GFDL_CM2_6_control_ocean_surface

GFDL CM2.6 climate model control run daily ocean surface fields

Load in Python

from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean/GFDL_CM2.6.yaml") ds = cat["GFDL_CM2_6_control_ocean_surface"].to_dask()

Working with requester pays data

Several of the datasets within the cloud data catalog are contained in requester pays storage buckets. This means that a user requesting data must provide their own billing project (created and authenticated through Google Cloud Platform) to be billed for the charges associated with accessing a dataset. To set up an GCP billing project and use it for authentication in applications:

Metadata

url https://www.gfdl.noaa.gov/cm2-6/
tags ['ocean', 'model']

Dataset Contents

Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • nv: 2
    • st_ocean_sub01: 1
    • time: 7305
    • xt_ocean: 3600
    • xu_ocean: 3600
    • yt_ocean: 2700
    • yu_ocean: 2700
    • nv
      (nv)
      float64
      1.0 2.0
      cartesian_axis :
      N
      long_name :
      vertex number
      units :
      none
      array([1., 2.])
    • st_ocean_sub01
      (st_ocean_sub01)
      float64
      5.034
      cartesian_axis :
      Z
      long_name :
      tcell zstar depth
      positive :
      down
      units :
      meters
      array([5.03355])
    • time
      (time)
      object
      0181-01-01 12:00:00 ... 0200-12-31 12:00:00
      bounds :
      time_bounds
      calendar_type :
      JULIAN
      cartesian_axis :
      T
      long_name :
      time
      array([cftime.DatetimeJulian(181, 1, 1, 12, 0, 0, 0),
             cftime.DatetimeJulian(181, 1, 2, 12, 0, 0, 0),
             cftime.DatetimeJulian(181, 1, 3, 12, 0, 0, 0), ...,
             cftime.DatetimeJulian(200, 12, 29, 12, 0, 0, 0),
             cftime.DatetimeJulian(200, 12, 30, 12, 0, 0, 0),
             cftime.DatetimeJulian(200, 12, 31, 12, 0, 0, 0)], dtype=object)
    • xt_ocean
      (xt_ocean)
      float64
      -279.9 -279.8 ... 79.85 79.95
      cartesian_axis :
      X
      long_name :
      tcell longitude
      units :
      degrees_E
      array([-279.95, -279.85, -279.75, ...,   79.75,   79.85,   79.95])
    • xu_ocean
      (xu_ocean)
      float64
      -279.9 -279.8 -279.7 ... 79.9 80.0
      cartesian_axis :
      X
      long_name :
      ucell longitude
      units :
      degrees_E
      array([-279.9, -279.8, -279.7, ...,   79.8,   79.9,   80. ])
    • yt_ocean
      (yt_ocean)
      float64
      -81.11 -81.07 ... 89.94 89.98
      cartesian_axis :
      Y
      long_name :
      tcell latitude
      units :
      degrees_N
      array([-81.108632, -81.066392, -81.024153, ...,  89.894417,  89.936657,
              89.978896])
    • yu_ocean
      (yu_ocean)
      float64
      -81.09 -81.05 -81.0 ... 89.96 90.0
      cartesian_axis :
      Y
      long_name :
      ucell latitude
      units :
      degrees_N
      array([-81.087512, -81.045273, -81.003033, ...,  89.915537,  89.957776,
              90.      ])
    • biomass_p
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      coordinates :
      geolon_t geolat_t
      long_name :
      Surface Biomass-P concentration
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      mol kg-1
      Array Chunk
      Bytes 284.02 GB 38.88 MB
      Shape (7305, 2700, 3600) (1, 2700, 3600)
      Count 7306 Tasks 7305 Chunks
      Type float32 numpy.ndarray
      3600 2700 7305
    • chl
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      coordinates :
      geolon_t geolat_t
      long_name :
      Surface Chl concentration
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      mol kg-1
      Array Chunk
      Bytes 284.02 GB 38.88 MB
      Shape (7305, 2700, 3600) (1, 2700, 3600)
      Count 7306 Tasks 7305 Chunks
      Type float32 numpy.ndarray