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-stac")

catalog = OceanDataCatalog(catalog_name="noc-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')

Out[4]:

Search Results 8 Items found noc-npd-era5

Item ID	Title	Platform	Start Date	End Date	Variables
noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y	T1y Icechunk repository	gn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	74 variables empmr e3t hfempds hfds fsitherm hflx_rnf hfns hfrainds hfls ficeberg friver berg_latent_heat_flux mlotst hfss hfto hfsr hfevapds deptht_bounds mlotstmax evs mlotstsq ocontempdiff osaltpmdiff osaltdiff mlotstmin ocontempmint rlntds ocontempadvect osalttend rsdo prsn osaltadvect ocontemptend pbo sbt_con snow_ai_cea sbs_abs rsntds sfdsi so_abs ocontemppmdiff sohfcisf somixhgt sos_abs sosafldo soicecov somint_abs sohflisf rsdoabsorb snowpre strd_atf_li sowaflup sowflisf strd_bbl_li thetao_con somxl010 time_centered_bounds strd_evd_li somxzint1 ttrd_evd_li ttrd_atf_li sowindsp time_counter_bounds tos_con tossq_con tnpeo ttrd_bbl_li sossq_abs ttrd_qns_li zossq vohflisf zos vowflisf vohfcisf
noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1m	T1m Icechunk repository	gn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	74 variables berg_latent_heat_flux deptht_bounds ficeberg fsitherm e3t evs empmr friver hfds hfevapds hfrainds hfsr hfempds hflx_rnf hfls hfns mlotst mlotstmin mlotstsq mlotstmax hfto hfss ocontemptend osaltadvect osaltdiff ocontempdiff ocontempmint osaltpmdiff ocontempadvect ocontemppmdiff rlntds rsdo rsdoabsorb osalttend pbo prsn rsntds sbs_abs sbt_con snow_ai_cea sohfcisf soicecov snowpre sohflisf so_abs somxl010 somint_abs sosafldo sos_abs sossq_abs strd_bbl_li strd_atf_li strd_evd_li sowaflup sowindsp sowflisf somixhgt somxzint1 sfdsi thetao_con tossq_con tnpeo tos_con time_centered_bounds zossq time_counter_bounds ttrd_bbl_li vowflisf zos ttrd_evd_li ttrd_atf_li vohfcisf ttrd_qns_li vohflisf
noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T1y_3d	T1y_3d Icechunk repository	gn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	51 variables hfls evs friver empmr hfempds berg_latent_heat_flux ficeberg hfevapds fsitherm hfds hflx_rnf hfns hfss mlotstmax mlotstsq hfto hfsr hfrainds mlotstmin mlotst prsn sbt_con sbs_abs pbo ocontempmint rlntds rsntds snow_ai_cea sohfcisf somint_abs somixhgt soicecov sfdsi sowflisf somxl010 somxzint1 sohflisf sossq_abs sowaflup time_centered_bounds snowpre time_counter_bounds sos_abs tnpeo sosafldo sowindsp tossq_con zos tos_con ttrd_qns_li zossq
noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T1m_3d	T1m_3d Icechunk repository	gn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	51 variables evs empmr ficeberg fsitherm hfds hfls berg_latent_heat_flux hfevapds hfempds friver hflx_rnf hfsr mlotst hfss hfrainds hfto hfns mlotstsq mlotstmax pbo rlntds ocontempmint prsn rsntds mlotstmin sbs_abs sbt_con snowpre sohfcisf somixhgt soicecov snow_ai_cea sfdsi somint_abs sohflisf somxl010 sosafldo sos_abs sossq_abs sowaflup time_counter_bounds somxzint1 sowindsp time_centered_bounds tos_con ttrd_qns_li tossq_con zos sowflisf zossq tnpeo
noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/T5d_3d	T5d_3d Icechunk repository	gn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	50 variables hfls evs empmr fsitherm berg_latent_heat_flux hfds hfevapds hfss friver hfrainds ficeberg hfto hfempds hfns hfsr mlotst hflx_rnf rlntds ocontempmint mlotstmax mlotstmin rsntds sbs_abs snow_ai_cea sbt_con sfdsi snowpre sohfcisf mlotstsq prsn pbo somixhgt sowaflup somint_abs sowindsp sosafldo sohflisf soicecov sowflisf somxl010 time_centered_bounds tossq_con sossq_abs tos_con somxzint1 time_counter_bounds tnpeo sos_abs zos zossq
noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/T1y_3d	T1y_3d Icechunk repository	gn	1976-01-01T00:00:00Z	2025-06-30T00:00:00Z	50 variables berg_latent_heat_flux ficeberg friver hfds empmr fsitherm hfls evs hfevapds hfempds hfns mlotstmax hfsr mlotstsq hfto hflx_rnf mlotst mlotstmin hfrainds pbo sbt_con rlntds ocontempmint hfss sbs_abs sohflisf prsn rsntds sohfcisf snow_ai_cea sfdsi sos_abs somxzint1 soicecov sossq_abs sosafldo somixhgt time_centered_bounds sowflisf sowaflup somxl010 snowpre somint_abs sowindsp time_counter_bounds tos_con tnpeo zossq zos tossq_con
noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/T1m_3d	T1m_3d Icechunk repository	gn	1976-01-01T00:00:00Z	2025-06-30T00:00:00Z	50 variables berg_latent_heat_flux hfevapds friver ficeberg evs hfls hfempds empmr fsitherm mlotstmax hfsr hflx_rnf hfss hfto mlotst mlotstmin hfrainds hfns hfds ocontempmint sbt_con mlotstsq rlntds pbo rsntds prsn sfdsi sohflisf somint_abs sbs_abs snowpre sohfcisf somxl010 snow_ai_cea soicecov somixhgt sos_abs sossq_abs sowindsp time_centered_bounds sosafldo tnpeo somxzint1 sowflisf time_counter_bounds tos_con sowaflup tossq_con zos zossq
noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/T5d_3d	T5d_3d Icechunk repository	gn	1990-01-01T00:00:00Z	2025-06-30T00:00:00Z	50 variables berg_latent_heat_flux hfds hfevapds hflx_rnf fsitherm hfempds friver empmr evs ficeberg hfls hfns mlotstsq hfto mlotst mlotstmin mlotstmax hfsr hfss hfrainds pbo prsn rsntds sbt_con sfdsi sbs_abs ocontempmint rlntds snow_ai_cea sohfcisf somixhgt somint_abs somxl010 soicecov sohflisf snowpre sosafldo sowaflup sowindsp time_centered_bounds somxzint1 sowflisf sossq_abs sos_abs time_counter_bounds tos_con zos tnpeo tossq_con zossq

