-MM') to the start_datetime and end_datetime arguments.

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ds = catalog.open_dataset(id=catalog.Items[0].id,
                          start_datetime='1980-01',
                          end_datetime='1990-12',
                          )

ds
ds = catalog.open_dataset(id=catalog.Items[0].id,
                          start_datetime='1980-01',
                          end_datetime='1990-12',
                          )

ds

Out[7]:

<xarray.Dataset> Size: 9GB
Dimensions:                (deptht: 75, axis_nbounds: 2, time_counter: 11,
                            y: 331, x: 360)
Coordinates:
  * deptht                 (deptht) float32 300B 0.5058 1.556 ... 5.902e+03
  * time_counter           (time_counter) datetime64[ns] 88B 1980-07-02 ... 1...
    time_centered          (time_counter) datetime64[ns] 88B dask.array<chunksize=(1,), meta=np.ndarray>
    nav_lat                (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    nav_lon                (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
Dimensions without coordinates: axis_nbounds, y, x
Data variables: (12/74)
    deptht_bounds          (deptht, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>
    e3t                    (time_counter, deptht, y, x) float32 393MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
    evs                    (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    ficeberg               (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    hfds                   (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    friver                 (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    ...                     ...
    ttrd_bbl_li            (time_counter, deptht, y, x) float32 393MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
    zos                    (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    zossq                  (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    vohflisf               (time_counter, deptht, y, x) float32 393MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
    strd_atf_li            (time_counter, deptht, y, x) float32 393MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
    vowflisf               (time_counter, deptht, y, x) float32 393MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

The resulting xarray.Dataset is lazy, meaning that we have not yet downloaded any data (besided the metadata shown) from the JASMIN cloud object store.

As a quick sense check, let's create a plot of the time-mean (1980-1990) SST for the global ocean.
To do this, we use .mean(dim='time_counter') to tell xarray to compute the average over the entire time-dimension and .plot(cmap='RdBu_r') to create a colormesh plot using a basic Blue-Red colormap.

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ds['tos_con'].mean(dim='time_counter').plot(cmap='RdBu_r', vmin=-2, vmax=30, shading='auto')
ds['tos_con'].mean(dim='time_counter').plot(cmap='RdBu_r', vmin=-2, vmax=30, shading='auto')

Out[8]:

<matplotlib.collections.QuadMesh at 0x1493f5b1f4d0>

No description has been provided for this image

Notice that the sea surface temperature field is plotted as a function of the grid indexes y and x rather than latitude and longitude; this is because NEMO uses curvilinear rather than regular grids.
Let's create a geographical plot of the same sea surface temperature field:

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# Define figure and axes using Robinson projection:
fig, ax = plt.subplots(
    figsize=(8, 5),
    subplot_kw={"projection": ccrs.Robinson()}
)

# Plot time-mean sea surface temperature:
mesh = ax.pcolormesh(
    ds['nav_lon'],
    ds['nav_lat'],
    ds['tos_con'].mean(dim='time_counter'),
    transform=ccrs.PlateCarree(central_longitude=-1.0), # Add a small offset to prevent striping.
    shading="auto",
    cmap="RdBu_r",
    zorder=0,
)

# Add land & coastlines features:
ax.coastlines(resolution="110m", linewidth=1)
ax.add_feature(cfeature.LAND, facecolor="white", zorder=1)

# Add colorbar:
cbar = plt.colorbar(mesh, ax=ax, orientation="vertical", pad=0.02, shrink=0.6)
cbar.set_label("Sea Surface Temperature (°C)")

plt.tight_layout()
plt.show()
# Define figure and axes using Robinson projection:
fig, ax = plt.subplots(
    figsize=(8, 5),
    subplot_kw={"projection": ccrs.Robinson()}
)

# Plot time-mean sea surface temperature:
mesh = ax.pcolormesh(
    ds['nav_lon'],
    ds['nav_lat'],
    ds['tos_con'].mean(dim='time_counter'),
    transform=ccrs.PlateCarree(central_longitude=-1.0), # Add a small offset to prevent striping.
    shading="auto",
    cmap="RdBu_r",
    zorder=0,
)

