OceanDataCatalog | NOC Near-Present Day¶

About¶

This Notebook demonstrates 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.

In [1]:

from OceanDataStore import OceanDataCatalog

from OceanDataStore import OceanDataCatalog

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

In [2]:

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

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

• Let's use the available_collections property to return the names (IDs) of all available dataset collections in the noc-model-stac:

In [3]:

catalog.available_collections

catalog.available_collections

Out[3]:

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

• 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:

In [4]:

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/gn/T1y
              Title: eORCA1 ERA5v1 NPD T1y Icechunk repository
              Description: Icechunk repository containing eORCA1 ERA5v1 NPD global ocean physics annual mean outputs defined at T-points.
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca1-era5v1/gn/T1m
              Title: eORCA1 ERA5v1 NPD T1m Icechunk repository
              Description: Icechunk repository containing eORCA1 ERA5v1 NPD global ocean physics monthly mean outputs defined at T-points.
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/gn/T1y_3d
              Title: eORCA025 ERA5v1 NPD T1y_3d Icechunk repository
              Description: Icechunk repository containing eORCA025 ERA5v1 NPD global ocean physics annual mean outputs defined at T-points.
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/gn/T1m_3d
              Title: eORCA025 ERA5v1 NPD T1m_3d Icechunk repository
              Description: Icechunk repository containing eORCA025 ERA5v1 NPD global ocean physics monthly mean outputs defined at T-points.
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/gn/T5d_3d
              Title: eORCA025 ERA5v1 NPD T5d_3d Icechunk repository
              Description: Icechunk repository containing eORCA025 ERA5v1 NPD global ocean physics 5-day mean outputs defined at T-points.
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/gn/T1y_3d
              Title: eORCA12 ERA5v1 NPD T1y_3d Icechunk repository
              Description: Icechunk repository containing eORCA12 ERA5v1 NPD global ocean physics annual mean outputs defined at T-points.
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/gn/T1m_3d
              Title: eORCA12 ERA5v1 NPD T1m_3d Icechunk repository
              Description: Icechunk repository containing eORCA12 ERA5v1 NPD global ocean physics monthly mean outputs defined at T-points.
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

• Now that 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.

In [5]:

catalog.available_items

catalog.available_items

Out[5]:

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

• Now, let's take a closer look at the first Item in our search results. This corresponds to the annual-mean T-grid variables output by the 1-degree NPD eORCA1 ERA5v1 simulation.

• By looking in properties/variable_standard_names, we can see that 'sea_surface_temperature' is the 59th variable in this dataset and is named tos_con.

In [6]:

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/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 "eORCA1 ERA5v1 NPD T1y Icechunk repository"
  • description "Icechunk repository containing eORCA1 ERA5v1 NPD global ocean physics annual mean outputs defined at T-points."
  • platform "gn"
  • variables[] 73 items

    • 0 "fsitherm"

    • 1 "hfempds"

    • 2 "e3t"

    • 3 "evs"

    • 4 "friver"

    • 5 "hfds"

    • 6 "berg_latent_heat_flux"

    • 7 "ficeberg"

    • 8 "empmr"

    • 9 "hfls"

    • 10 "hfevapds"

    • 11 "hfsr"

    • 12 "hfto"

    • 13 "hfrainds"

    • 14 "hflx_rnf"

    • 15 "hfns"

    • 16 "mlotstsq"

    • 17 "mlotstmin"

    • 18 "hfss"

    • 19 "ocontemppmdiff"

    • 20 "ocontempadvect"

    • 21 "mlotstmax"

    • 22 "ocontempdiff"

    • 23 "ocontemptend"

    • 24 "osaltpmdiff"

    • 25 "mlotst"

    • 26 "ocontempmint"

    • 27 "osaltadvect"

    • 28 "osalttend"

    • 29 "pbo"

    • 30 "rsntds"

    • 31 "rsdoabsorb"

    • 32 "osaltdiff"

    • 33 "sfdsi"

    • 34 "rlntds"

    • 35 "so_abs"

    • 36 "rsdo"

    • 37 "sohfcisf"

    • 38 "sbs_abs"

    • 39 "snowpre"

    • 40 "prsn"

    • 41 "sbt_con"

    • 42 "snow_ai_cea"

    • 43 "somxzint1"

    • 44 "sosafldo"

    • 45 "soicecov"

    • 46 "sos_abs"

    • 47 "sohflisf"

    • 48 "somint_abs"

    • 49 "somixhgt"

    • 50 "somxl010"

    • 51 "sossq_abs"

    • 52 "thetao_con"

    • 53 "strd_atf_li"

    • 54 "sowaflup"

    • 55 "sowindsp"

    • 56 "tos_con"

    • 57 "tossq_con"

    • 58 "sowflisf"

    • 59 "time_centered_bounds"

    • 60 "tnpeo"

    • 61 "time_counter_bounds"

    • 62 "strd_evd_li"

    • 63 "strd_bbl_li"

    • 64 "ttrd_evd_li"

    • 65 "ttrd_atf_li"

    • 66 "zossq"

    • 67 "vohfcisf"

    • 68 "ttrd_qns_li"

    • 69 "vowflisf"

    • 70 "vohflisf"

    • 71 "ttrd_bbl_li"

    • 72 "zos"
  • variable_standard_names[] 73 items

    • 0 "fsitherm"

    • 1 "ocean_surface_downward_heat_flux_from_E-P"

    • 2 "cell_thickness"

    • 3 "water_evaporation_flux"

    • 4 "water_flux_into_sea_water_from_rivers"

    • 5 "ocean_surface_downward_total_heat_flux"

    • 6 "latent_heat_flux_from_icebergs"

    • 7 "water_flux_into_sea_water_from_icebergs"

    • 8 "water_flux_out_of_sea_ice_and_sea_water"

    • 9 "ocean_surface_downward_latent_heat_flux"

    • 10 "temperature_flux_due_to_evaporation_expressed_as_heat_flux_out_of_sea_water"

    • 11 "surface_net_downward_solar_heat_flux"

    • 12 "surface_net_downward_total_heat_flux"

    • 13 "temperature_flux_due_to_rain_expressed_as_heat_flux_into_sea_water"

    • 14 "temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water"

    • 15 "surface_net_downward_non_solar_heat_flux"

    • 16 "square_of_ocean_mixed_layer_thickness_defined_by_sigma_theta"

    • 17 "ocean_mixed_layer_thickness_defined_by_sigma_theta"

    • 18 "ocean_surface_downward_sensible_heat_flux"

    • 19 "ocontemppmdiff"

    • 20 "ocontempadvect"

    • 21 "ocean_mixed_layer_thickness_defined_by_sigma_theta"

    • 22 "ocontempdiff"

    • 23 "ocontemptend"

    • 24 "osaltpmdiff"

    • 25 "ocean_mixed_layer_thickness_defined_by_sigma_theta"

    • 26 "integral_wrt_depth_of_product_of_density_and_conservative_temperature"

    • 27 "osaltadvect"

    • 28 "osalttend"

    • 29 "sea_water_pressure_at_sea_floor"

    • 30 "ocean_surface_net_downward_shortwave_heat_flux"

    • 31 "rsdoabsorb"

    • 32 "osaltdiff"

    • 33 "downward_sea_ice_basal_salt_flux"

    • 34 "ocean_surface_net_downward_longwave_heat_flux"

    • 35 "sea_water_absolute_salinity"

    • 36 "downwelling_shortwave_flux_in_sea_water"

    • 37 ""

    • 38 "sbs_abs"

    • 39 "snowfall_flux"

    • 40 "snowfall_flux"

    • 41 "sbt_con"

    • 42 "snowfall_flux"

    • 43 "ocean_mixed_layer_thickness_defined_by_sigma_theta"

    • 44 "salt_flux_into_sea_water"

