ECCOv4r3

Estimating the Circulation and Climate of the Ocean (ECCO) State Estimate Version 4 Release 3

Load in Python

from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean.yaml") ds = cat["ECCOv4r3"].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://ecco-v4-python-tutorial.readthedocs.io/intro.html
tags ['ocean', 'model']

Dataset Contents

Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • face: 13
    • i: 90
    • i_g: 90
    • j: 90
    • j_g: 90
    • k: 50
    • k_l: 50
    • k_p1: 51
    • k_u: 50
    • time: 288
    • time_snp: 287
    • Depth
      (face, j, i)
      float32
      dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
      coordinate :
      XC YC
      long_name :
      ocean depth
      standard_name :
      ocean_depth
      units :
      m
      Array Chunk
      Bytes 421.20 kB 421.20 kB
      Shape (13, 90, 90) (13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • PHrefC
      (k)
      float32
      dask.array<chunksize=(50,), meta=np.ndarray>
      long_name :
      Reference Hydrostatic Pressure
      standard_name :
      cell_reference_pressure
      units :
      m2 s-2
      Array Chunk
      Bytes 200 B 200 B
      Shape (50,) (50,)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      50 1
    • PHrefF
      (k_p1)
      float32
      dask.array<chunksize=(51,), meta=np.ndarray>
      long_name :
      Reference Hydrostatic Pressure
      standard_name :
      cell_reference_pressure
      units :
      m2 s-2
      Array Chunk
      Bytes 204 B 204 B
      Shape (51,) (51,)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      51 1
    • XC
      (face, j, i)
      float32
      dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
      coordinate :
      YC XC
      long_name :
      longitude
      standard_name :
      longitude
      units :
      degrees_east
      Array Chunk
      Bytes 421.20 kB 421.20 kB
      Shape (13, 90, 90) (13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • XG
      (face, j_g, i_g)
      float32
      dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
      coordinate :
      YG XG
      long_name :
      longitude
      standard_name :
      longitude_at_f_location
      units :
      degrees_east
      Array Chunk
      Bytes 421.20 kB 421.20 kB
      Shape (13, 90, 90) (13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • YC
      (face, j, i)
      float32
      dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
      coordinate :
      YC XC
      long_name :
      latitude
      standard_name :
      latitude
      units :
      degrees_north
      Array Chunk
      Bytes 421.20 kB 421.20 kB
      Shape (13, 90, 90) (13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • YG
      (face, j_g, i_g)
      float32
      dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
      long_name :
      latitude
      standard_name :
      latitude_at_f_location
      units :
      degrees_north
      Array Chunk
      Bytes 421.20 kB 421.20 kB
      Shape (13, 90, 90) (13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • Z
      (k)
      float32
      dask.array<chunksize=(50,), meta=np.ndarray>
      long_name :
      vertical coordinate of cell center
      positive :
      down
      standard_name :
      depth
      units :
      m
      Array Chunk
      Bytes 200 B 200 B
      Shape (50,) (50,)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      50 1
    • Zl
      (k_l)
      float32
      dask.array<chunksize=(50,), meta=np.ndarray>
      long_name :
      vertical coordinate of upper cell interface
      positive :
      down
      standard_name :
      depth_at_upper_w_location
      units :
      m
      Array Chunk
      Bytes 200 B 200 B
      Shape (50,) (50,)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      50 1
    • Zp1
      (k_p1)
      float32
      dask.array<chunksize=(51,), meta=np.ndarray>
      long_name :
      vertical coordinate of cell interface
      positive :
      down
      standard_name :
      depth_at_w_location
      units :
      m
      Array Chunk
      Bytes 204 B 204 B
      Shape (51,) (51,)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      51 1
    • Zu
      (k_u)
      float32
      dask.array<chunksize=(50,), meta=np.ndarray>
      long_name :
      vertical coordinate of lower cell interface
      positive :
      down
      standard_name :
      depth_at_lower_w_location
      units :
      m
      Array Chunk
      Bytes 200 B 200 B
      Shape (50,) (50,)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      50 1
    • drC
      (k_p1)
      float32
      dask.array<chunksize=(51,), meta=np.ndarray>
      long_name :
      cell z size
      standard_name :
      cell_z_size_at_w_location
      units :
      m
      Array Chunk
      Bytes 204 B 204 B
      Shape (51,) (51,)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      51 1
    • drF
      (k)
      float32
      dask.array<chunksize=(50,), meta=np.ndarray>