# Add land & coastlines features:
ax.coastlines(resolution="110m", linewidth=1)
ax.add_feature(cfeature.LAND, facecolor="white", zorder=1)

# Add colorbar:
cbar = plt.colorbar(mesh, ax=ax, orientation="vertical", pad=0.02, shrink=0.6)
cbar.set_label("Sea Surface Temperature (°C)")

plt.tight_layout()
plt.show()

1.2 Ocean Model Domain Variables¶

So far, we have seen how to access NPD ocean model variables (e.g., temperature, salinity and velocities) defined on their native NEMO model grid.
But often when calculating diagnostics derived from these variables, such as volume transports and spatial-averages, we also need to access the variables describing the ocean model domain.
Let's next search the OceanDataCatalog for any Items which contains "domain_cfg" in the Item ID:

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catalog.search(collection='noc-npd-era5', item_name='domain_cfg')
catalog.search(collection='noc-npd-era5', item_name='domain_cfg')

            * Item ID: noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/domain/domain_cfg
              Title: domain/domain_cfg Icechunk repository
              Description: **Global ocean model domain and mesh mask variables.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/domain/domain_cfg')`
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/domain/domain_cfg
              Title: domain/domain_cfg Icechunk repository
              Description: **Global ocean model domain and mesh mask variables.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/domain/domain_cfg')`
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/domain/domain_cfg
              Title: domain/domain_cfg Icechunk repository
              Description: **Global ocean model domain and mesh mask variables.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/domain/domain_cfg')`
              Platform: gn
              Start Date: 1990-01-01T00:00:00Z
              End Date: 2025-06-30T00:00:00Z

We can see from the list of search Items that domain_cfg ancillary datasets are available for each NPD model configuration.
Next, let's open the domain_cfg file for the 1° NPD eORCA1 ERA5v1 simulation we explored earlier.

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ds_domain_cfg = catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/domain/domain_cfg')

ds_domain_cfg
ds_domain_cfg = catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/domain/domain_cfg')

ds_domain_cfg

Out[11]:

<xarray.Dataset> Size: 710MB
Dimensions:       (y: 331, x: 360, nav_lev: 75)
Coordinates:
  * y             (y) int64 3kB 0 1 2 3 4 5 6 7 ... 324 325 326 327 328 329 330
  * x             (x) int64 3kB 0 1 2 3 4 5 6 7 ... 353 354 355 356 357 358 359
  * nav_lev       (nav_lev) int64 600B 0 1 2 3 4 5 6 7 ... 68 69 70 71 72 73 74
Data variables: (12/49)
    atlmsk        (y, x) float32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    e1f           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    e1u           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    e2f           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    e1t           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    bottom_level  (y, x) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    ...            ...
    socmsk        (y, x) float32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    top_level     (y, x) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    tmask         (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>
    pacmsk        (y, x) float32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    vmaskutil     (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    wmask         (nav_lev, y, x) bool 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>
Attributes:
    CfgName:    UNKNOWN
    CfgIndex:   -999
    Iperio:     1
    Jperio:     0
    NFold:      1
    NFtype:     F
    VertCoord:  zps
    IsfCav:     0
    file_name:  mesh_mask.nc
    TimeStamp:  01/03/2025 22:19:49 -0000

1.3 Pre-Calculated Diagnostics¶

In addition to variables output by our NEMO model, we can also access a range of pre-calculated diagnostics, such as the meridional overturning, heat and freshwater transports across the following trans-basin sections:
- Overturning in the Subpolar North Atlantic Program (OSNAP) array
- Rapid Climate Change-Meridional Overturning Circulation and Heatflux Array (RAPID-MOCHA) at 26.5°N
- Meridional Overturning Variability Experiment (MOVE) array at 16°N
- South Atlantic Meridional overturning circulation Basin-wide Array (SAMBA) array at 34.5°S
The OSNAP section diagnostics are extracted using NEMO Cookbook, we shall revisit this in Tutorial 2.
The remaining diagnostics are calculated using the Meridional ovErTurning ciRculation diagnostIC (METRIC) package.
Let's see how we can access these diagnostics by searching the OceanDataCatalog for any Items with identifiers containing the key word "tn", which corresponds to transects which are defined on the native NEMO model grid:

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catalog.search(collection='noc-npd-era5', item_name='tn')
catalog.search(collection='noc-npd-era5', item_name='tn')

            * Item ID: noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/MOVE_16N
              Title: M1m/MOVE_16N Icechunk repository
              Description: **Monthly mean ocean physics transect outputs defined at MOVE_16N.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/MOVE_16N')`
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/SAMBA_34_5S
              Title: M1m/SAMBA_34_5S Icechunk repository
              Description: **Monthly mean global ocean scalar outputs.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/SAMBA_34_5S')`
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/RAPID_26N
              Title: M1m/RAPID_26N Icechunk repository
              Description: **Monthly mean global sea-ice outputs defined at NEMO model T-points.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/RAPID_26N')`
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/OSNAP
              Title: M1m/OSNAP Icechunk repository
              Description: **Monthly mean global ocean scalar outputs.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/OSNAP')`
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/tn/M1m/MOVE_16N
              Title: M1m/MOVE_16N Icechunk repository
              Description: **Monthly mean ocean physics transect outputs defined at MOVE_16N.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/tn/M1m/MOVE_16N')`
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/tn/M1m/SAMBA_34_5S
              Title: M1m/SAMBA_34_5S Icechunk repository
              Description: **Monthly mean global ocean scalar outputs.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/tn/M1m/SAMBA_34_5S')`
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/tn/M1m/RAPID_26N
              Title: M1m/RAPID_26N Icechunk repository
              Description: **Monthly mean global sea-ice outputs defined at NEMO model T-points.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/tn/M1m/RAPID_26N')`
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/tn/M1m/OSNAP
              Title: M1m/OSNAP Icechunk repository
              Description: **Monthly mean global ocean scalar outputs.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/tn/M1m/OSNAP')`
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2025-07-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/tn/M1m/MOVE_16N
              Title: M1m/MOVE_16N Icechunk repository
              Description: **Monthly mean ocean physics transect outputs defined at MOVE_16N.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/tn/M1m/MOVE_16N')`
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/tn/M1m/SAMBA_34_5S
              Title: M1m/SAMBA_34_5S Icechunk repository
              Description: **Monthly mean global ocean scalar outputs.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/tn/M1m/SAMBA_34_5S')`
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/tn/M1m/RAPID_26N
              Title: M1m/RAPID_26N Icechunk repository
              Description: **Monthly mean global sea-ice outputs defined at NEMO model T-points.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/tn/M1m/RAPID_26N')`
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/tn/M1m/OSNAP
              Title: M1m/OSNAP Icechunk repository
              Description: **Monthly mean global ocean scalar outputs.**

**OceanDataCatalog Access:**
`catalog.open_dataset(id='noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/tn/M1m/OSNAP')`
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z

We can see from the list of search Items that OSNAP, RAPID_26N, MOVE_16N and SAMBA_34_5S diagnostics are available for all NPD model configurations.
Next, let's open the monthly-mean RAPID_26N diagnostics for the 1° NPD eORCA1 ERA5v1 simulation.