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

• To view a STAC Item in detail, we can pass the Item ID to the .item_summary() method.

In [6]:

catalog.item_summary(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y')

catalog.item_summary(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y')

Out[6]:

noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y noc-npd-era5

Platform gn

Start 1976-01-01T00:00:00Z

End 2025-07-31T00:00:00Z

BBox (-180.00, -90.00, 180.00, 90.00)

Title	Description
T1y Icechunk repository	Annual mean global ocean physics outputs defined at NEMO model T-points.

Access

catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y') Copy

Properties

Property	Value
variant	r1i1c1f1
variables	74 items empmr e3t hfempds hfds fsitherm hflx_rnf hfns hfrainds hfls ficeberg friver berg_latent_heat_flux mlotst hfss hfto hfsr hfevapds deptht_bounds mlotstmax evs mlotstsq ocontempdiff osaltpmdiff osaltdiff mlotstmin ocontempmint rlntds ocontempadvect osalttend rsdo prsn osaltadvect ocontemptend pbo sbt_con snow_ai_cea sbs_abs rsntds sfdsi so_abs ocontemppmdiff sohfcisf somixhgt sos_abs sosafldo soicecov somint_abs sohflisf rsdoabsorb snowpre strd_atf_li sowaflup sowflisf strd_bbl_li thetao_con somxl010 time_centered_bounds strd_evd_li somxzint1 ttrd_evd_li ttrd_atf_li sowindsp time_counter_bounds tos_con tossq_con tnpeo ttrd_bbl_li sossq_abs ttrd_qns_li zossq vohflisf zos vowflisf vohfcisf
variable_standard_names	74 items water_flux_out_of_sea_ice_and_sea_water cell_thickness ocean_surface_downward_heat_flux_from_E-P ocean_surface_downward_total_heat_flux fsitherm temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water surface_net_downward_non_solar_heat_flux temperature_flux_due_to_rain_expressed_as_heat_flux_into_sea_water ocean_surface_downward_latent_heat_flux water_flux_into_sea_water_from_icebergs water_flux_into_sea_water_from_rivers latent_heat_flux_from_icebergs ocean_mixed_layer_thickness_defined_by_sigma_theta ocean_surface_downward_sensible_heat_flux surface_net_downward_total_heat_flux surface_net_downward_solar_heat_flux temperature_flux_due_to_evaporation_expressed_as_heat_flux_out_of_sea_water deptht_bounds ocean_mixed_layer_thickness_defined_by_sigma_theta water_evaporation_flux square_of_ocean_mixed_layer_thickness_defined_by_sigma_theta ocontempdiff osaltpmdiff osaltdiff ocean_mixed_layer_thickness_defined_by_sigma_theta integral_wrt_depth_of_product_of_density_and_conservative_temperature ocean_surface_net_downward_longwave_heat_flux ocontempadvect osalttend downwelling_shortwave_flux_in_sea_water snowfall_flux osaltadvect ocontemptend sea_water_pressure_at_sea_floor sbt_con snowfall_flux sbs_abs ocean_surface_net_downward_shortwave_heat_flux downward_sea_ice_basal_salt_flux sea_water_absolute_salinity ocontemppmdiff ocean_mixed_layer_thickness_defined_by_vertical_tracer_diffusivity sea_surface_salinity salt_flux_into_sea_water sea_ice_area_fraction integral_wrt_depth_of_product_of_density_and_absolute_salinity rsdoabsorb snowfall_flux strd_atf_li water_flux_out_of_sea_ice_and_sea_water strd_bbl_li sea_water_conservative_temperature ocean_mixed_layer_thickness_defined_by_sigma_theta time_centered_bounds strd_evd_li ocean_mixed_layer_thickness_defined_by_sigma_theta ttrd_evd_li ttrd_atf_li wind_speed time_counter_bounds sea_surface_temperature square_of_sea_surface_temperature tnpeo ttrd_bbl_li square_of_sea_surface_Salinity ttrd_qns_li square_of_sea_surface_height_above_geoid sea_surface_height_above_geoid
dimensions	5 items time_counter y x deptht axis_nbounds
operation	mean
operation_frequency	annual
ocean_component	NEMO v4.2.2
sea_ice_component	SI3 v4.0
biogeochemistry_component	—
atmosphere_component	—
status	ongoing
update_frequency	quarterly
latest_data_update	2026-03-22T21:41:49.874907

