GFDL_CM2_6_control_ocean

GFDL CM2.6 climate model control run monthly ocean 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"].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_edges_ocean: 51
    • st_ocean: 50
    • sw_edges_ocean: 51
    • sw_ocean: 50
    • time: 240
    • xt_ocean: 3600
    • xu_ocean: 3600
    • yt_ocean: 2700
    • yu_ocean: 2700
    • geolat_c
      (yu_ocean, xu_ocean)
      float32
      dask.array<chunksize=(2700, 3600), meta=np.ndarray>
      cell_methods :
      time: point
      long_name :
      uv latitude
      units :
      degrees_N
      valid_range :
      [-91.0, 91.0]
      Array Chunk
      Bytes 38.88 MB 38.88 MB
      Shape (2700, 3600) (2700, 3600)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      3600 2700
    • geolat_t
      (yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(2700, 3600), meta=np.ndarray>
      cell_methods :
      time: point
      long_name :
      tracer latitude
      units :
      degrees_N
      valid_range :
      [-91.0, 91.0]
      Array Chunk
      Bytes 38.88 MB 38.88 MB
      Shape (2700, 3600) (2700, 3600)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      3600 2700
    • geolon_c
      (yu_ocean, xu_ocean)
      float32
      dask.array<chunksize=(2700, 3600), meta=np.ndarray>
      cell_methods :
      time: point
      long_name :
      uv longitude
      units :
      degrees_E
      valid_range :
      [-281.0, 361.0]
      Array Chunk
      Bytes 38.88 MB 38.88 MB
      Shape (2700, 3600) (2700, 3600)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      3600 2700
    • geolon_t
      (yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(2700, 3600), meta=np.ndarray>
      cell_methods :
      time: point
      long_name :
      tracer longitude
      units :
      degrees_E
      valid_range :
      [-281.0, 361.0]
      Array Chunk
      Bytes 38.88 MB 38.88 MB
      Shape (2700, 3600) (2700, 3600)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      3600 2700
    • nv
      (nv)
      float64
      1.0 2.0
      cartesian_axis :
      N
      long_name :
      vertex number
      units :
      none
      array([1., 2.])
    • st_edges_ocean
      (st_edges_ocean)
      float64
      0.0 10.07 ... 5.29e+03 5.5e+03
      cartesian_axis :
      Z
      long_name :
      tcell zstar depth edges
      positive :
      down
      units :
      meters
      array([   0.      ,   10.0671  ,   20.16    ,   30.2889  ,   40.4674  ,
               50.714802,   61.057499,   71.532303,   82.189903,   93.100098,
              104.359703,  116.101402,  128.507599,  141.827606,  156.400208,
              172.683105,  191.287704,  213.020096,  238.922699,  270.309509,
              308.779297,  356.186401,  414.545685,  485.854401,  571.842773,
              673.697571,  791.842773,  925.85437 , 1074.545654, 1236.186401,
             1408.779297, 1590.30957 , 1778.922729, 1973.020142, 2171.287598,
             2372.683105, 2576.400146, 2781.827637, 2988.507568, 3196.101562,
             3404.359619, 3613.100098, 3822.189941, 4031.532227, 4241.057617,
             4450.714844, 4660.467285, 4870.289062, 5080.160156, 5290.066895,
             5500.      ])
    • st_ocean
      (st_ocean)
      float64
      5.034 15.1 ... 5.185e+03 5.395e+03
      cartesian_axis :
      Z
      edges :
      st_edges_ocean
      long_name :
      tcell zstar depth
      positive :
      down
      units :
      meters
      array([5.033550e+00, 1.510065e+01, 2.521935e+01, 3.535845e+01, 4.557635e+01,
             5.585325e+01, 6.626175e+01, 7.680285e+01, 8.757695e+01, 9.862325e+01,
             1.100962e+02, 1.221067e+02, 1.349086e+02, 1.487466e+02, 1.640538e+02,
             1.813125e+02, 2.012630e+02, 2.247773e+02, 2.530681e+02, 2.875508e+02,
             3.300078e+02, 3.823651e+02, 4.467263e+02, 5.249824e+02, 6.187031e+02,
             7.286921e+02, 8.549935e+02, 9.967153e+02, 1.152376e+03, 1.319997e+03,
             1.497562e+03, 1.683057e+03, 1.874788e+03, 2.071252e+03, 2.271323e+03,
             2.474043e+03, 2.678757e+03, 2.884898e+03, 3.092117e+03, 3.300086e+03,
             3.508633e+03, 3.717567e+03, 3.926813e+03, 4.136251e+03, 4.345864e+03,
             4.555566e+03, 4.765369e+03, 4.975209e+03, 5.185111e+03, 5.395023e+03])
    • sw_edges_ocean
      (sw_edges_ocean)
      float64
      5.034 15.1 ... 5.395e+03 5.5e+03
      cartesian_axis :
      Z
      long_name :
      ucell zstar depth edges
      positive :
      down
      units :
      meters
      array([5.033550e+00, 1.510065e+01, 2.521935e+01, 3.535845e+01, 4.557635e+01,
             5.585325e+01, 6.626175e+01, 7.680285e+01, 8.757695e+01, 9.862325e+01,
             1.100962e+02, 1.221067e+02, 1.349086e+02, 1.487466e+02, 1.640538e+02,