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ds_rapid = catalog.open_dataset(id="noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/RAPID_26N")

ds_rapid
ds_rapid = catalog.open_dataset(id="noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/RAPID_26N")

ds_rapid

Out[13]:

<xarray.Dataset> Size: 158MB
Dimensions:        (time: 595, z: 75, xbounds: 72, x: 71)
Coordinates:
  * time           (time) datetime64[ns] 5kB 1976-01-16T12:00:00 ... 2025-07-...
  * z              (z) float64 600B 0.5058 1.556 2.668 ... 5.698e+03 5.902e+03
  * xbounds        (xbounds) float64 576B -81.0 -80.0 -79.0 ... -11.0 -9.998
  * x              (x) float64 568B -80.5 -79.5 -78.5 ... -12.5 -11.5 -10.5
Data variables: (12/62)
    dh             (time, z, xbounds) float64 26MB dask.array<chunksize=(1, 75, 72), meta=np.ndarray>
    dz             (z) float64 600B dask.array<chunksize=(75,), meta=np.ndarray>
    fc             (time) float64 5kB dask.array<chunksize=(298,), meta=np.ndarray>
    ekman          (time) float64 5kB dask.array<chunksize=(298,), meta=np.ndarray>
    dx             (x) float64 568B dask.array<chunksize=(71,), meta=np.ndarray>
    fw_geoint      (time) float64 5kB dask.array<chunksize=(298,), meta=np.ndarray>
    ...             ...
    v              (time, z, x) float64 25MB dask.array<chunksize=(1, 75, 71), meta=np.ndarray>
    v_fc           (time, z) float64 357kB dask.array<chunksize=(1, 75), meta=np.ndarray>
    v_basin_model  (time, z) float64 357kB dask.array<chunksize=(1, 75), meta=np.ndarray>
    wbw            (time) float64 5kB dask.array<chunksize=(298,), meta=np.ndarray>
    v_basin_rapid  (time, z) float64 357kB dask.array<chunksize=(1, 75), meta=np.ndarray>
    vgeo           (time, z, x) float64 25MB dask.array<chunksize=(1, 75, 71), meta=np.ndarray>

The monthly-mean RAPID_26N diagnostics include the strength of the Atlantic Meridional Overturning Circulation (AMOC) at 26.5N evaluated directly from the model velocity fields (moc_model) and using an observational-equivalent method, including an end-point geostrophic calculation for the basin interior (moc_rapid).
Finally, let's plot the time series of the monthly mean AMOC (vertical overturning) strength at 26.5N as simulated in the 1° NPD eORCA1 ERA5v1 simulation.

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plt.figure(figsize=(8, 4))
plt.grid(True, linewidth=1, linestyle='--', alpha=0.5)

# Plot monthly-mean observational-equivalent AMOC strength time series:
ds_rapid['moc_rapid'].plot(linewidth=2)
# Plot 12-month rolling mean:
ds_rapid['moc_rapid'].rolling(time=12).mean().plot(linewidth=2)

# Add axes labels:
plt.xlabel("Time", fontsize=12, fontweight='bold')
plt.ylabel("AMOC Strength (Sv)", fontsize=12, fontweight='bold')
plt.figure(figsize=(8, 4))
plt.grid(True, linewidth=1, linestyle='--', alpha=0.5)

# Plot monthly-mean observational-equivalent AMOC strength time series:
ds_rapid['moc_rapid'].plot(linewidth=2)
# Plot 12-month rolling mean:
ds_rapid['moc_rapid'].rolling(time=12).mean().plot(linewidth=2)

# Add axes labels:
plt.xlabel("Time", fontsize=12, fontweight='bold')
plt.ylabel("AMOC Strength (Sv)", fontsize=12, fontweight='bold')

Out[14]:

Text(0, 0.5, 'AMOC Strength (Sv)')

1.4 Exercises¶

Now we have seen how to use the OceanDataCatalog to explore & access available collections of NOC NPD data stored in the JASMIN Object Store, let's put this into practice with two exercises demonstrating how to use OceanDataCatalog in typical ocean analysis workflows.

Exercise 1.¶

Plot a time series of the annual area-weighted mean sea surface temperature in the Atlantic Ocean in the 1/4° NPD eORCA025 ERA5v1 simulation. (Hint: To do this, you will need to open datasets for both the monthly mean sea surface temperature data and model domain).