    • 45 "sea_ice_area_fraction"

    • 46 "sea_surface_salinity"

    • 47 ""

    • 48 "integral_wrt_depth_of_product_of_density_and_absolute_salinity"

    • 49 "ocean_mixed_layer_thickness_defined_by_vertical_tracer_diffusivity"

    • 50 "ocean_mixed_layer_thickness_defined_by_sigma_theta"

    • 51 "square_of_sea_surface_Salinity"

    • 52 "sea_water_conservative_temperature"

    • 53 "strd_atf_li"

    • 54 "water_flux_out_of_sea_ice_and_sea_water"

    • 55 "wind_speed"

    • 56 "sea_surface_temperature"

    • 57 "square_of_sea_surface_temperature"

    • 58 ""

    • 59 "time_centered_bounds"

    • 60 "tnpeo"

    • 61 "time_counter_bounds"

    • 62 "strd_evd_li"

    • 63 "strd_bbl_li"

    • 64 "ttrd_evd_li"

    • 65 "ttrd_atf_li"

    • 66 "square_of_sea_surface_height_above_geoid"

    • 67 ""

    • 68 "ttrd_qns_li"

    • 69 ""

    • 70 ""

    • 71 "ttrd_bbl_li"

    • 72 "sea_surface_height_above_geoid"
  • 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"
  • si_component "SI3 v4.0"
  • status "ongoing"
  • update_frequency "quarterly"
  • latest_data_update "2025-10-13T16:42:16.418842"
  • start_datetime "1976-01-01T00:00:00Z"
  • end_datetime "2024-12-31T00:00:00Z"
  • datetime None
• links[] 3 items

  • 0

    • rel "root"
    • href "https://noc-msm-o.s3-ext.jc.rl.ac.uk/oceandatastore/noc-model-stac/catalog.json"
    • type "application/json"
    • title "NOC Model STAC Catalog"

  • 1

    • rel "self"
    • href "https://noc-msm-o.s3-ext.jc.rl.ac.uk/oceandatastore/noc-model-stac/noc-npd-era5/npd-eorca1-era5v1/gn/noc-npd-era5/npd-eorca1-era5v1/gn/T1y/noc-npd-era5/npd-eorca1-era5v1/gn/T1y.json"
    • type "application/json"

  • 2

    • rel "parent"
    • href "https://noc-msm-o.s3-ext.jc.rl.ac.uk/oceandatastore/noc-model-stac/noc-npd-era5/npd-eorca1-era5v1/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/icechunk"
    • title "eORCA1 ERA5v1 NPD T1y Icechunk repository"
    • description "Icechunk repository containing eORCA1 ERA5v1 NPD global ocean physics annual mean outputs defined at T-points."
    • bucket "npd-eorca1-era5v1"
    • prefix "T1y"
    • endpoint_url "https://noc-msm-o.s3-ext.jc.rl.ac.uk"
    • 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 and specifying start and end date strings.

• Here, we use the .id attribute of our first Item, but we could have also copied the id string from above.

In [7]:

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:                (time_counter: 11, y: 331, x: 360, deptht: 75,
                            axis_nbounds: 2)
Coordinates:
  * deptht                 (deptht) float32 300B 0.5058 1.556 ... 5.902e+03
    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>
    time_centered          (time_counter) datetime64[ns] 88B dask.array<chunksize=(1,), meta=np.ndarray>
  * time_counter           (time_counter) datetime64[ns] 88B 1980-07-02 ... 1...
Dimensions without coordinates: y, x, axis_nbounds
Data variables: (12/73)
    empmr                  (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    e3t                    (time_counter, deptht, y, x) float32 393MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
    fsitherm               (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    berg_latent_heat_flux  (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>
    evs                    (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    ...                     ...
    vohfcisf               (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>
    ttrd_bbl_li            (time_counter, deptht, y, x) float32 393MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
    vohflisf               (time_counter, deptht, y, x) float32 393MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
    zossq                  (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    ttrd_qns_li            (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time_counter: 11
  • y: 331
  • x: 360
  • deptht: 75
  • axis_nbounds: 2
• Coordinates: (5)
  • deptht
    (deptht)
    float32
    0.5058 1.556 ... 5.902e+03

    name :

    deptht

    long_name :

    Vertical T levels

    units :

    m

    positive :

    down

    bounds :

    deptht_bounds

    array([5.057600e-01, 1.555855e+00, 2.667682e+00, 3.856280e+00, 5.140361e+00,
           6.543034e+00, 8.092519e+00, 9.822750e+00, 1.177368e+01, 1.399104e+01,
           1.652532e+01, 1.942980e+01, 2.275762e+01, 2.655830e+01, 3.087456e+01,
           3.574020e+01, 4.118002e+01, 4.721189e+01, 5.385064e+01, 6.111284e+01,
           6.902168e+01, 7.761116e+01, 8.692943e+01, 9.704131e+01, 1.080303e+02,
           1.200000e+02, 1.330758e+02, 1.474062e+02, 1.631645e+02, 1.805499e+02,
           1.997900e+02, 2.211412e+02, 2.448906e+02, 2.713564e+02, 3.008875e+02,
           3.338628e+02, 3.706885e+02, 4.117939e+02, 4.576256e+02, 5.086399e+02,
           5.652923e+02, 6.280260e+02, 6.972587e+02, 7.733683e+02, 8.566790e+02,
           9.474479e+02, 1.045854e+03, 1.151991e+03, 1.265861e+03, 1.387377e+03,
           1.516364e+03, 1.652568e+03, 1.795671e+03, 1.945296e+03, 2.101027e+03,
           2.262422e+03, 2.429025e+03, 2.600380e+03, 2.776039e+03, 2.955570e+03,
           3.138565e+03, 3.324641e+03, 3.513446e+03, 3.704657e+03, 3.897982e+03,
           4.093159e+03, 4.289953e+03, 4.488155e+03, 4.687581e+03, 4.888070e+03,
           5.089479e+03, 5.291683e+03, 5.494575e+03, 5.698061e+03, 5.902058e+03],
          dtype=float32)

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

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

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

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

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

    standard_name :

    time

    long_name :

    Time axis

    time_origin :

    1900-01-01 00:00:00

    bounds :

    time_centered_bounds

    Array Chunk Bytes 88 B 8 B Shape (11,) (1,) Dask graph 11 chunks in 3 graph layers Data type datetime64[ns] numpy.ndarray

  • time_counter
    (time_counter)
    datetime64[ns]
    1980-07-02 ... 1990-07-02T12:00:00

    axis :

    T

    standard_name :

    time

    long_name :

    Time axis

    time_origin :

    1900-01-01 00:00:00

    bounds :

    time_counter_bounds

    array(['1980-07-02T00:00:00.000000000', '1981-07-02T12:00:00.000000000',
           '1982-07-02T12:00:00.000000000', '1983-07-02T12:00:00.000000000',
           '1984-07-02T00:00:00.000000000', '1985-07-02T12:00:00.000000000',
           '1986-07-02T12:00:00.000000000', '1987-07-02T12:00:00.000000000',
           '1988-07-02T00:00:00.000000000', '1989-07-02T12:00:00.000000000',
           '1990-07-02T12:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (73)
  • empmr
    (time_counter, y, x)
    float32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    standard_name :

    water_flux_out_of_sea_ice_and_sea_water

    long_name :

    Net Upward Water Flux

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • e3t
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    standard_name :

    cell_thickness

    long_name :

    T-cell thickness

    units :

    m

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    long_name :

    Water flux due to freezing/melting

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    latent_heat_flux_from_icebergs

    long_name :

    icb heat flux to ocean due to melting latent heat

    units :

    J/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    ocean_surface_downward_total_heat_flux

    long_name :

    total flux at ocean surface

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    water_evaporation_flux

    long_name :