      long_name :
      cell z size
      standard_name :
      cell_z_size
      units :
      m
      Array Chunk
      Bytes 200 B 200 B
      Shape (50,) (50,)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      50 1
    • dxC
      (face, j, i_g)
      float32
      dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
      coordinate :
      YC XG
      long_name :
      cell x size
      standard_name :
      cell_x_size_at_u_location
      units :
      m
      Array Chunk
      Bytes 421.20 kB 421.20 kB
      Shape (13, 90, 90) (13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • dxG
      (face, j_g, i)
      float32
      dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
      coordinate :
      YG XC
      long_name :
      cell x size
      standard_name :
      cell_x_size_at_v_location
      units :
      m
      Array Chunk
      Bytes 421.20 kB 421.20 kB
      Shape (13, 90, 90) (13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • dyC
      (face, j_g, i)
      float32
      dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
      coordinate :
      YG XC
      long_name :
      cell y size
      standard_name :
      cell_y_size_at_v_location
      units :
      m
      Array Chunk
      Bytes 421.20 kB 421.20 kB
      Shape (13, 90, 90) (13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • dyG
      (face, j, i_g)
      float32
      dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
      coordinate :
      YC XG
      long_name :
      cell y size
      standard_name :
      cell_y_size_at_u_location
      units :
      m
      Array Chunk
      Bytes 421.20 kB 421.20 kB
      Shape (13, 90, 90) (13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • face
      (face)
      int64
      0 1 2 3 4 5 6 7 8 9 10 11 12
      standard_name :
      face_index
      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])
    • hFacC
      (k, face, j, i)
      float32
      dask.array<chunksize=(50, 13, 90, 90), meta=np.ndarray>
      long_name :
      vertical fraction of open cell
      standard_name :
      cell_vertical_fraction
      Array Chunk
      Bytes 21.06 MB 21.06 MB
      Shape (50, 13, 90, 90) (50, 13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      50 1 90 90 13
    • hFacS
      (k, face, j_g, i)
      float32
      dask.array<chunksize=(50, 13, 90, 90), meta=np.ndarray>
      long_name :
      vertical fraction of open cell
      standard_name :
      cell_vertical_fraction_at_v_location
      Array Chunk
      Bytes 21.06 MB 21.06 MB
      Shape (50, 13, 90, 90) (50, 13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      50 1 90 90 13
    • hFacW
      (k, face, j, i_g)
      float32
      dask.array<chunksize=(50, 13, 90, 90), meta=np.ndarray>
      long_name :
      vertical fraction of open cell
      standard_name :
      cell_vertical_fraction_at_u_location
      Array Chunk
      Bytes 21.06 MB 21.06 MB
      Shape (50, 13, 90, 90) (50, 13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      50 1 90 90 13
    • i
      (i)
      int64
      0 1 2 3 4 5 6 ... 84 85 86 87 88 89
      axis :
      X
      long_name :
      x-dimension of the t grid
      standard_name :
      x_grid_index
      swap_dim :
      XC
      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, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89])
    • i_g
      (i_g)
      int64
      0 1 2 3 4 5 6 ... 84 85 86 87 88 89
      axis :
      X
      c_grid_axis_shift :
      -0.5
      long_name :
      x-dimension of the u grid
      standard_name :
      x_grid_index_at_u_location
      swap_dim :
      XG
      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, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89])
    • iter
      (time)
      int64
      dask.array<chunksize=(1,), meta=np.ndarray>
      long_name :
      model timestep number
      standard_name :
      timestep
      Array Chunk
      Bytes 2.30 kB 8 B
      Shape (288,) (1,)
      Count 289 Tasks 288 Chunks
      Type int64 numpy.ndarray
      288 1
    • iter_snp
      (time_snp)
      int64
      dask.array<chunksize=(1,), meta=np.ndarray>
      long_name :
      model timestep number
      standard_name :
      timestep
      Array Chunk
      Bytes 2.30 kB 8 B
      Shape (287,) (1,)
      Count 288 Tasks 287 Chunks
      Type int64 numpy.ndarray
      287 1
    • j
      (j)
      int64
      0 1 2 3 4 5 6 ... 84 85 86 87 88 89
      axis :
      Y
      long_name :
      y-dimension of the t grid
      standard_name :
      y_grid_index
      swap_dim :
      YC
      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, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89])
    • j_g
      (j_g)
      int64
      0 1 2 3 4 5 6 ... 84 85 86 87 88 89
      axis :
      Y
      c_grid_axis_shift :
      -0.5
      long_name :
      y-dimension of the v grid
      standard_name :
      y_grid_index_at_v_location
      swap_dim :