Assets

Key	Media Type	Endpoint	Bucket	Prefix
T1y	application/vnd.zarr+icechunk	https://noc-msm-o.s3-ext.jc.rl.ac.uk	npd-eorca1-era5v1	T1y

• Alternatively, we can explore the first Item in our search results by accessing the Item attribute of the catalog. This Item 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 [7]:

catalog.Items[0]

catalog.Items[0]

Out[7]:

<Item id=noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y>

• type "Feature"
• stac_version "1.1.0"
• stac_extensions[] 0 items
• id "noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y"
• geometry

  • type "Polygon"
  • coordinates[] 1 items

    • 0[] 5 items

      • 0[] 2 items

        • 0 -180.0

        • 1 -90.0

      • 1[] 2 items

        • 0 180.0

        • 1 -90.0

      • 2[] 2 items

        • 0 180.0

        • 1 90.0

      • 3[] 2 items

        • 0 -180.0

        • 1 90.0

      • 4[] 2 items

        • 0 -180.0

        • 1 -90.0
• bbox[] 4 items

  • 0 -180.0

  • 1 -90.0

  • 2 180.0

  • 3 90.0
• properties

  • title "T1y Icechunk repository"
  • description "**Annual mean global ocean physics outputs defined at NEMO model T-points.** **OceanDataCatalog Access:** `catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y')`"
  • variant "r1i1c1f1"
  • platform "gn"
  • variables[] 74 items

    • 0 "empmr"

    • 1 "e3t"

    • 2 "hfempds"

    • 3 "hfds"

    • 4 "fsitherm"

    • 5 "hflx_rnf"

    • 6 "hfns"

    • 7 "hfrainds"

    • 8 "hfls"

    • 9 "ficeberg"

    • 10 "friver"

    • 11 "berg_latent_heat_flux"

    • 12 "mlotst"

    • 13 "hfss"

    • 14 "hfto"

    • 15 "hfsr"

    • 16 "hfevapds"

    • 17 "deptht_bounds"

    • 18 "mlotstmax"

    • 19 "evs"

    • 20 "mlotstsq"

    • 21 "ocontempdiff"

    • 22 "osaltpmdiff"

    • 23 "osaltdiff"

    • 24 "mlotstmin"

    • 25 "ocontempmint"

    • 26 "rlntds"

    • 27 "ocontempadvect"

    • 28 "osalttend"

    • 29 "rsdo"

    • 30 "prsn"

    • 31 "osaltadvect"

    • 32 "ocontemptend"

    • 33 "pbo"

    • 34 "sbt_con"

    • 35 "snow_ai_cea"

    • 36 "sbs_abs"

    • 37 "rsntds"

    • 38 "sfdsi"

    • 39 "so_abs"

    • 40 "ocontemppmdiff"

    • 41 "sohfcisf"

    • 42 "somixhgt"

    • 43 "sos_abs"

    • 44 "sosafldo"

    • 45 "soicecov"

    • 46 "somint_abs"

    • 47 "sohflisf"

    • 48 "rsdoabsorb"

    • 49 "snowpre"

    • 50 "strd_atf_li"

    • 51 "sowaflup"

    • 52 "sowflisf"

    • 53 "strd_bbl_li"

    • 54 "thetao_con"

    • 55 "somxl010"

    • 56 "time_centered_bounds"

    • 57 "strd_evd_li"

    • 58 "somxzint1"

    • 59 "ttrd_evd_li"

    • 60 "ttrd_atf_li"

    • 61 "sowindsp"

    • 62 "time_counter_bounds"

    • 63 "tos_con"

    • 64 "tossq_con"

    • 65 "tnpeo"

    • 66 "ttrd_bbl_li"

    • 67 "sossq_abs"

    • 68 "ttrd_qns_li"

    • 69 "zossq"

    • 70 "vohflisf"

    • 71 "zos"

    • 72 "vowflisf"

    • 73 "vohfcisf"
  • variable_standard_names[] 74 items

    • 0 "water_flux_out_of_sea_ice_and_sea_water"

    • 1 "cell_thickness"

    • 2 "ocean_surface_downward_heat_flux_from_E-P"

    • 3 "ocean_surface_downward_total_heat_flux"

    • 4 "fsitherm"

    • 5 "temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water"

    • 6 "surface_net_downward_non_solar_heat_flux"

    • 7 "temperature_flux_due_to_rain_expressed_as_heat_flux_into_sea_water"

    • 8 "ocean_surface_downward_latent_heat_flux"

    • 9 "water_flux_into_sea_water_from_icebergs"

    • 10 "water_flux_into_sea_water_from_rivers"

    • 11 "latent_heat_flux_from_icebergs"

    • 12 "ocean_mixed_layer_thickness_defined_by_sigma_theta"

    • 13 "ocean_surface_downward_sensible_heat_flux"