    Water Evaporation Flux Where Ice Free Ocean over Sea

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    ocean_surface_downward_heat_flux_from_E-P

    long_name :

    Downward Heat Flux from E-P over open ocean

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    water_flux_into_sea_water_from_rivers

    long_name :

    River Runoffs

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    ocean_surface_downward_sensible_heat_flux

    long_name :

    Sensible Downward Heat Flux over open ocean

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    ocean_surface_downward_latent_heat_flux

    long_name :

    Latent Downward Heat Flux over open ocean

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    surface_net_downward_total_heat_flux

    long_name :

    Net Downward Heat Flux

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water

    long_name :

    Temperature Flux due to Runoff Expressed as Heat Flux into Sea Water

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    water_flux_into_sea_water_from_icebergs

    long_name :

    icb melt rate of icebergs

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    temperature_flux_due_to_rain_expressed_as_heat_flux_into_sea_water

    long_name :

    Temperature Flux due to Rain Expressed as Heat Flux In To Sea Water

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    temperature_flux_due_to_evaporation_expressed_as_heat_flux_out_of_sea_water

    long_name :

    Temperature Flux due to Evaporation Expressed as Heat Flux Out of Sea Water

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    surface_net_downward_non_solar_heat_flux

    long_name :

    non solar Downward Heat Flux

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    surface_net_downward_solar_heat_flux

    long_name :

    Shortwave Radiation

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    ocean_mixed_layer_thickness_defined_by_sigma_theta

    long_name :

    Ocean Mixed Layer Thickness Defined by Sigma T

    units :

    m

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    ocean_mixed_layer_thickness_defined_by_sigma_theta

    long_name :

    Ocean Mixed Layer Thickness Defined by Sigma T

    units :

    m

    online_operation :

    maximum

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: maximum (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • ocontemppmdiff
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated heat-trend: isopycnal diffusion

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    ocean_mixed_layer_thickness_defined_by_sigma_theta

    long_name :

    Ocean Mixed Layer Thickness Defined by Sigma T

    units :

    m

    online_operation :

    minimum

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: minimum (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    square_of_ocean_mixed_layer_thickness_defined_by_sigma_theta

    long_name :

    Square of Ocean Mixed Layer Thickness Defined by Sigma T

    units :

    m

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • ocontempdiff
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated heat-trend: pure vert. diffusion

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

  • osalttend
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated salt-trend: total model trend :

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

  • ocontemptend
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated heat-trend: total model trend :

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    integral_wrt_depth_of_product_of_density_and_conservative_temperature

    long_name :

    vertical integral of conservative temperature times density

    units :

    (kg m2) degree_C

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • osaltpmdiff
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated salt-trend: isopycnal diffusion

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

  • osaltdiff
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated salt-trend: pure vert. diffusion

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

  • osaltadvect
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated salt-trend: total advection

    units :

    Kg/m2/s

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    sea_water_pressure_at_sea_floor

    long_name :

    Sea Water Pressure at Sea Floor

    units :

    dbar

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • ocontempadvect
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated heat-trend: total advection

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    snowfall_flux

    long_name :

    Snow precipitation

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • rsdoabsorb
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated heat-trend: solar flux

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    downward_sea_ice_basal_salt_flux

    long_name :

    Downward salt flux

    units :

    g/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    ocean_surface_net_downward_shortwave_heat_flux

    long_name :

    solar heat flux at ocean surface

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    snowfall_flux

    long_name :

    Snowfall Flux

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    long_name :

    sea bottom absolute salinity

    units :

    g/kg

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • rsdo
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    standard_name :

    downwelling_shortwave_flux_in_sea_water

    long_name :

    Shortwave Radiation 3D distribution

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

  • so_abs
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    standard_name :

    sea_water_absolute_salinity

    long_name :

    sea_water_absolute_salinity

    units :

    g/kg

    online_operation :

    average

    interval_operation :

    1 yr

    interval_write :

    1 yr

    cell_methods :

    time: mean

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    long_name :

    Ice shelf latent heat flux

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    snowfall_flux

    long_name :

    Snow over sea-ice (cell average)

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    ocean_surface_net_downward_longwave_heat_flux

    long_name :

    Longwave Downward Heat Flux over open ocean

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    long_name :

    sea bottom conservative temperature

    units :

    degC

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    long_name :

    Ice shelf heat content flux

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    ocean_mixed_layer_thickness_defined_by_vertical_tracer_diffusivity

    long_name :

    Turbocline depth (Kz = 5e-4)

    units :

    m

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    water_flux_out_of_sea_ice_and_sea_water

    long_name :

    Net Upward Water Flux

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    square_of_sea_surface_Salinity

    long_name :

    Square of Sea Surface Salinity

    units :

    1e-6

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • somxl010
    (time_counter, y, x)
    float32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    standard_name :

    ocean_mixed_layer_thickness_defined_by_sigma_theta

    long_name :

    Mixed Layer Depth (dsigma = 0.01 wrt 10m)

    units :

    m

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    sea_surface_salinity

    long_name :

    sea_surface_absolute_salinity

    units :

    g/kg

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    sea_ice_area_fraction

    long_name :

    Ice fraction

    units :

    1

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    long_name :

    Ice shelf melting

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • somxzint1
    (time_counter, y, x)
    float32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    standard_name :

    ocean_mixed_layer_thickness_defined_by_sigma_theta

    long_name :

    Mixed Layer Depth interpolated

    units :

    m

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    salt_flux_into_sea_water

    long_name :

    Downward salt flux

    units :

    g/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    integral_wrt_depth_of_product_of_density_and_absolute_salinity

    long_name :

    vertical integral of absolute salinity times density

    units :

    (kg m2) x (1e-3)

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • strd_atf_li
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated salt -trend: asselin time filter

    units :

    kg/(m^2 s)

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

  • strd_bbl_li
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated salt-trend: bottom boundary layer

    units :

    kg/(m^2 s)

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

  • thetao_con
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    standard_name :

    sea_water_conservative_temperature

    long_name :

    sea_water_conservative_temperature

    units :

    degC

    online_operation :

    average

    interval_operation :

    1 yr

    interval_write :

    1 yr

    cell_methods :

    time: mean

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

  • time_centered_bounds
    (time_counter, axis_nbounds)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    Array Chunk Bytes 176 B 16 B Shape (11, 2) (1, 2) Dask graph 11 chunks in 3 graph layers Data type datetime64[ns] numpy.ndarray

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

    standard_name :

    sea_surface_temperature

    long_name :

    sea_surface_conservative_temperature

    units :

    degC

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • strd_evd_li
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated salt-trend: evd convection

    units :

    kg/(m^2 s)

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    wind_speed

    long_name :

    wind speed module

    units :

    m/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • time_counter_bounds
    (time_counter, axis_nbounds)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    Array Chunk Bytes 176 B 16 B Shape (11, 2) (1, 2) Dask graph 11 chunks in 3 graph layers Data type datetime64[ns] numpy.ndarray

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

    long_name :

    Tendency of ocean potential energy content

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    square_of_sea_surface_temperature

    long_name :

    square_of_sea_surface_conservative_temperature

    units :

    degC2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • ttrd_evd_li
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated heat-trend: evd convection

    units :

    W/m^2

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

  • ttrd_atf_li
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated heat-trend: asselin time filter

    units :

    W/m^2

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

  • vowflisf
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    standard_name :

    long_name :

    3d Ice shelf fresh water flux ( from isf to oce )

    units :

    kg/m2/s

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

  • vohfcisf
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    standard_name :

    long_name :

    Ice shelf heat content flux of injected water ( from isf to oce )

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    sea_surface_height_above_geoid

    long_name :