             1.813125e+02, 2.012630e+02, 2.247773e+02, 2.530681e+02, 2.875508e+02,
             3.300078e+02, 3.823651e+02, 4.467263e+02, 5.249824e+02, 6.187031e+02,
             7.286921e+02, 8.549935e+02, 9.967153e+02, 1.152376e+03, 1.319997e+03,
             1.497562e+03, 1.683057e+03, 1.874788e+03, 2.071252e+03, 2.271323e+03,
             2.474043e+03, 2.678757e+03, 2.884898e+03, 3.092117e+03, 3.300086e+03,
             3.508633e+03, 3.717567e+03, 3.926813e+03, 4.136251e+03, 4.345864e+03,
             4.555566e+03, 4.765369e+03, 4.975209e+03, 5.185111e+03, 5.395023e+03,
             5.500000e+03])
    • sw_ocean
      (sw_ocean)
      float64
      10.07 20.16 ... 5.29e+03 5.5e+03
      cartesian_axis :
      Z
      edges :
      sw_edges_ocean
      long_name :
      ucell zstar depth
      positive :
      down
      units :
      meters
      array([  10.0671  ,   20.16    ,   30.2889  ,   40.4674  ,   50.714802,
               61.057499,   71.532303,   82.189903,   93.100098,  104.359703,
              116.101402,  128.507599,  141.827606,  156.400208,  172.683105,
              191.287704,  213.020096,  238.922699,  270.309509,  308.779297,
              356.186401,  414.545685,  485.854401,  571.842773,  673.697571,
              791.842773,  925.85437 , 1074.545654, 1236.186401, 1408.779297,
             1590.30957 , 1778.922729, 1973.020142, 2171.287598, 2372.683105,
             2576.400146, 2781.827637, 2988.507568, 3196.101562, 3404.359619,
             3613.100098, 3822.189941, 4031.532227, 4241.057617, 4450.714844,
             4660.467285, 4870.289062, 5080.160156, 5290.066895, 5500.      ])
    • time
      (time)
      object
      0181-01-16 12:00:00 ... 0200-12-16 12:00:00
      bounds :
      time_bounds
      calendar_type :
      JULIAN
      cartesian_axis :
      T
      long_name :
      time
      array([cftime.DatetimeJulian(181, 1, 16, 12, 0, 0, 0),
             cftime.DatetimeJulian(181, 2, 15, 0, 0, 0, 0),
             cftime.DatetimeJulian(181, 3, 16, 12, 0, 0, 0), ...,
             cftime.DatetimeJulian(200, 10, 16, 12, 0, 0, 0),
             cftime.DatetimeJulian(200, 11, 16, 0, 0, 0, 0),
             cftime.DatetimeJulian(200, 12, 16, 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.      ])
    • average_DT
      (time)
      timedelta64[ns]
      dask.array<chunksize=(12,), meta=np.ndarray>
      long_name :
      Length of average period
      Array Chunk
      Bytes 1.92 kB 96 B
      Shape (240,) (12,)
      Count 21 Tasks 20 Chunks
      Type timedelta64[ns] numpy.ndarray
      240 1
    • average_T1
      (time)
      object
      dask.array<chunksize=(12,), meta=np.ndarray>
      long_name :
      Start time for average period
      Array Chunk
      Bytes 1.92 kB 96 B
      Shape (240,) (12,)
      Count 21 Tasks 20 Chunks
      Type object numpy.ndarray
      240 1
    • average_T2
      (time)
      object
      dask.array<chunksize=(12,), meta=np.ndarray>
      long_name :
      End time for average period
      Array Chunk
      Bytes 1.92 kB 96 B
      Shape (240,) (12,)
      Count 21 Tasks 20 Chunks
      Type object numpy.ndarray
      240 1
    • eta_t
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      surface height on T cells [Boussinesq (volume conserving) model]
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      meter
      valid_range :
      [-1000.0, 1000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • eta_u
      (time, yu_ocean, xu_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      surface height on U cells
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      meter
      valid_range :
      [-1000.0, 1000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • frazil_2d
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      ocn frazil heat flux over time step
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      W/m^2
      valid_range :
      [-10000000000.0, 10000000000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • hblt
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      T-cell boundary layer depth from KPP
      standard_name :
      ocean_mixed_layer_thickness_defined_by_mixing_scheme
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      m
      valid_range :
      [-100000.0, 1000000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • ice_calving
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      mass flux of land ice calving into ocean
      standard_name :
      water_flux_into_sea_water_from_icebergs
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      (kg/m^3)*(m/sec)
      valid_range :
      [-1000000.0, 1000000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • mld