    • 14 "surface_net_downward_total_heat_flux"

    • 15 "surface_net_downward_solar_heat_flux"

    • 16 "temperature_flux_due_to_evaporation_expressed_as_heat_flux_out_of_sea_water"

    • 17 "deptht_bounds"

    • 18 "ocean_mixed_layer_thickness_defined_by_sigma_theta"

    • 19 "water_evaporation_flux"

    • 20 "square_of_ocean_mixed_layer_thickness_defined_by_sigma_theta"

    • 21 "ocontempdiff"

    • 22 "osaltpmdiff"

    • 23 "osaltdiff"

    • 24 "ocean_mixed_layer_thickness_defined_by_sigma_theta"

    • 25 "integral_wrt_depth_of_product_of_density_and_conservative_temperature"

    • 26 "ocean_surface_net_downward_longwave_heat_flux"

    • 27 "ocontempadvect"

    • 28 "osalttend"

    • 29 "downwelling_shortwave_flux_in_sea_water"

    • 30 "snowfall_flux"

    • 31 "osaltadvect"

    • 32 "ocontemptend"

    • 33 "sea_water_pressure_at_sea_floor"

    • 34 "sbt_con"

    • 35 "snowfall_flux"

    • 36 "sbs_abs"

    • 37 "ocean_surface_net_downward_shortwave_heat_flux"

    • 38 "downward_sea_ice_basal_salt_flux"

    • 39 "sea_water_absolute_salinity"

    • 40 "ocontemppmdiff"

    • 41 ""

    • 42 "ocean_mixed_layer_thickness_defined_by_vertical_tracer_diffusivity"

    • 43 "sea_surface_salinity"

    • 44 "salt_flux_into_sea_water"

    • 45 "sea_ice_area_fraction"

    • 46 "integral_wrt_depth_of_product_of_density_and_absolute_salinity"

    • 47 ""

    • 48 "rsdoabsorb"

    • 49 "snowfall_flux"

    • 50 "strd_atf_li"

    • 51 "water_flux_out_of_sea_ice_and_sea_water"

    • 52 ""

    • 53 "strd_bbl_li"

    • 54 "sea_water_conservative_temperature"

    • 55 "ocean_mixed_layer_thickness_defined_by_sigma_theta"

    • 56 "time_centered_bounds"

    • 57 "strd_evd_li"

    • 58 "ocean_mixed_layer_thickness_defined_by_sigma_theta"

    • 59 "ttrd_evd_li"

    • 60 "ttrd_atf_li"

    • 61 "wind_speed"

    • 62 "time_counter_bounds"

    • 63 "sea_surface_temperature"

    • 64 "square_of_sea_surface_temperature"

    • 65 "tnpeo"

    • 66 "ttrd_bbl_li"

    • 67 "square_of_sea_surface_Salinity"

    • 68 "ttrd_qns_li"

    • 69 "square_of_sea_surface_height_above_geoid"

    • 70 ""

    • 71 "sea_surface_height_above_geoid"

    • 72 ""

    • 73 ""
  • dimensions[] 5 items

    • 0 "time_counter"

    • 1 "y"

    • 2 "x"

    • 3 "deptht"

    • 4 "axis_nbounds"
  • operation "mean"
  • operation_frequency "annual"
  • ocean_component "NEMO v4.2.2"
  • sea_ice_component "SI3 v4.0"
  • biogeochemistry_component None
  • atmosphere_component None
  • status "ongoing"
  • update_frequency "quarterly"
  • latest_data_update "2026-03-22T21:41:49.874907"
  • start_datetime "1976-01-01T00:00:00Z"
  • end_datetime "2025-07-31T00:00:00Z"
  • datetime "2000-01-01T00:00:00Z"
• links[] 3 items

  • 0

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

  • 1

    • rel "self"
    • href "https://noc-msm-o.s3-ext.jc.rl.ac.uk/oceandatastore/noc-stac/noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y/noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y.json"
    • type "application/json"

  • 2

    • rel "parent"
    • href "https://noc-msm-o.s3-ext.jc.rl.ac.uk/oceandatastore/noc-stac/noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/catalog.json"
    • type "application/json"
    • title "eORCA1 ERA5v1 NPD: Global Native Model Grid Catalog"
• assets

  • T1y

    • href "https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-era5v1/T1y"
    • type "application/vnd.zarr+icechunk"
    • title "eORCA1 ERA5v1 NPD: T1y Icechunk repository"
    • description "**Annual mean global ocean physics outputs defined at NEMO model T-points.** **OceanDataCatalog Access:** `catalog.open_dataset(id='noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/T1y')`"
    • endpoint_url "https://noc-msm-o.s3-ext.jc.rl.ac.uk"
    • bucket "npd-eorca1-era5v1"
    • prefix "T1y"
    • variant "r1i1c1f1"
    • anonymous True
• collection "noc-npd-era5"

• Next, let's open a subset (1980-1990) of the annual-mean SST data as an xarray.Dataset by using the .open_dataset() method 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 [8]:

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[8]:

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

xarray.Dataset

• Dimensions:
  • time_counter: 11
  • y: 331
  • x: 360
  • deptht: 75
  • axis_nbounds: 2
• Coordinates: (5)
  • 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]')

  • 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

  • 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

  • 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)