    Sea Surface Height Above Geoid

    units :

    m

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

  • ttrd_bbl_li
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    long_name :

    layer integrated heat-trend: bottom boundary layer

    units :

    W/m^2

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

  • vohflisf
    (time_counter, deptht, y, x)
    float32
    dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>

    standard_name :

    long_name :

    Ice shelf latent heat flux ( from isf to oce )

    units :

    W/m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 375.01 MiB 11.36 MiB Shape (11, 75, 331, 360) (1, 25, 331, 360) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    square_of_sea_surface_height_above_geoid

    long_name :

    Square of Sea Surface Height Above Geoid

    units :

    m2

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

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

    long_name :

    layer integrated heat-trend: non-solar flux + runoff

    units :

    W/m^2

    online_operation :

    average

    interval_operation :

    7200 s

    interval_write :

    1 yr

    cell_methods :

    time: mean (interval: 7200 s)

    Array Chunk Bytes 5.00 MiB 465.47 kiB Shape (11, 331, 360) (1, 331, 360) Dask graph 11 chunks in 3 graph layers Data type float32 numpy.ndarray

• Indexes: (2)
  • deptht
    PandasIndex

    PandasIndex(Index([0.5057600140571594, 1.5558552742004395, 2.6676816940307617,
           3.8562798500061035,  5.140361309051514,  6.543033599853516,
             8.09251880645752,  9.822750091552734, 11.773679733276367,
            13.99103832244873,  16.52532196044922,  19.42980194091797,
            22.75761604309082, 26.558300018310547, 30.874561309814453,
           35.740203857421875, 41.180023193359375,  47.21189498901367,
            53.85063552856445,  61.11283874511719,  69.02168273925781,
            77.61116027832031,  86.92942810058594,  97.04131317138672,
           108.03028106689453,              120.0, 133.07582092285156,
                    147.40625, 163.16445922851562,  180.5499267578125,
            199.7899627685547, 221.14117431640625,         244.890625,
           271.35638427734375, 300.88751220703125,  333.8628234863281,
               370.6884765625,  411.7938537597656,  457.6256103515625,
             508.639892578125,  565.2922973632812,  628.0260009765625,
            697.2586669921875,  773.3682861328125,   856.678955078125,
            947.4478759765625,  1045.854248046875,    1151.9912109375,
           1265.8614501953125,     1387.376953125, 1516.3636474609375,
           1652.5684814453125, 1795.6707763671875, 1945.2955322265625,
            2101.026611328125,  2262.421630859375,  2429.025146484375,
             2600.38037109375,  2776.039306640625,       2955.5703125,
             3138.56494140625,  3324.640869140625,  3513.445556640625,
             3704.65673828125,   3897.98193359375,   4093.15869140625,
             4289.95263671875,   4488.15478515625,    4687.5810546875,
             4888.06982421875,     5089.478515625,   5291.68310546875,
              5494.5751953125,     5698.060546875,    5902.0576171875],
          dtype='float32', name='deptht'))

  • time_counter
    PandasIndex

    PandasIndex(DatetimeIndex(['1980-07-02 00:00:00', '1981-07-02 12:00:00',
                   '1982-07-02 12:00:00', '1983-07-02 12:00:00',
                   '1984-07-02 00:00:00', '1985-07-02 12:00:00',
                   '1986-07-02 12:00:00', '1987-07-02 12:00:00',
                   '1988-07-02 00:00:00', '1989-07-02 12:00:00',
                   '1990-07-02 12:00:00'],
                  dtype='datetime64[ns]', name='time_counter', freq=None))

• Attributes: (0)

• Finally, let's create a plot of the time-mean (1980-1990) SST for the globe:

In [8]:

ds['tos_con'].mean(dim='time_counter').plot(cmap='RdBu_r')

ds['tos_con'].mean(dim='time_counter').plot(cmap='RdBu_r')

Out[8]:

<matplotlib.collections.QuadMesh at 0x14f303675d00>

Pre-Calculated Diagnostics¶

• 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.

• In addition to these variables, 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

• These diagnostics are calculated using the Meridional ovErTurning ciRculation diagnostIC (METRIC) package.

• Next, 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:

In [9]:

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/tn/M1m/MOVE_16N
              Title: eORCA1 ERA5v1 NPD M1m/MOVE_16N Icechunk repository
              Description: Icechunk repository containing eORCA1 ERA5v1 NPD ocean physics transect monthly mean outputs defined at MOVE_16N.
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca1-era5v1/tn/M1m/SAMBA_34_5S
              Title: eORCA1 ERA5v1 NPD M1m/SAMBA_34_5S Icechunk repository
              Description: Icechunk repository containing eORCA1 ERA5v1 NPD global ocean scalar monthly mean outputs.
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca1-era5v1/tn/M1m/RAPID_26N
              Title: eORCA1 ERA5v1 NPD M1m/RAPID_26N Icechunk repository
              Description: Icechunk repository containing eORCA1 ERA5v1 NPD global sea-ice monthly mean outputs defined at T-points.
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca1-era5v1/tn/M1m/OSNAP
              Title: eORCA1 ERA5v1 NPD M1m/OSNAP Icechunk repository
              Description: Icechunk repository containing eORCA1 ERA5v1 NPD global ocean scalar monthly mean outputs.
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/tn/M1m/MOVE_16N
              Title: eORCA025 ERA5v1 NPD M1m/MOVE_16N Icechunk repository
              Description: Icechunk repository containing eORCA025 ERA5v1 NPD ocean physics transect monthly mean outputs defined at MOVE_16N.
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/tn/M1m/SAMBA_34_5S
              Title: eORCA025 ERA5v1 NPD M1m/SAMBA_34_5S Icechunk repository
              Description: Icechunk repository containing eORCA025 ERA5v1 NPD global ocean scalar monthly mean outputs.
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/tn/M1m/RAPID_26N
              Title: eORCA025 ERA5v1 NPD M1m/RAPID_26N Icechunk repository
              Description: Icechunk repository containing eORCA025 ERA5v1 NPD global sea-ice monthly mean outputs defined at T-points.
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/tn/M1m/OSNAP
              Title: eORCA025 ERA5v1 NPD M1m/OSNAP Icechunk repository
              Description: Icechunk repository containing eORCA025 ERA5v1 NPD global ocean scalar monthly mean outputs.
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/tn/M1m/MOVE_16N
              Title: eORCA12 ERA5v1 NPD M1m/MOVE_16N Icechunk repository
              Description: Icechunk repository containing eORCA12 ERA5v1 NPD ocean physics transect monthly mean outputs defined at 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/tn/M1m/SAMBA_34_5S
              Title: eORCA12 ERA5v1 NPD M1m/SAMBA_34_5S Icechunk repository
              Description: Icechunk repository containing eORCA12 ERA5v1 NPD global ocean scalar monthly mean outputs.
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/tn/M1m/RAPID_26N
              Title: eORCA12 ERA5v1 NPD M1m/RAPID_26N Icechunk repository
              Description: Icechunk repository containing eORCA12 ERA5v1 NPD global sea-ice monthly mean outputs defined at T-points.
              Platform: tn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/tn/M1m/OSNAP
              Title: eORCA12 ERA5v1 NPD M1m/OSNAP Icechunk repository
              Description: Icechunk repository containing eORCA12 ERA5v1 NPD global ocean scalar monthly mean outputs.
              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.

• Now, let's open the RAPID_26N file for the 1-degree NPD eORCA1 ERA5v1 simulation we explored earlier.

