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 search the Near-Present Day collection for all ocean model outputs including the sea surface temperature (SST) variable tos_con:

In [3]:

catalog.search(collection='noc-npd', variable='tos_con')

catalog.search(collection='noc-npd', variable='tos_con')

            * Item ID: noc-npd/npd-eorca1-era5v1/gn_global/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_global
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd/npd-eorca1-era5v1/gn_global/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_global
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd/npd-eorca025-era5v1/gn_global/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_global
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd/npd-eorca025-era5v1/gn_global/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_global
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd/npd-eorca025-era5v1/gn_global/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_global
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

• Now, let's take a closer look at the first Item in our search results:

In [6]:

catalog.Items[0]

catalog.Items[0]

Out[6]:

• type "Feature"
• stac_version "1.1.0"
• stac_extensions[] 0 items
• id "noc-npd/npd-eorca1-era5v1/gn_global/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_global"
  • variables[] 73 items

    • 0 "fsitherm"

    • 1 "hfempds"

    • 2 "ficeberg"

    • 3 "e3t"

    • 4 "berg_latent_heat_flux"

    • 5 "empmr"

    • 6 "friver"

    • 7 "evs"

    • 8 "hfss"

    • 9 "hfds"

    • 10 "hfls"

    • 11 "hfrainds"

    • 12 "hflx_rnf"

    • 13 "hfns"

    • 14 "hfsr"

    • 15 "mlotstmax"

    • 16 "mlotst"

    • 17 "hfevapds"

    • 18 "hfto"

    • 19 "mlotstsq"

    • 20 "ocontempmint"

    • 21 "ocontemppmdiff"

    • 22 "ocontemptend"

    • 23 "osaltdiff"

    • 24 "ocontempadvect"

    • 25 "mlotstmin"

    • 26 "osaltadvect"

    • 27 "osalttend"

    • 28 "prsn"

    • 29 "ocontempdiff"

    • 30 "rlntds"

    • 31 "sbt_con"

    • 32 "pbo"

    • 33 "sfdsi"

    • 34 "osaltpmdiff"

    • 35 "sbs_abs"

    • 36 "rsdo"

    • 37 "rsdoabsorb"

    • 38 "snowpre"

    • 39 "so_abs"

    • 40 "soicecov"

    • 41 "sohflisf"

    • 42 "sohfcisf"

    • 43 "snow_ai_cea"

    • 44 "sos_abs"

    • 45 "somint_abs"

    • 46 "somixhgt"

    • 47 "rsntds"

    • 48 "sossq_abs"

    • 49 "sowindsp"

    • 50 "somxl010"

    • 51 "sowaflup"

    • 52 "sosafldo"

    • 53 "thetao_con"

    • 54 "somxzint1"

    • 55 "sowflisf"

    • 56 "time_counter_bounds"

    • 57 "strd_evd_li"

    • 58 "strd_bbl_li"

    • 59 "strd_atf_li"

    • 60 "time_centered_bounds"

    • 61 "ttrd_evd_li"

    • 62 "tossq_con"

    • 63 "ttrd_atf_li"

    • 64 "tos_con"

    • 65 "ttrd_bbl_li"

    • 66 "tnpeo"

    • 67 "ttrd_qns_li"

    • 68 "vohflisf"

    • 69 "vowflisf"

    • 70 "vohfcisf"

    • 71 "zos"

    • 72 "zossq"
  • 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 "2025-07-09T16:01:15.985190"
  • start_datetime "1976-01-01T00:00:00Z"
  • end_datetime "2024-12-31T00:00:00Z"
  • datetime None
• links[] 3 items

  • 0

    • rel "root"
    • href "https://raw.githubusercontent.com/NOC-MSM/OceanDataStore/dev/catalogs/noc-model-stac/catalog.json"
    • type "application/json"
    • title "NOC Model STAC Catalog"

  • 1

    • rel "self"
    • href "https://raw.githubusercontent.com/NOC-MSM/OceanDataStore/dev/catalogs/noc-model-stac/noc-npd/noc-npd/npd-eorca1-era5v1/noc-npd/npd-eorca1-era5v1/gn_global/noc-npd/npd-eorca1-era5v1/gn_global/T1y/noc-npd/npd-eorca1-era5v1/gn_global/T1y.json"
    • type "application/json"

  • 2

    • rel "parent"
    • href "https://raw.githubusercontent.com/NOC-MSM/OceanDataStore/dev/catalogs/noc-model-stac/noc-npd/noc-npd/npd-eorca1-era5v1/noc-npd/npd-eorca1-era5v1/gn_global/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/gn_global/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"

• Next, we will open a subset (1980-1990) of the annual-mean SST data from the 1-degree eORCA1 ERA5v1 simulation as an xarray Dataset by using the open_dataset() method and specifying start and end date strings:

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_lon                (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    nav_lat                (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)
    hfds                   (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>
    fsitherm               (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    friver                 (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    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>
    ...                     ...
    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>
    vowflisf               (time_counter, deptht, y, x) float32 393MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
    ttrd_evd_li            (time_counter, deptht, y, x) float32 393MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
    ttrd_qns_li            (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
    tossq_con              (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_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

  • 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

  • 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)
  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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_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

  • 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_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

  • 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

  • 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

  • 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_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

  • 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

• 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 0x14bacac31160>