• Data variables: (74)
  • 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

  • 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

  • deptht_bounds
    (deptht, axis_nbounds)
    float32
    dask.array<chunksize=(25, 2), meta=np.ndarray>

    units :

    m

    Array Chunk Bytes 600 B 200 B Shape (75, 2) (25, 2) Dask graph 3 chunks in 2 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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_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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

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

In [9]:

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

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

Out[9]:

<matplotlib.collections.QuadMesh at 0x153ffb7086e0>

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 [10]:

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

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

Out[10]:

Search Results 12 Items found noc-npd-era5

Item ID	Title	Platform	Start Date	End Date	Variables
noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/MOVE_16N	M1m/MOVE_16N Icechunk repository	tn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	34 variables dh dz dx fw_int fw_model fw_int_model q_int q_int_model moc_move q_bdry fw_move q_model fw_bdry moc_model rho q_move s_move t_int t_bdry_fwt t_move s_bdry_fwt s_int temp salt trans_bdry trans_int v v_bdry v_move v_model trans_int_model vgeo v_int_model v_int
noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/SAMBA_34_5S	M1m/SAMBA_34_5S Icechunk repository	tn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	58 variables dx dh fw_ek fw_geoint fw_eddy fw_gyre_model ekman dz ebw fw_ebw fw_net_model fw_ot_model geoint fw_wbw fw_net_samba fw_mo fw_sum_model fw_sum_samba fw_gyre_samba mocmax_model mocmax_samba fw_ot_samba moc_samba int_mod moc_model q_gyre_model q_eddy q_ebw q_geoint q_net_model q_ot_model q_net_samba q_mo s_basin q_wbw rho q_sum_samba q_sum_model salt q_gyre_samba q_ot_samba sf_ek t_basin sf_wbw sf_int_mod q_ek sf_mo sf_ebw sf_model sf_samba umo sf_geoint v_basin_model v_basin_samba vgeo v wbw temp
noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/RAPID_26N	M1m/RAPID_26N Icechunk repository	tn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	62 variables fw_fc fc dh dz fw_eddy fw_geoint ekman dx fw_gyre_model fw_ek fw_gyre_rapid fw_ot_rapid fw_net_model fw_wbw geoint fw_ot_model fw_net_rapid moc_rapid fw_mo int_mod fw_sum_rapid fw_sum_model mocmax_model moc_model mass_balance q_fc q_net_model q_gyre_model q_ek q_eddy q_mo q_net_rapid mocmax_rapid q_gyre_rapid q_geoint salt q_sum_model q_wbw rho s_fc_fwt q_ot_rapid sf_ek q_sum_rapid q_ot_model s_basin sf_wbw sf_int_mod umo sf_geoint sf_rapid t_basin temp t_fc_fwt sf_fc sf_mo sf_model v_basin_rapid vgeo wbw v v_fc v_basin_model
noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/tn/M1m/OSNAP	M1m/OSNAP Icechunk repository	tn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	14 variables e3b i_bdy flux_dir moc_east moc_west e1b moc_total sigma0 so_abs velocity flux_type volume_transport j_bdy thetao_con
noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/tn/M1m/MOVE_16N	M1m/MOVE_16N Icechunk repository	tn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	34 variables fw_int dz fw_bdry fw_model moc_model moc_move dx fw_move fw_int_model dh q_bdry q_int s_bdry_fwt q_move s_move rho q_model s_int q_int_model salt t_int t_bdry_fwt trans_bdry t_move trans_int v_int v_int_model v temp v_bdry trans_int_model vgeo v_model v_move
noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/tn/M1m/SAMBA_34_5S	M1m/SAMBA_34_5S Icechunk repository	tn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	58 variables dh dz fw_gyre_model fw_gyre_samba ekman fw_ebw fw_geoint dx fw_mo fw_eddy fw_ek fw_ot_samba fw_sum_model fw_sum_samba ebw fw_wbw fw_ot_model fw_net_samba int_mod moc_samba mocmax_samba q_ebw fw_net_model q_eddy moc_model mocmax_model geoint q_gyre_samba q_ek q_mo q_net_samba q_geoint q_ot_samba q_sum_samba q_wbw s_basin q_ot_model salt q_net_model sf_ebw rho q_gyre_model q_sum_model sf_wbw temp sf_geoint sf_mo sf_int_mod sf_ek sf_model t_basin sf_samba v_basin_samba wbw v umo vgeo v_basin_model
noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/tn/M1m/RAPID_26N	M1m/RAPID_26N Icechunk repository	tn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	62 variables fw_gyre_model dz ekman dx fw_ek fw_fc fw_geoint fw_eddy dh fc fw_sum_rapid fw_net_rapid fw_wbw fw_mo fw_gyre_rapid geoint fw_net_model fw_ot_model fw_sum_model fw_ot_rapid int_mod mocmax_rapid q_geoint mocmax_model moc_rapid q_ek mass_balance q_fc q_eddy q_gyre_model moc_model q_net_model q_sum_model q_sum_rapid q_wbw q_mo q_gyre_rapid q_net_rapid q_ot_rapid q_ot_model salt s_basin sf_mo sf_model t_basin rho s_fc_fwt sf_rapid sf_ek sf_wbw sf_fc sf_geoint sf_int_mod t_fc_fwt vgeo v_basin_model v_basin_rapid umo temp v_fc wbw v
noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/tn/M1m/OSNAP	M1m/OSNAP Icechunk repository	tn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	14 variables flux_dir j_bdy flux_type e1b e3b i_bdy moc_east moc_total so_abs moc_west sigma0 velocity thetao_con volume_transport
noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/tn/M1m/MOVE_16N	M1m/MOVE_16N Icechunk repository	tn	1976-01-01T00:00:00Z	2024-12-31T00:00:00Z	34 variables dh moc_model moc_move fw_bdry dz fw_move q_bdry q_int fw_int_model dx s_move q_model s_bdry_fwt q_move s_int fw_int fw_model salt rho q_int_model trans_int_model t_move trans_bdry trans_int temp v_bdry v t_bdry_fwt t_int v_model v_int v_move vgeo v_int_model
noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/tn/M1m/SAMBA_34_5S	M1m/SAMBA_34_5S Icechunk repository	tn	1976-01-01T00:00:00Z	2024-12-31T00:00:00Z	58 variables dh ekman fw_gyre_model dz fw_gyre_samba fw_geoint dx fw_ek fw_ebw fw_eddy fw_net_model fw_net_samba fw_mo ebw fw_ot_model fw_sum_samba fw_wbw moc_model moc_samba fw_sum_model geoint mocmax_model int_mod fw_ot_samba q_ek q_eddy q_mo q_ebw mocmax_samba q_net_samba q_net_model q_geoint q_gyre_model q_gyre_samba q_wbw q_sum_model rho sf_geoint salt sf_ebw q_ot_samba q_sum_samba s_basin sf_int_mod sf_ek umo temp q_ot_model v t_basin sf_samba wbw sf_mo v_basin_samba sf_wbw v_basin_model vgeo sf_model
noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/tn/M1m/RAPID_26N	M1m/RAPID_26N Icechunk repository	tn	1976-01-01T00:00:00Z	2024-12-31T00:00:00Z	62 variables dh dz ekman dx fw_ek fw_gyre_rapid fc fw_net_model fw_geoint fw_mo fw_eddy fw_net_rapid fw_ot_model fw_gyre_model fw_fc fw_wbw geoint fw_ot_rapid moc_rapid fw_sum_rapid moc_model mass_balance q_eddy q_fc q_geoint fw_sum_model int_mod mocmax_model q_gyre_rapid q_net_model q_ot_model q_gyre_model q_ek q_wbw mocmax_rapid q_net_rapid q_sum_model q_ot_rapid s_basin rho q_sum_rapid salt q_mo sf_geoint sf_mo s_fc_fwt sf_int_mod sf_model t_basin sf_ek sf_wbw sf_rapid sf_fc v_basin_model vgeo t_fc_fwt temp v_fc v_basin_rapid umo wbw v
noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/tn/M1m/OSNAP	M1m/OSNAP Icechunk repository	tn	1976-01-01T00:00:00Z	2024-12-31T00:00:00Z	14 variables e3b e1b i_bdy flux_type sigma0 moc_east j_bdy volume_transport velocity moc_total thetao_con moc_west flux_dir so_abs