In [10]:

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

ds_rapid

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

Out[10]:

<xarray.Dataset> Size: 156MB
Dimensions:        (time: 588, z: 75, xbounds: 72, x: 71)
Coordinates:
  * time           (time) datetime64[ns] 5kB 1976-01-16T12:00:00 ... 2024-12-...
  * x              (x) float64 568B -80.5 -79.5 -78.5 ... -12.5 -11.5 -10.5
  * 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
Data variables: (12/60)
    fw_fc          (time) float64 5kB dask.array<chunksize=(294,), meta=np.ndarray>
    fw_gyre_model  (time) float64 5kB dask.array<chunksize=(294,), meta=np.ndarray>
    fw_eddy        (time) float64 5kB dask.array<chunksize=(294,), meta=np.ndarray>
    dh             (time, z, xbounds) float64 25MB dask.array<chunksize=(1, 75, 72), meta=np.ndarray>
    fw_gyre_rapid  (time) float64 5kB dask.array<chunksize=(294,), meta=np.ndarray>
    fw_ek          (time) float64 5kB dask.array<chunksize=(294,), meta=np.ndarray>
    ...             ...
    temp           (time, z, x) float64 25MB dask.array<chunksize=(1, 75, 71), meta=np.ndarray>
    v_fc           (time, z) float64 353kB dask.array<chunksize=(1, 75), meta=np.ndarray>
    v_basin_rapid  (time, z) float64 353kB dask.array<chunksize=(1, 75), meta=np.ndarray>
    sf_wbw         (time, z) float64 353kB dask.array<chunksize=(1, 75), meta=np.ndarray>
    wbw            (time) float64 5kB dask.array<chunksize=(294,), meta=np.ndarray>
    vgeo           (time, z, x) float64 25MB dask.array<chunksize=(1, 75, 71), meta=np.ndarray>
Attributes:
    geostrophic_method:           bottom-up
    geostrophic_reference_level:  4820.0
    reference_to_model_velocity:  None
    eos:                          teos10
    rhocp:                        4084625.0
    ekman_level:                  100.0
    contact:                      fredc.ucar.edu
    code_reference:               https://github.com/NCAR/metric
    method_references:            (1) McCarthy, G. D., and Coauthors, 2015: M...

xarray.Dataset

• Dimensions:
  • time: 588
  • z: 75
  • xbounds: 72
  • x: 71
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1976-01-16T12:00:00 ... 2024-12-...

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

  • x
    (x)
    float64
    -80.5 -79.5 -78.5 ... -11.5 -10.5

    units :

    degrees_east

    array([-80.501541, -79.501572, -78.50161 , -77.50164 , -76.501671, -75.501694,
           -74.501717, -73.50174 , -72.501755, -71.50177 , -70.501785, -69.501793,
           -68.501801, -67.501808, -66.501808, -65.501808, -64.501808, -63.501801,
           -62.501793, -61.501778, -60.501766, -59.501747, -58.501728, -57.501701,
           -56.501678, -55.501648, -54.501617, -53.501583, -52.501545, -51.501507,
           -50.501465, -49.501419, -48.501373, -47.501324, -46.50127 , -45.501217,
           -44.50116 , -43.501099, -42.501038, -41.500973, -40.500908, -39.500843,
           -38.500771, -37.500702, -36.500629, -35.500553, -34.500477, -33.500401,
           -32.50032 , -31.50024 , -30.500158, -29.500076, -28.499992, -27.499908,
           -26.499823, -25.499737, -24.499651, -23.499563, -22.499475, -21.499388,
           -20.4993  , -19.499212, -18.499125, -17.499037, -16.498951, -15.498863,
           -14.498777, -13.498692, -12.498607, -11.498522, -10.498439])

  • z
    (z)
    float64
    0.5058 1.556 ... 5.902e+03

    units :

    m

    array([5.057600e-01, 1.555855e+00, 2.667682e+00, 3.856280e+00, 5.140361e+00,
           6.543034e+00, 8.092519e+00, 9.822750e+00, 1.177368e+01, 1.399104e+01,
           1.652532e+01, 1.942980e+01, 2.275762e+01, 2.655830e+01, 3.087456e+01,
           3.574020e+01, 4.118002e+01, 4.721189e+01, 5.385064e+01, 6.111284e+01,
           6.902168e+01, 7.761116e+01, 8.692943e+01, 9.704131e+01, 1.080303e+02,
           1.200000e+02, 1.330758e+02, 1.474062e+02, 1.631645e+02, 1.805499e+02,
           1.997900e+02, 2.211412e+02, 2.448906e+02, 2.713564e+02, 3.008875e+02,
           3.338628e+02, 3.706885e+02, 4.117939e+02, 4.576256e+02, 5.086399e+02,
           5.652923e+02, 6.280260e+02, 6.972587e+02, 7.733683e+02, 8.566790e+02,
           9.474479e+02, 1.045854e+03, 1.151991e+03, 1.265861e+03, 1.387377e+03,
           1.516364e+03, 1.652568e+03, 1.795671e+03, 1.945296e+03, 2.101027e+03,
           2.262422e+03, 2.429025e+03, 2.600380e+03, 2.776039e+03, 2.955570e+03,
           3.138565e+03, 3.324641e+03, 3.513446e+03, 3.704657e+03, 3.897982e+03,
           4.093159e+03, 4.289953e+03, 4.488155e+03, 4.687581e+03, 4.888070e+03,
           5.089479e+03, 5.291683e+03, 5.494575e+03, 5.698061e+03, 5.902058e+03])

  • xbounds
    (xbounds)
    float64
    -81.0 -80.0 -79.0 ... -11.0 -9.998

    units :

    degrees_east

    array([-81.001526, -80.001556, -79.001587, -78.001625, -77.001656, -76.001678,
           -75.001709, -74.001724, -73.001747, -72.001762, -71.001778, -70.001785,
           -69.001801, -68.001801, -67.001808, -66.001808, -65.001808, -64.001801,
           -63.001797, -62.001785, -61.00177 , -60.001755, -59.00174 , -58.001717,
           -57.00169 , -56.001663, -55.001633, -54.001602, -53.001564, -52.001526,
           -51.001488, -50.001442, -49.001396, -48.00135 , -47.001297, -46.001244,
           -45.00119 , -44.001129, -43.001068, -42.001007, -41.000938, -40.000877,
           -39.000809, -38.000736, -37.000664, -36.000591, -35.000515, -34.000439,
           -33.000359, -32.000282, -31.000198, -30.000118, -29.000034, -27.99995 ,
           -26.999866, -25.999779, -24.999695, -23.999607, -22.999519, -21.999432,
           -20.999344, -19.999256, -18.999168, -17.999081, -16.998993, -15.998907,
           -14.99882 , -13.998734, -12.99865 , -11.998564, -10.99848 ,  -9.998398])

• Data variables: (60)
  • fw_fc
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -73.5

    reference_salinity :

    35.17

    comment :

    equivalent freshwater transport by the Florida current

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_gyre_model
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    freshwater transport by the horizontal circulation calculated using model velocities

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_eddy
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    reference_salinity :

    35.17

    comment :

    equivalent freshwater transport by standing eddy component of geostrophic interior transport

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • dh
    (time, z, xbounds)
    float64
    dask.array<chunksize=(1, 75, 72), meta=np.ndarray>

    units :

    m2/s2

    comment :

    dynamic heights

    Array Chunk Bytes 24.22 MiB 42.19 kiB Shape (588, 75, 72) (1, 75, 72) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_gyre_rapid
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    freshwater transport by the horizontal circulation calculated using RAPID approximations

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_ek
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    reference_salinity :

    35.17

    comment :

    equivalent freshwater transport by Ekman transport

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_net_model
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    reference_salinity :

    35.17

    comment :

    equivalent freshwater transport by the net flow through the section using model velocities

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • ekman
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    comment :

    Ekman transport time series (streamfunction at 1000m)

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_mo
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    reference_salinity :

    35.17

    comment :

    equivalent freshwater transport by mid-ocean transport (fw_mo = fw_geoint + fw_wbw + fw_eddy)

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fc
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -73.5

    comment :

    Florida current transport time series (streamfunction at 1000m).