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# Write your solution here...
# Write your solution here...

Exercise 2.¶

Plot a time series of March sea ice area in the Northern Hemisphere in the 1/4° NPD eORCA025 ERA5v1 simulation. (Hint: To do this, you will need to calculate the horizontal area of each NEMO model grid cell located at T-points using areacello = e1t * e2t).
Extension: How do these plots compare to the coarser 1° NPD eORCA1 and finer 1/12° NPD eORCA12 ERA5v1 simulations?

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# Write your solution here...
# Write your solution here...

Solution: Exercise 1.¶

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# -- Opening datasets for eORCA025 ERA5v1 -- #
# Opening domain_cfg data for eORCA025 ERA5v1:
ds_domain_cfg = catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/domain/domain_cfg')

# Opening Sea Surface Temperature (SST; tos_con) data for eORCA025 ERA5v1:
ds_tos_con = catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T1y_3d',
                                  variable_names=['tos_con'],
                                  )
ds_tos_con
# -- Opening datasets for eORCA025 ERA5v1 -- #
# Opening domain_cfg data for eORCA025 ERA5v1:
ds_domain_cfg = catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/domain/domain_cfg')

# Opening Sea Surface Temperature (SST; tos_con) data for eORCA025 ERA5v1:
ds_tos_con = catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T1y_3d',
                                  variable_names=['tos_con'],
                                  )
ds_tos_con

Out[18]:

<xarray.Dataset> Size: 368MB
Dimensions:        (time_counter: 49, y: 1206, x: 1440)
Coordinates:
  * time_counter   (time_counter) datetime64[ns] 392B 1976-07-02 ... 2024-07-02
    time_centered  (time_counter) datetime64[ns] 392B dask.array<chunksize=(1,), meta=np.ndarray>
    nav_lat        (y, x) float64 14MB dask.array<chunksize=(1206, 1440), meta=np.ndarray>
    nav_lon        (y, x) float64 14MB dask.array<chunksize=(1206, 1440), meta=np.ndarray>
Dimensions without coordinates: y, x
Data variables:
    tos_con        (time_counter, y, x) float32 340MB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>

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# -- Check Atlantic Ocean mask -- #
ds_tos_con['tos_con'].mean(dim='time_counter').where(ds_domain_cfg['atlmsk']).plot(cmap='RdBu_r')
# -- Check Atlantic Ocean mask -- #
ds_tos_con['tos_con'].mean(dim='time_counter').where(ds_domain_cfg['atlmsk']).plot(cmap='RdBu_r')

Out[19]:

<matplotlib.collections.QuadMesh at 0x14939ce4d950>

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# -- Plotting the annual area-weighted mean Sea Surface Temperature (SST) -- #
# Calculate the area of each grid cell using the e1t and e2t variables (grid cell scale factors).
areacello = ds_domain_cfg['e1t'] * ds_domain_cfg['e2t']

(ds_tos_con['tos_con']
 # Apply Atlantic Ocean mask.
 .where(ds_domain_cfg['atlmsk'])
 # Apply area-weighting.
 .weighted(areacello)
 # Compute the mean over x and y dimensions.
 .mean(dim=['x', 'y'])
 # Plot the resulting time series.
 .plot(lw=2)
 )
# -- Plotting the annual area-weighted mean Sea Surface Temperature (SST) -- #
# Calculate the area of each grid cell using the e1t and e2t variables (grid cell scale factors).
areacello = ds_domain_cfg['e1t'] * ds_domain_cfg['e2t']

(ds_tos_con['tos_con']
 # Apply Atlantic Ocean mask.
 .where(ds_domain_cfg['atlmsk'])
 # Apply area-weighting.
 .weighted(areacello)
 # Compute the mean over x and y dimensions.
 .mean(dim=['x', 'y'])
 # Plot the resulting time series.
 .plot(lw=2)
 )

Out[20]:

[<matplotlib.lines.Line2D at 0x149381548f50>]