      YG
      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, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89])
    • k
      (k)
      int64
      0 1 2 3 4 5 6 ... 44 45 46 47 48 49
      axis :
      Z
      long_name :
      z-dimension of the t grid
      standard_name :
      z_grid_index
      swap_dim :
      Z
      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])
    • k_l
      (k_l)
      int64
      0 1 2 3 4 5 6 ... 44 45 46 47 48 49
      axis :
      Z
      c_grid_axis_shift :
      -0.5
      long_name :
      z-dimension of the w grid
      standard_name :
      z_grid_index_at_upper_w_location
      swap_dim :
      Zl
      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])
    • k_p1
      (k_p1)
      int64
      0 1 2 3 4 5 6 ... 45 46 47 48 49 50
      axis :
      Z
      c_grid_axis_shift :
      [-0.5, 0.5]
      long_name :
      z-dimension of the w grid
      standard_name :
      z_grid_index_at_w_location
      swap_dim :
      Zp1
      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])
    • k_u
      (k_u)
      int64
      0 1 2 3 4 5 6 ... 44 45 46 47 48 49
      axis :
      Z
      c_grid_axis_shift :
      0.5
      long_name :
      z-dimension of the w grid
      standard_name :
      z_grid_index_at_lower_w_location
      swap_dim :
      Zu
      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])
    • rA
      (face, j, i)
      float32
      dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
      coordinate :
      YC XC
      long_name :
      cell area
      standard_name :
      cell_area
      units :
      m2
      Array Chunk
      Bytes 421.20 kB 421.20 kB
      Shape (13, 90, 90) (13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • rAs
      (face, j_g, i)
      float32
      dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
      long_name :
      cell area
      standard_name :
      cell_area_at_v_location
      units :
      m2
      Array Chunk
      Bytes 421.20 kB 421.20 kB
      Shape (13, 90, 90) (13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • rAw
      (face, j, i_g)
      float32
      dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
      coordinate :
      YG XC
      long_name :
      cell area
      standard_name :
      cell_area_at_u_location
      units :
      m2
      Array Chunk
      Bytes 421.20 kB 421.20 kB
      Shape (13, 90, 90) (13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • rAz
      (face, j_g, i_g)
      float32
      dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
      coordinate :
      YG XG
      long_name :
      cell area
      standard_name :
      cell_area_at_f_location
      units :
      m
      Array Chunk
      Bytes 421.20 kB 421.20 kB
      Shape (13, 90, 90) (13, 90, 90)
      Count 2 Tasks 1 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • time
      (time)
      datetime64[ns]
      1992-01-15 ... 2015-12-14
      axis :
      T
      long_name :
      Time
      standard_name :
      time
      array(['1992-01-15T00:00:00.000000000', '1992-02-13T00:00:00.000000000',
             '1992-03-15T00:00:00.000000000', ..., '2015-10-15T00:00:00.000000000',
             '2015-11-14T00:00:00.000000000', '2015-12-14T00:00:00.000000000'],
            dtype='datetime64[ns]')
    • time_snp
      (time_snp)
      datetime64[ns]
      1992-02-01 ... 2015-12-01
      axis :
      T
      c_grid_axis_shift :
      0.5
      long_name :
      Time
      standard_name :
      time
      array(['1992-02-01T00:00:00.000000000', '1992-03-01T00:00:00.000000000',
             '1992-04-01T00:00:00.000000000', ..., '2015-10-01T00:00:00.000000000',
             '2015-11-01T00:00:00.000000000', '2015-12-01T00:00:00.000000000'],
            dtype='datetime64[ns]')
    • ADVr_SLT
      (time, k_l, face, j, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Vertical Advective Flux of Salinity
      standard_name :
      ADVr_SLT
      units :
      psu.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • ADVr_TH
      (time, k_l, face, j, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Vertical Advective Flux of Pot.Temperature
      standard_name :
      ADVr_TH
      units :
      degC.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • ADVx_SLT
      (time, k, face, j, i_g)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Zonal Advective Flux of Salinity
      mate :
      ADVy_SLT
      standard_name :
      ADVx_SLT
      units :
      psu.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • ADVx_TH
      (time, k, face, j, i_g)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Zonal Advective Flux of Pot.Temperature
      mate :
      ADVy_TH
      standard_name :
      ADVx_TH
      units :
      degC.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • ADVy_SLT
      (time, k, face, j_g, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Meridional Advective Flux of Salinity
      mate :
      ADVx_SLT