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      mixed layer depth determined by density criteria
      standard_name :
      ocean_mixed_layer_thickness_defined_by_sigma_t
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      m
      valid_range :
      [0.0, 1000000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • mld_dtheta
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      mixed layer depth determined by temperature criteria
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      m
      valid_range :
      [0.0, 1000000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • net_sfc_heating
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      surface ocean heat flux coming through coupler and mass transfer
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      Watts/m^2
      valid_range :
      [-10000.0, 10000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • pme_river
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      mass flux of precip-evap+river via sbc (liquid, frozen, evaporation)
      standard_name :
      water_flux_into_sea_water
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      (kg/m^3)*(m/sec)
      valid_range :
      [-1000000.0, 1000000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • pot_rho_0
      (time, st_ocean, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(1, 5, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      potential density referenced to 0 dbar
      standard_name :
      sea_water_potential_density
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      kg/m^3
      valid_range :
      [-10.0, 100000.0]
      Array Chunk
      Bytes 466.56 GB 194.40 MB
      Shape (240, 50, 2700, 3600) (1, 5, 2700, 3600)
      Count 2401 Tasks 2400 Chunks
      Type float32 numpy.ndarray
      240 1 3600 2700 50
    • river
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      mass flux of river (runoff + calving) entering ocean
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      (kg/m^3)*(m/sec)
      valid_range :
      [-1000000.0, 1000000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • salt
      (time, st_ocean, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(1, 5, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      Practical Salinity
      standard_name :
      sea_water_salinity
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      psu
      valid_range :
      [-10.0, 100.0]
      Array Chunk
      Bytes 466.56 GB 194.40 MB
      Shape (240, 50, 2700, 3600) (1, 5, 2700, 3600)
      Count 2401 Tasks 2400 Chunks
      Type float32 numpy.ndarray
      240 1 3600 2700 50
    • salt_int_rhodz
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      vertical sum of Practical Salinity * rho_dzt
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      psu*(kg/m^3)*m
      valid_range :
      [-1.0000000200408773e+20, 1.0000000200408773e+20]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • sea_level
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      effective sea level (eta_t + patm/(rho0*g)) on T cells
      standard_name :
      sea_surface_height_above_geoid
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      meter
      valid_range :
      [-1000.0, 1000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • sea_levelsq
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      square of effective sea level (eta_t + patm/(rho0*g)) on T cells
      standard_name :
      square_of_sea_surface_height_above_geoid
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      m^2
      valid_range :
      [-1000.0, 1000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • sfc_hflux_coupler
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      surface heat flux coming through coupler
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      Watts/m^2
      valid_range :
      [-10000.0, 10000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • sfc_hflux_pme
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      heat flux (relative to 0C) from pme transfer of water across ocean surface
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      Watts/m^2
      valid_range :
      [-10000.0, 10000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • swflx
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      shortwave flux into ocean (>0 heats ocean)
      standard_name :
      surface_net_downward_shortwave_flux
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      W/m^2
      valid_range :
      [-10000000000.0, 10000000000.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • tau_x
      (time, yu_ocean, xu_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      i-directed wind stress forcing u-velocity