• 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 [11]:

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

ds_rapid

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

Out[11]:

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

xarray.Dataset

• Dimensions:
  • time: 595
  • z: 75
  • xbounds: 72
  • x: 71
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1976-01-16T12:00:00 ... 2025-07-...

    array(['1976-01-16T12:00:00.000000000', '1976-02-15T12:00:00.000000000',
           '1976-03-16T12:00:00.000000000', ..., '2025-05-16T12:00:00.000000000',
           '2025-06-16T00:00:00.000000000', '2025-07-16T12:00:00.000000000'],
          shape=(595,), dtype='datetime64[ns]')

  • 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])

  • 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])

• Data variables: (62)
  • dh
    (time, z, xbounds)
    float64
    dask.array<chunksize=(1, 75, 72), meta=np.ndarray>

    units :

    m2/s2

    comment :

    dynamic heights

    Array Chunk Bytes 24.51 MiB 42.19 kiB Shape (595, 75, 72) (1, 75, 72) Dask graph 595 chunks in 2 graph layers Data type float64 numpy.ndarray

  • ekman
    (time)
    float64
    dask.array<chunksize=(298,), meta=np.ndarray>

    units :

    Sv

    minimum_longitude :

    -70.0

    maximum_longitude :

    -10.0

    comment :

    Ekman transport time series (streamfunction at 1000m)

    Array Chunk Bytes 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • dx
    (x)
    float64
    dask.array<chunksize=(71,), meta=np.ndarray>

    units :

    m

    Array Chunk Bytes 568 B 568 B Shape (71,) (71,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fc
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_fc
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_ek
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

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

    units :

    m

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

  • fw_geoint
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_gyre_model
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_eddy
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_net_model
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_net_rapid
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_sum_rapid
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_ot_model
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_wbw
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_sum_model
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • geoint
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_ot_rapid
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_mo
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • int_mod
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • fw_gyre_rapid
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mass_balance
    (time)
    float64
    dask.array<chunksize=(298,), meta=np.ndarray>

    units :

    Sv

    comment :