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_ot_rapid
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    freshwater transport by the overturning circulation calculated using RAPID approximations

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_geoint
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    reference_salinity :

    35.17

    comment :

    equivalent freshwater transport by zonal mean of geostrophic interior transport

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_ot_model
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    freshwater transport by the overturning circulation calculated using model velocities

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_net_rapid
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    reference_salinity :

    35.17

    comment :

    equivalent freshwater transport by the net flow through the section using RAPID approximations

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_sum_model
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    reference_salinity :

    35.17

    comment :

    Total freshwater transport across section calculated using model velocities (fw_sum_model = fw_gyre_model + fw_ot_model + fw_net_model)

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mocmax_model
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Time series of meridional overturning transport using model velocities (streamfunction maxima)

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_sum_rapid
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    reference_salinity :

    35.17

    comment :

    Total equivalent freshwater transport across section calculated using RAPID approximations (fw_sum_rapid = fw_fc + fw_ek + fw_mo = fw_ot_rapid + fw_gyre_rapid + fw_net_rapid)

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_wbw
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -73.5

    maximum_longitude :

    -70.0

    reference_salinity :

    35.17

    comment :

    equivalent freshwater transport by western boundary wedge transport

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • geoint
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    comment :

    Geostrophic interior transport time series (streamfunction at 1000m).

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • moc_model
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Time series of meridional overturning transport using model velocities (streamfunction at 1000m)

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mass_balance
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    comment :

    Rapid mass balance term

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • int_mod
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    comment :

    Model interior transport time series (streamfunction at 1000m).

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • moc_rapid
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Time series of meridional overturning transport using RAPID approximation (streamfunction at 1000m)

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_net_model
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Heat transport referenced to 0C by the net flow through the section using model velocities

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_gyre_model
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Heat transport by the horizontal circulation calculated using model velocities

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_net_rapid
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Heat transport referenced to 0C by the net flow through the section using RAPID approximations

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_ot_rapid
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Heat transport by the overturning circulation calculated using RAPID approximations

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_ek
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    comment :

    Heat transport referenced to 0C by Ekman transport

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_gyre_rapid
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Heat transport by the horizontal circulation calculated using RAPID approximations

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_fc
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -81.0

    maximum_longitude :

    -73.5

    comment :

    Heat transport referenced to 0C by the Florida current

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_mo
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    comment :

    Heat transport referenced to 0C by mid-ocean transport (q_mo = q_geoint + q_wbw + q_eddy)

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_geoint
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    comment :

    Heat transport referenced to 0C by zonal mean of geostrophic interior transport

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mocmax_rapid
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Time series of meridional overturning transport using RAPID approximation (streamfunction maxima)

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_eddy
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    comment :

    Heat transport referenced to 0C by standing eddy component of geostrophic interior transport

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_ot_model
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Heat transport by the overturning circulation calculated using model velocities

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • rho
    (time, z, xbounds)
    float64
    dask.array<chunksize=(1, 75, 72), meta=np.ndarray>

    units :

    kg/m3

    comment :

    density

    Array Chunk Bytes 24.22 MiB 42.19 kiB Shape (588, 75, 72) (1, 75, 72) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sf_ek
    (time, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    comment :

    Ekman overturning streamfunction.

    Array Chunk Bytes 344.53 kiB 600 B Shape (588, 75) (1, 75) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sf_geoint
    (time, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    comment :

    Geostrophic interior overturning streamfunction.

    Array Chunk Bytes 344.53 kiB 600 B Shape (588, 75) (1, 75) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_sum_rapid
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Total heat transport across section calculated using RAPID approximations (q_sum_rapid = q_fc + q_ek + q_mo = q_ot_rapid + q_gyre_rapid + q_net_rapid)

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sf_fc
    (time, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -73.5

    comment :

    Florida current overturning streamfunction.

    Array Chunk Bytes 344.53 kiB 600 B Shape (588, 75) (1, 75) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • salt
    (time, z, x)
    float64
    dask.array<chunksize=(1, 75, 71), meta=np.ndarray>

    units :

    PSU

    comment :

    model salinity

    Array Chunk Bytes 23.89 MiB 41.60 kiB Shape (588, 75, 71) (1, 75, 71) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_sum_model
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Total heat transport across section calculated using model velocities (q_sum_model = q_gyre_model + q_ot_model + q_net_model)

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_wbw
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PW

    minimum_longitude :

    -73.5

    maximum_longitude :

    -70.0

    comment :

    Heat transport referenced to 0C by western boundary wedge transport

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sf_mo
    (time, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -73.5

    maximum_longitude :

    -10.0

    comment :

    Mid ocean overturning streamfunction (sf_mo = sf_wbw + sf_int).

    Array Chunk Bytes 344.53 kiB 600 B Shape (588, 75) (1, 75) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_basin
    (time, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    units :

    PSU

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Basinwide zonal mean salinity profile

    Array Chunk Bytes 344.53 kiB 600 B Shape (588, 75) (1, 75) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sf_int_mod
    (time, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    comment :

    Model interior overturning streamfunction.

    Array Chunk Bytes 344.53 kiB 600 B Shape (588, 75) (1, 75) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sf_model
    (time, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Overturning streamfunctions using model velocities.

    Array Chunk Bytes 344.53 kiB 600 B Shape (588, 75) (1, 75) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • t_basin
    (time, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    units :

    degC

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Basinwide zonal mean potential temperature profile

    Array Chunk Bytes 344.53 kiB 600 B Shape (588, 75) (1, 75) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_fc_fwt
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    PSU

    minimum_longitude :

    -81.0

    maximum_longitude :

    -73.5

    comment :

    Florida current flow-weighted salinity

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sf_rapid
    (time, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Overturning streamfunctions using RAPID approximation.

    Array Chunk Bytes 344.53 kiB 600 B Shape (588, 75) (1, 75) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • t_fc_fwt
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    degC

    minimum_longitude :

    -81.0

    maximum_longitude :

    -73.5

    comment :

    Florida current flow-weighted potential temperature

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • umo
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -73.5

    maximum_longitude :

    -10.0

    comment :

    Upper mid-ocean transport time series (streamfunction at 1000m). umo = wbw + geoint

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • v
    (time, z, x)
    float64
    dask.array<chunksize=(1, 75, 71), meta=np.ndarray>

    units :

    m/s

    comment :

    model velocity

    Array Chunk Bytes 23.89 MiB 41.60 kiB Shape (588, 75, 71) (1, 75, 71) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • v_basin_model
    (time, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    units :

    Sv/m

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Basinwide transport profile using model velocities

    Array Chunk Bytes 344.53 kiB 600 B Shape (588, 75) (1, 75) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • temp
    (time, z, x)
    float64
    dask.array<chunksize=(1, 75, 71), meta=np.ndarray>

    units :

    degC

    comment :

    model potential temperature

    Array Chunk Bytes 23.89 MiB 41.60 kiB Shape (588, 75, 71) (1, 75, 71) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • v_fc
    (time, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    units :

    Sv/m

    minimum_longitude :

    -81.0

    maximum_longitude :

    -73.5

    comment :

    Florida current transport profile

    Array Chunk Bytes 344.53 kiB 600 B Shape (588, 75) (1, 75) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • v_basin_rapid
    (time, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    units :

    Sv/m

    minimum_longitude :

    -81.0

    maximum_longitude :

    -10.0

    comment :

    Basinwide transport profile using RAPID approximations

    Array Chunk Bytes 344.53 kiB 600 B Shape (588, 75) (1, 75) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sf_wbw
    (time, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -81.0

    maximum_longitude :

    -70.0

    comment :

    Western boundary wedge overturning streamfunction.