Solution: Exercise 2.¶

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# -- Opening datasets for eORCA025 ERA5v1 -- #
# Opening domain_cfg data for eORCA025 ERA5v1:
ds_domain_cfg = catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/domain/domain_cfg')

# Opening Sea Ice Concentration (SIC; siconc) data for eORCA025 ERA5v1:
ds_siconc = catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/I1m_3d',
                                  variable_names=['siconc'],
                                  )
ds_siconc
# -- Opening datasets for eORCA025 ERA5v1 -- #
# Opening domain_cfg data for eORCA025 ERA5v1:
ds_domain_cfg = catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/domain/domain_cfg')

# Opening Sea Ice Concentration (SIC; siconc) data for eORCA025 ERA5v1:
ds_siconc = catalog.open_dataset(id='noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/I1m_3d',
                                  variable_names=['siconc'],
                                  )
ds_siconc

Out[21]:

<xarray.Dataset> Size: 4GB
Dimensions:        (time_counter: 595, y: 1206, x: 1440)
Coordinates:
  * time_counter   (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:00 ... ...
    time_centered  (time_counter) datetime64[ns] 5kB dask.array<chunksize=(1,), meta=np.ndarray>
    nav_lat        (y, x) float64 14MB dask.array<chunksize=(1206, 1440), meta=np.ndarray>
    nav_lon        (y, x) float64 14MB dask.array<chunksize=(1206, 1440), meta=np.ndarray>
Dimensions without coordinates: y, x
Data variables:
    siconc         (time_counter, y, x) float32 4GB dask.array<chunksize=(1, 1206, 1440), meta=np.ndarray>

In [22]:

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# -- Define Northern Hemisphere mask -- #
# Define mask for Northern Hemisphere T-points using the gphit (latitude) variable.
NH_mask = ds_domain_cfg['gphit'] > 0
ds_siconc['siconc'].isel(time_counter=0).where(NH_mask).plot(cmap='Blues_r')
# -- Define Northern Hemisphere mask -- #
# Define mask for Northern Hemisphere T-points using the gphit (latitude) variable.
NH_mask = ds_domain_cfg['gphit'] > 0
ds_siconc['siconc'].isel(time_counter=0).where(NH_mask).plot(cmap='Blues_r')

Out[22]:

<matplotlib.collections.QuadMesh at 0x1493814e6210>

In [23]:

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# -- Plotting the March Northern Hemisphere Sea Ice Area -- #
# Calculate the area of each grid cell using the e1t and e2t variables (grid cell scale factors).
areacello = ds_domain_cfg['e1t'] * ds_domain_cfg['e2t']

(ds_siconc['siconc']
 # Select only March sea ice concentration data.
 .sel(time_counter=ds_siconc['time_counter'].dt.month.isin([3]))
 # Apply Northern Hemisphere mask.
 .where(NH_mask & ds_domain_cfg['tmaskutil'])
 # Apply area-weighting.
 .weighted(areacello)
 # Compute the total sea ice area by summing over x and y dimensions.
 .sum(dim=['x', 'y'])
 # Plot the resulting time series.
 .plot(lw=2)
 )
# -- Plotting the March Northern Hemisphere Sea Ice Area -- #
# Calculate the area of each grid cell using the e1t and e2t variables (grid cell scale factors).
areacello = ds_domain_cfg['e1t'] * ds_domain_cfg['e2t']

(ds_siconc['siconc']
 # Select only March sea ice concentration data.
 .sel(time_counter=ds_siconc['time_counter'].dt.month.isin([3]))
 # Apply Northern Hemisphere mask.
 .where(NH_mask & ds_domain_cfg['tmaskutil'])
 # Apply area-weighting.
 .weighted(areacello)
 # Compute the total sea ice area by summing over x and y dimensions.
 .sum(dim=['x', 'y'])
 # Plot the resulting time series.
 .plot(lw=2)
 )

Out[23]:

[<matplotlib.lines.Line2D at 0x149381297250>]