    Rapid mass balance term

    Array Chunk Bytes 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • moc_model
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mocmax_model
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_eddy
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_mo
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mocmax_rapid
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • moc_rapid
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_gyre_rapid
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_fc
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_ek
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_geoint
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_wbw
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_sum_rapid
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_ot_rapid
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 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 348.63 kiB 600 B Shape (595, 75) (1, 75) Dask graph 595 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 24.51 MiB 42.19 kiB Shape (595, 75, 72) (1, 75, 72) Dask graph 595 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_net_rapid
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_net_model
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_ot_model
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_gyre_model
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_sum_model
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_fc_fwt
    (time)
    float64
    dask.array<chunksize=(298,), meta=np.ndarray>

    units :

    PSU

    minimum_longitude :

    -81.0

    maximum_longitude :

    -73.5

    comment :

    Florida current flow-weighted salinity

    Array Chunk Bytes 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 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 348.63 kiB 600 B Shape (595, 75) (1, 75) Dask graph 595 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 348.63 kiB 600 B Shape (595, 75) (1, 75) Dask graph 595 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 348.63 kiB 600 B Shape (595, 75) (1, 75) Dask graph 595 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 348.63 kiB 600 B Shape (595, 75) (1, 75) Dask graph 595 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 348.63 kiB 600 B Shape (595, 75) (1, 75) Dask graph 595 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 348.63 kiB 600 B Shape (595, 75) (1, 75) Dask graph 595 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 24.17 MiB 41.60 kiB Shape (595, 75, 71) (1, 75, 71) Dask graph 595 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 348.63 kiB 600 B Shape (595, 75) (1, 75) Dask graph 595 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 348.63 kiB 600 B Shape (595, 75) (1, 75) Dask graph 595 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 348.63 kiB 600 B Shape (595, 75) (1, 75) Dask graph 595 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 24.17 MiB 41.60 kiB Shape (595, 75, 71) (1, 75, 71) Dask graph 595 chunks in 2 graph layers Data type float64 numpy.ndarray

  • umo
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) 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 24.17 MiB 41.60 kiB Shape (595, 75, 71) (1, 75, 71) Dask graph 595 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 348.63 kiB 600 B Shape (595, 75) (1, 75) Dask graph 595 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 348.63 kiB 600 B Shape (595, 75) (1, 75) Dask graph 595 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 24.17 MiB 41.60 kiB Shape (595, 75, 71) (1, 75, 71) Dask graph 595 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 348.63 kiB 600 B Shape (595, 75) (1, 75) Dask graph 595 chunks in 2 graph layers Data type float64 numpy.ndarray

  • t_fc_fwt
    (time)
    float64
    dask.array<chunksize=(298,), meta=np.ndarray>

    units :

    degC

    minimum_longitude :

    -81.0

    maximum_longitude :

    -73.5

    comment :

    Florida current flow-weighted potential temperature

    Array Chunk Bytes 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • wbw
    (time)
    float64
    dask.array<chunksize=(298,), 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 4.65 kiB 2.33 kiB Shape (595,) (298,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

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 [12]:

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

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

Out[12]:

Search Results 3 Items found noc-npd-era5

Item ID	Title	Platform	Start Date	End Date	Variables
noc-npd-era5/npd-eorca1-era5v1/r1i1c1f1/gn/domain/domain_cfg	domain/domain_cfg Icechunk repository	gn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	49 variables atlmsk bottom_level e1t e1f e2f e2t bathy_metry e2u e1u e1v e3t_0 e3t_1d e3uw_0 e3v_0 e3f_0 e3w_0 e3w_1d e3u_0 e3vw_0 gdept_1d e2v glamt gdepw_0 gdept_0 glamf gdepw_1d fmask ff_t ff_f gphif glamv gphiu gphiv misf glamu mask_opensea mbathy gphit top_level tmask umaskutil pacmsk umask vmaskutil tmaskutil wmask vmask indmsk socmsk
noc-npd-era5/npd-eorca025-era5v1/r1i1c1f1/gn/domain/domain_cfg	domain/domain_cfg Icechunk repository	gn	1976-01-01T00:00:00Z	2025-07-31T00:00:00Z	49 variables bottom_level e2f e1t e1f bathy_metry e2u e1u atlmsk e2t e1v e3t_0 e3w_1d e3w_0 e3f_0 e3u_0 e3uw_0 e3v_0 fmask gdept_0 ff_f ff_t e2v gdepw_0 e3t_1d gdept_1d gdepw_1d e3vw_0 glamu mask_opensea glamt glamf gphiv gphif gphit indmsk glamv gphiu pacmsk umaskutil top_level socmsk tmaskutil misf mbathy vmask tmask umask vmaskutil wmask
noc-npd-era5/npd-eorca12-era5v1/r1i1c1f1/gn/domain/domain_cfg	domain/domain_cfg Icechunk repository	gn	1990-01-01T00:00:00Z	2025-06-30T00:00:00Z	57 variables e2v e1t e1v e2u bathy_metry e3f_0 e1u e2f e1f e3u_0 e2t e3t_1d e3t_0 e3uw_0 e3w_0 e3w_1d fmask bottom_level e3v_0 ff_t e3vw_0 glamf fmaskutil ff_f glamu glamv gphiv gphiu gphif glamt gphit isf_draft jperio jpiglo mask_csemp ln_zco ln_sco mask_csrnf jpjglo ln_zps mask_csgrpglo mask_csgrprnf jpkglo ln_isfcav mask_csglo mask_csgrpemp mask_csundef umask vmask vmaskutil wmask tmaskutil top_level umaskutil mask_opensea wmaskutil tmask