    Array Chunk Bytes 344.53 kiB 600 B Shape (588, 75) (1, 75) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

  • wbw
    (time)
    float64
    dask.array<chunksize=(294,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -73.5

    maximum_longitude :

    -70.0

    comment :

    Western boundary wedge transport time series (streamfunction at 1000m).

    Array Chunk Bytes 4.59 kiB 2.30 kiB Shape (588,) (294,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • vgeo
    (time, z, x)
    float64
    dask.array<chunksize=(1, 75, 71), meta=np.ndarray>

    units :

    m/s

    comment :

    geostrophic velocity

    Array Chunk Bytes 23.89 MiB 41.60 kiB Shape (588, 75, 71) (1, 75, 71) Dask graph 588 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • time
    PandasIndex

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

  • x
    PandasIndex

    PandasIndex(Index([ -80.50154113769531,  -79.50157165527344,   -78.5016098022461,
            -77.50164031982422,  -76.50167083740234,  -75.50169372558594,
            -74.50171661376953,  -73.50173950195312,  -72.50175476074219,
            -71.50177001953125,  -70.50178527832031,  -69.50179290771484,
            -68.50180053710938,   -67.5018081665039,   -66.5018081665039,
             -65.5018081665039,   -64.5018081665039, -63.501800537109375,
           -62.501792907714844,  -61.50177764892578, -60.501766204833984,
           -59.501747131347656,  -58.50172805786133,  -57.50170135498047,
           -56.501678466796875,  -55.50164794921875, -54.501617431640625,
           -53.501583099365234,  -52.50154495239258,  -51.50150680541992,
               -50.50146484375,  -49.50141906738281, -48.501373291015625,
            -47.50132369995117,  -46.50127029418945, -45.501216888427734,
            -44.50115966796875,   -43.5010986328125,  -42.50103759765625,
           -41.500972747802734,  -40.50090789794922,   -39.5008430480957,
           -38.500770568847656, -37.500701904296875,  -36.50062942504883,
           -35.500553131103516,   -34.5004768371582,  -33.50040054321289,
            -32.50032043457031, -31.500240325927734, -30.500158309936523,
           -29.500076293945312,  -28.49999237060547, -27.499908447265625,
            -26.49982261657715, -25.499736785888672, -24.499650955200195,
           -23.499563217163086, -22.499475479125977, -21.499387741088867,
           -20.499300003051758,  -19.49921226501465,  -18.49912452697754,
            -17.49903678894043, -16.498950958251953, -15.498863220214844,
           -14.498777389526367,  -13.49869155883789,  -12.49860668182373,
            -11.49852180480957, -10.498438835144043],
          dtype='float64', name='x'))

  • z
    PandasIndex

    PandasIndex(Index([0.5057600140571594, 1.5558552742004395, 2.6676816940307617,
           3.8562798500061035,  5.140361309051514,  6.543033599853516,
             8.09251880645752,  9.822750091552734, 11.773679733276367,
            13.99103832244873,  16.52532196044922,  19.42980194091797,
            22.75761604309082, 26.558300018310547, 30.874561309814453,
           35.740203857421875, 41.180023193359375,  47.21189498901367,
            53.85063552856445,  61.11283874511719,  69.02168273925781,
            77.61116027832031,  86.92942810058594,  97.04131317138672,
           108.03028106689453,              120.0, 133.07582092285156,
                    147.40625, 163.16445922851562,  180.5499267578125,
            199.7899627685547, 221.14117431640625,         244.890625,
           271.35638427734375, 300.88751220703125,  333.8628234863281,
               370.6884765625,  411.7938537597656,  457.6256103515625,
             508.639892578125,  565.2922973632812,  628.0260009765625,
            697.2586669921875,  773.3682861328125,   856.678955078125,
            947.4478759765625,  1045.854248046875,    1151.9912109375,
           1265.8614501953125,     1387.376953125, 1516.3636474609375,
           1652.5684814453125, 1795.6707763671875, 1945.2955322265625,
            2101.026611328125,  2262.421630859375,  2429.025146484375,
             2600.38037109375,  2776.039306640625,       2955.5703125,
             3138.56494140625,  3324.640869140625,  3513.445556640625,
             3704.65673828125,   3897.98193359375,   4093.15869140625,
             4289.95263671875,   4488.15478515625,    4687.5810546875,
             4888.06982421875,     5089.478515625,   5291.68310546875,
              5494.5751953125,     5698.060546875,    5902.0576171875],
          dtype='float64', name='z'))

  • xbounds
    PandasIndex

    PandasIndex(Index([ -81.00152587890625,  -80.00155639648438,   -79.0015869140625,
            -78.00162506103516,  -77.00165557861328,  -76.00167846679688,
              -75.001708984375,  -74.00172424316406,  -73.00174713134766,
            -72.00176239013672,  -71.00177764892578,  -70.00178527832031,
            -69.00180053710938,  -68.00180053710938,   -67.0018081665039,
             -66.0018081665039,   -65.0018081665039,  -64.00180053710938,
            -63.00179672241211,  -62.00178527832031,  -61.00177001953125,
            -60.00175476074219, -59.001739501953125,  -58.00171661376953,
            -57.00168991088867,  -56.00166320800781,  -55.00163269042969,
            -54.00160217285156, -53.001564025878906,  -52.00152587890625,
           -51.001487731933594, -50.001441955566406,  -49.00139617919922,
            -48.00135040283203,  -47.00129699707031, -46.001243591308594,
           -45.001190185546875, -44.001129150390625, -43.001068115234375,
           -42.001007080078125, -41.000938415527344, -40.000877380371094,
            -39.00080871582031, -38.000736236572266,  -37.00066375732422,
            -36.00059127807617,  -35.00051498413086,  -34.00043869018555,
            -33.00035858154297, -32.000282287597656, -31.000198364257812,
           -30.000118255615234,  -29.00003433227539, -27.999950408935547,
           -26.999866485595703, -25.999778747558594,  -24.99969482421875,
            -23.99960708618164,  -22.99951934814453, -21.999431610107422,
           -20.999343872070312, -19.999256134033203, -18.999168395996094,
           -17.999080657958984, -16.998992919921875, -15.998907089233398,
           -14.998820304870605, -13.998734474182129, -12.998649597167969,
           -11.998563766479492, -10.998479843139648,  -9.998397827148438],
          dtype='float64', name='xbounds'))

• Attributes: (9)

  geostrophic_method :

  bottom-up

  geostrophic_reference_level :

  4820.0

  reference_to_model_velocity :

  None

  eos :

  teos10

  rhocp :

  4084625.0

  ekman_level :

  100.0

  contact :

  fredc.ucar.edu

  code_reference :

  https://github.com/NCAR/metric

  method_references :

  (1) McCarthy, G. D., and Coauthors, 2015: Measuring the Atlantic Meridional Overturning Circulation at 26 degrees N. Progress in Oceanography, 130, 91-111. (2) Johns, W.E., M.O. Baringer, L.M. Beal, S.A. Cunningham, T. Kanzow, H.L. Bryden, J.J. Hirschi, J. Marotzke, C.S. Meinen, B. Shaw, and R. Curry, 2011: Continuous, Array-Based Estimates of Atlantic Ocean Heat Transport at 26.5N. J. Climate, 24, 2429-2449, doi: 10.1175/2010JCLI3997.1.

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, heat and freshwater transports, we also need access to the variables describing the model domain.