      standard_name :
      surface_downward_x_stress
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      N/m^2
      valid_range :
      [-10.0, 10.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • tau_y
      (time, yu_ocean, xu_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      j-directed wind stress forcing v-velocity
      standard_name :
      surface_downward_y_stress
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      N/m^2
      valid_range :
      [-10.0, 10.0]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • temp
      (time, st_ocean, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(1, 5, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      Potential temperature
      standard_name :
      sea_water_potential_temperature
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      degrees C
      valid_range :
      [-10.0, 500.0]
      Array Chunk
      Bytes 466.56 GB 194.40 MB
      Shape (240, 50, 2700, 3600) (1, 5, 2700, 3600)
      Count 2401 Tasks 2400 Chunks
      Type float32 numpy.ndarray
      240 1 3600 2700 50
    • temp_int_rhodz
      (time, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      vertical sum of Potential temperature * rho_dzt
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      deg_C*(kg/m^3)*m
      valid_range :
      [-1.0000000200408773e+20, 1.0000000200408773e+20]
      Array Chunk
      Bytes 9.33 GB 116.64 MB
      Shape (240, 2700, 3600) (3, 2700, 3600)
      Count 81 Tasks 80 Chunks
      Type float32 numpy.ndarray
      3600 2700 240
    • temp_rivermix
      (time, st_ocean, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(1, 5, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      cp*rivermix*rho_dzt*temp
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      Watt/m^2
      valid_range :
      [-10000000000.0, 10000000000.0]
      Array Chunk
      Bytes 466.56 GB 194.40 MB
      Shape (240, 50, 2700, 3600) (1, 5, 2700, 3600)
      Count 2401 Tasks 2400 Chunks
      Type float32 numpy.ndarray
      240 1 3600 2700 50
    • time_bounds
      (time, nv)
      timedelta64[ns]
      dask.array<chunksize=(12, 2), meta=np.ndarray>
      long_name :
      time axis boundaries
      calendar :
      JULIAN
      Array Chunk
      Bytes 3.84 kB 192 B
      Shape (240, 2) (12, 2)
      Count 21 Tasks 20 Chunks
      Type timedelta64[ns] numpy.ndarray
      2 240
    • ty_trans
      (time, st_ocean, yu_ocean, xt_ocean)
      float32
      dask.array<chunksize=(1, 5, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      T-cell j-mass transport
      standard_name :
      ocean_y_mass_transport
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      Sv (10^9 kg/s)
      valid_range :
      [-1.0000000200408773e+20, 1.0000000200408773e+20]
      Array Chunk
      Bytes 466.56 GB 194.40 MB
      Shape (240, 50, 2700, 3600) (1, 5, 2700, 3600)
      Count 2401 Tasks 2400 Chunks
      Type float32 numpy.ndarray
      240 1 3600 2700 50
    • u
      (time, st_ocean, yu_ocean, xu_ocean)
      float32
      dask.array<chunksize=(1, 5, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      i-current
      standard_name :
      sea_water_x_velocity
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      m/sec
      valid_range :
      [-10.0, 10.0]
      Array Chunk
      Bytes 466.56 GB 194.40 MB
      Shape (240, 50, 2700, 3600) (1, 5, 2700, 3600)
      Count 2401 Tasks 2400 Chunks
      Type float32 numpy.ndarray
      240 1 3600 2700 50
    • v
      (time, st_ocean, yu_ocean, xu_ocean)
      float32
      dask.array<chunksize=(1, 5, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      j-current
      standard_name :
      sea_water_y_velocity
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      m/sec
      valid_range :
      [-10.0, 10.0]
      Array Chunk
      Bytes 466.56 GB 194.40 MB
      Shape (240, 50, 2700, 3600) (1, 5, 2700, 3600)
      Count 2401 Tasks 2400 Chunks
      Type float32 numpy.ndarray
      240 1 3600 2700 50
    • wt
      (time, sw_ocean, yt_ocean, xt_ocean)
      float32
      dask.array<chunksize=(1, 50, 2700, 3600), meta=np.ndarray>
      cell_methods :
      time: mean
      long_name :
      dia-surface velocity T-points
      time_avg_info :
      average_T1,average_T2,average_DT
      units :
      m/sec
      valid_range :
      [-100000.0, 100000.0]
      Array Chunk
      Bytes 466.56 GB 1.94 GB
      Shape (240, 50, 2700, 3600) (1, 50, 2700, 3600)
      Count 241 Tasks 240 Chunks
      Type float32 numpy.ndarray
      240 1 3600 2700 50
  • filename :
    01810101.ocean.nc
    grid_tile :
    1
    grid_type :
    mosaic
    title :
    CM2.6_miniBling