• 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 [13]:

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

ds_domain_cfg

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

Out[13]:

<xarray.Dataset> Size: 710MB
Dimensions:       (y: 331, x: 360, nav_lev: 75)
Coordinates:
  * y             (y) int64 3kB 0 1 2 3 4 5 6 7 ... 324 325 326 327 328 329 330
  * x             (x) int64 3kB 0 1 2 3 4 5 6 7 ... 353 354 355 356 357 358 359
  * nav_lev       (nav_lev) int64 600B 0 1 2 3 4 5 6 7 ... 68 69 70 71 72 73 74
Data variables: (12/49)
    e1u           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    atlmsk        (y, x) float32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    e1t           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    e1v           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    e2t           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    bottom_level  (y, x) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    ...            ...
    socmsk        (y, x) float32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    vmaskutil     (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    gphiv         (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    pacmsk        (y, x) float32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    umaskutil     (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    vmask         (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>
Attributes:
    CfgName:    UNKNOWN
    CfgIndex:   -999
    Iperio:     1
    Jperio:     0
    NFold:      1
    NFtype:     F
    VertCoord:  zps
    IsfCav:     0
    file_name:  mesh_mask.nc
    TimeStamp:  01/03/2025 22:19:49 -0000

xarray.Dataset

• Dimensions:
  • y: 331
  • x: 360
  • nav_lev: 75
• Coordinates: (3)
  • y
    (y)
    int64
    0 1 2 3 4 5 ... 326 327 328 329 330

    array([  0,   1,   2, ..., 328, 329, 330], shape=(331,))

  • x
    (x)
    int64
    0 1 2 3 4 5 ... 355 356 357 358 359

    array([  0,   1,   2, ..., 357, 358, 359], shape=(360,))

  • nav_lev
    (nav_lev)
    int64
    0 1 2 3 4 5 6 ... 69 70 71 72 73 74

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
           54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
           72, 73, 74])

• Data variables: (49)
  • e1u
    (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

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

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

  • e1t
    (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

  • e1v
    (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

  • e2t
    (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

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

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

  • e1f
    (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

  • e2u
    (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

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

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

  • e2f
    (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

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

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

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

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

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

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

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

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

  • ff_t
    (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

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

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

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

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

  • ff_f
    (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

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

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

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

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

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

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

  • e2v
    (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

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

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

  • glamf
    (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

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

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

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

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

  • fmask
    (nav_lev, y, x)
    int8
    dask.array<chunksize=(75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 8.52 MiB 8.52 MiB Shape (75, 331, 360) (75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int8 numpy.ndarray

  • glamt
    (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

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

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

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

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

  • gphit
    (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

  • glamu
    (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

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

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

  • gphif
    (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

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

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

  • glamv
    (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

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

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

  • gphiu
    (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

  • tmaskutil
    (y, x)
    int8
    dask.array<chunksize=(331, 360), meta=np.ndarray>

    Array Chunk Bytes 116.37 kiB 116.37 kiB Shape (331, 360) (331, 360) Dask graph 1 chunks in 2 graph layers Data type int8 numpy.ndarray

  • tmask
    (nav_lev, y, x)
    int8
    dask.array<chunksize=(75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 8.52 MiB 8.52 MiB Shape (75, 331, 360) (75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int8 numpy.ndarray

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

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

  • wmask
    (nav_lev, y, x)
    bool
    dask.array<chunksize=(75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 8.52 MiB 8.52 MiB Shape (75, 331, 360) (75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type bool numpy.ndarray

  • umask
    (nav_lev, y, x)
    int8
    dask.array<chunksize=(75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 8.52 MiB 8.52 MiB Shape (75, 331, 360) (75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int8 numpy.ndarray

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

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

  • vmaskutil
    (y, x)
    int8
    dask.array<chunksize=(331, 360), meta=np.ndarray>

    Array Chunk Bytes 116.37 kiB 116.37 kiB Shape (331, 360) (331, 360) Dask graph 1 chunks in 2 graph layers Data type int8 numpy.ndarray

  • gphiv
    (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

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

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

  • umaskutil
    (y, x)
    int8
    dask.array<chunksize=(331, 360), meta=np.ndarray>

    Array Chunk Bytes 116.37 kiB 116.37 kiB Shape (331, 360) (331, 360) Dask graph 1 chunks in 2 graph layers Data type int8 numpy.ndarray

  • vmask
    (nav_lev, y, x)
    int8
    dask.array<chunksize=(75, 331, 360), meta=np.ndarray>

    Array Chunk Bytes 8.52 MiB 8.52 MiB Shape (75, 331, 360) (75, 331, 360) Dask graph 1 chunks in 2 graph layers Data type int8 numpy.ndarray

• Attributes: (10)

  CfgName :

  UNKNOWN

  CfgIndex :

  -999

  Iperio :

  1

  Jperio :

  0

  NFold :

  1

  NFtype :

  F

  VertCoord :

  zps

  IsfCav :

  0

  file_name :

  mesh_mask.nc

  TimeStamp :

  01/03/2025 22:19:49 -0000