• Let's next search the OceanDataCatalog for any Items with identifiers which contain the "domain" key word:

In [11]:

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/gn/domain/domain_cfg
              Title: eORCA1 ERA5v1 NPD domain/domain_cfg Icechunk repository
              Description: Icechunk repository containing eORCA1 ERA5v1 NPD global ocean model domain_cfg variables.
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca025-era5v1/gn/domain/domain_cfg
              Title: eORCA025 ERA5v1 NPD domain/domain_cfg Icechunk repository
              Description: Icechunk repository containing eORCA025 ERA5v1 NPD global ocean model domain_cfg variables.
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd-era5/npd-eorca12-era5v1/gn/domain/domain_cfg
              Title: eORCA12 ERA5v1 NPD domain/domain_cfg Icechunk repository
              Description: Icechunk repository containing eORCA12 ERA5v1 NPD global ocean model domain_cfg variables.
              Platform: gn
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

• We can see from the list of search Items that domain_cfg, mesh_mask and subbasin ancillary data are available for each NPD model configuration.

• Next, let's open the domain_cfg file for the 1-degree NPD eORCA1 ERA5v1 simulation we explored earlier.

In [12]:

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

ds_domain_cfg

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

Out[12]:

<xarray.Dataset> Size: 666MB
Dimensions:        (t: 1, y: 331, x: 360, z: 75)
Dimensions without coordinates: t, y, x, z
Data variables: (12/43)
    bathy_metry    (t, y, x) float32 477kB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    bottom_level   (t, y, x) int32 477kB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    e2u            (t, y, x) float64 953kB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    e1v            (t, y, x) float64 953kB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    e2f            (t, y, x) float64 953kB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    e2v            (t, y, x) float64 953kB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    ...             ...
    top_level      (t, y, x) int32 477kB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    mask_opensea   (t, y, x) int32 477kB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    mask_csrnf     (t, y, x) int32 477kB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    gphit          (t, y, x) float64 953kB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    mask_csglo     (t, y, x) int32 477kB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    mask_csundef   (t, y, x) int32 477kB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
Attributes:
    DOMAIN_number_total:     1
    DOMAIN_number:           0
    DOMAIN_dimensions_ids:   [1, 2]
    DOMAIN_size_global:      [362, 332]
    DOMAIN_size_local:       [362, 332]
    DOMAIN_position_first:   [1, 1]
    DOMAIN_position_last:    [362, 332]
    DOMAIN_halo_size_start:  [0, 0]
    DOMAIN_halo_size_end:    [0, 0]
    DOMAIN_type:             BOX
    history:                 Mon Jun  5 12:41:32 2023: ncks -A mask.nc ORCA1_...
    NCO:                     4.4.7

xarray.Dataset

• Dimensions:
  • t: 1
  • y: 331
  • x: 360
  • z: 75
• Coordinates: (0)
• Data variables: (43)
  • bathy_metry
    (t, y, x)
    float32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • bottom_level
    (t, y, x)
    int32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 465.47 kiB 465.47 kiB Shape (1, 331, 360) (1, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • e2u
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • e1v
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • e2f
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • e2v
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • e1t
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • e2t
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • e1f
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • e1u
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • e3t_1d
    (t, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    Array Chunk Bytes 600 B 600 B Shape (1, 75) (1, 75) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • e3f_0
    (t, z, y, x)
    float64
    dask.array<chunksize=(1, 75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 68.18 MiB 68.18 MiB Shape (1, 75, 331, 360) (1, 75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • e3w_0
    (t, z, y, x)
    float64
    dask.array<chunksize=(1, 75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 68.18 MiB 68.18 MiB Shape (1, 75, 331, 360) (1, 75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • e3uw_0
    (t, z, y, x)
    float64
    dask.array<chunksize=(1, 75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 68.18 MiB 68.18 MiB Shape (1, 75, 331, 360) (1, 75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • e3u_0
    (t, z, y, x)
    float64
    dask.array<chunksize=(1, 75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 68.18 MiB 68.18 MiB Shape (1, 75, 331, 360) (1, 75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • e3vw_0
    (t, z, y, x)
    float64
    dask.array<chunksize=(1, 75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 68.18 MiB 68.18 MiB Shape (1, 75, 331, 360) (1, 75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • e3v_0
    (t, z, y, x)
    float64
    dask.array<chunksize=(1, 75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 68.18 MiB 68.18 MiB Shape (1, 75, 331, 360) (1, 75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • e3t_0
    (t, z, y, x)
    float64
    dask.array<chunksize=(1, 75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 68.18 MiB 68.18 MiB Shape (1, 75, 331, 360) (1, 75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • glamt
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • ff_f
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • gdepw_0
    (t, z, y, x)
    float64
    dask.array<chunksize=(1, 75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 68.18 MiB 68.18 MiB Shape (1, 75, 331, 360) (1, 75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • gdept_1d
    (t, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    Array Chunk Bytes 600 B 600 B Shape (1, 75) (1, 75) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • glamu
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • glamf
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • ff_t
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • gdept_0
    (t, z, y, x)
    float64
    dask.array<chunksize=(1, 75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 68.18 MiB 68.18 MiB Shape (1, 75, 331, 360) (1, 75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • gdepw_1d
    (t, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    Array Chunk Bytes 600 B 600 B Shape (1, 75) (1, 75) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • glamv
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • gphif
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • e3w_1d
    (t, z)
    float64
    dask.array<chunksize=(1, 75), meta=np.ndarray>

    Array Chunk Bytes 600 B 600 B Shape (1, 75) (1, 75) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • gphiu
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • mask_csgrprnf
    (t, y, x)
    int32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 465.47 kiB 465.47 kiB Shape (1, 331, 360) (1, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • mask_csemp
    (t, y, x)
    int32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 465.47 kiB 465.47 kiB Shape (1, 331, 360) (1, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • mask_csgrpglo
    (t, y, x)
    int32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 465.47 kiB 465.47 kiB Shape (1, 331, 360) (1, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • gphiv
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • mask_csgrpemp
    (t, y, x)
    int32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 465.47 kiB 465.47 kiB Shape (1, 331, 360) (1, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • time_counter
    (t)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 8 B 8 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • top_level
    (t, y, x)
    int32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 465.47 kiB 465.47 kiB Shape (1, 331, 360) (1, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • mask_opensea
    (t, y, x)
    int32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 465.47 kiB 465.47 kiB Shape (1, 331, 360) (1, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • mask_csrnf
    (t, y, x)
    int32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 465.47 kiB 465.47 kiB Shape (1, 331, 360) (1, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • gphit
    (t, y, x)
    float64
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

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

  • mask_csglo
    (t, y, x)
    int32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 465.47 kiB 465.47 kiB Shape (1, 331, 360) (1, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • mask_csundef
    (t, y, x)
    int32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 465.47 kiB 465.47 kiB Shape (1, 331, 360) (1, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

• Indexes: (0)
• Attributes: (12)

  DOMAIN_number_total :

  1

  DOMAIN_number :

  0

  DOMAIN_dimensions_ids :

  [1, 2]

  DOMAIN_size_global :

  [362, 332]

  DOMAIN_size_local :

  [362, 332]

  DOMAIN_position_first :

  [1, 1]

  DOMAIN_position_last :

  [362, 332]

  DOMAIN_halo_size_start :

  [0, 0]

  DOMAIN_halo_size_end :

  [0, 0]

  DOMAIN_type :

  BOX

  history :

  Mon Jun 5 12:41:32 2023: ncks -A mask.nc ORCA1_r4.2.0_domcfg.nc Mon Jun 5 12:40:01 2023: ncrename -d time_counter,t orca1_vn4.2.0_masks.nc mask.nc Mon Jun 5 12:12:48 2023: ncks -v mask_csemp,mask_csglo,mask_csgrpemp,mask_csgrpglo,mask_csgrprnf,mask_csrnf,mask_csundef,mask_opensea orca1_vn4.2.0_domain_cfg.nc orca1_vn4.2.0_masks.nc Mon Jun 5 12:08:16 2023: ncks -F -d x,2,361 -d y,1,331 domain_cfg.nc orca1_vn4.2.0_domain_cfg.nc

  NCO :

  4.4.7