      standard_name :
      ADVy_SLT
      units :
      psu.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • ADVy_TH
      (time, k, face, j_g, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Meridional Advective Flux of Pot.Temperature
      mate :
      ADVx_TH
      standard_name :
      ADVy_TH
      units :
      degC.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • DFrE_SLT
      (time, k_l, face, j, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Vertical Diffusive Flux of Salinity (Explicit part)
      standard_name :
      DFrE_SLT
      units :
      psu.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • DFrE_TH
      (time, k_l, face, j, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Vertical Diffusive Flux of Pot.Temperature (Explicit part)
      standard_name :
      DFrE_TH
      units :
      degC.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • DFrI_SLT
      (time, k_l, face, j, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Vertical Diffusive Flux of Salinity (Implicit part)
      standard_name :
      DFrI_SLT
      units :
      psu.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • DFrI_TH
      (time, k_l, face, j, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Vertical Diffusive Flux of Pot.Temperature (Implicit part)
      standard_name :
      DFrI_TH
      units :
      degC.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • DFxE_SLT
      (time, k, face, j, i_g)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Zonal Diffusive Flux of Salinity
      mate :
      DFyE_SLT
      standard_name :
      DFxE_SLT
      units :
      psu.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • DFxE_TH
      (time, k, face, j, i_g)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Zonal Diffusive Flux of Pot.Temperature
      mate :
      DFyE_TH
      standard_name :
      DFxE_TH
      units :
      degC.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • DFyE_SLT
      (time, k, face, j_g, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Meridional Diffusive Flux of Salinity
      mate :
      DFxE_SLT
      standard_name :
      DFyE_SLT
      units :
      psu.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • DFyE_TH
      (time, k, face, j_g, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Meridional Diffusive Flux of Pot.Temperature
      mate :
      DFxE_TH
      standard_name :
      DFyE_TH
      units :
      degC.m^3/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • ETAN
      (time, face, j, i)
      float32
      dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
      long_name :
      Surface Height Anomaly
      standard_name :
      ETAN
      units :
      m
      Array Chunk
      Bytes 121.31 MB 421.20 kB
      Shape (288, 13, 90, 90) (1, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      288 1 90 90 13
    • ETAN_snp
      (time_snp, face, j, i)
      float32
      dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
      long_name :
      Surface Height Anomaly
      standard_name :
      ETAN
      units :
      m
      Array Chunk
      Bytes 120.88 MB 421.20 kB
      Shape (287, 13, 90, 90) (1, 13, 90, 90)
      Count 288 Tasks 287 Chunks
      Type float32 numpy.ndarray
      287 1 90 90 13
    • GEOFLX
      (face, j, i)
      float32
      dask.array<chunksize=(7, 90, 90), meta=np.ndarray>
      Array Chunk
      Bytes 421.20 kB 226.80 kB
      Shape (13, 90, 90) (7, 90, 90)
      Count 3 Tasks 2 Chunks
      Type float32 numpy.ndarray
      90 90 13
    • MXLDEPTH
      (time, face, j, i)
      float32
      dask.array<chunksize=(1, 1, 90, 90), meta=np.ndarray>
      coordinates :
      SN dt iter XC Depth YC CS rA
      long_name :
      Mixed-Layer Depth (>0)
      standard_name :
      MXLDEPTH
      units :
      m
      Array Chunk
      Bytes 121.31 MB 32.40 kB
      Shape (288, 13, 90, 90) (1, 1, 90, 90)
      Count 3745 Tasks 3744 Chunks
      Type float32 numpy.ndarray
      288 1 90 90 13
    • SALT
      (time, k, face, j, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Salinity
      standard_name :
      SALT
      units :
      psu
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • SALT_snp
      (time_snp, k, face, j, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Salinity
      standard_name :
      SALT
      units :
      psu
      Array Chunk
      Bytes 6.04 GB 21.06 MB
      Shape (287, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 288 Tasks 287 Chunks
      Type float32 numpy.ndarray
      50 287 90 90 13
    • SFLUX
      (time, face, j, i)
      float32
      dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
      long_name :
      total salt flux (match salt-content variations), >0 increases salt
      standard_name :
      SFLUX
      units :
      g/m^2/s
      Array Chunk
      Bytes 121.31 MB 421.20 kB
      Shape (288, 13, 90, 90) (1, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      288 1 90 90 13
    • TFLUX
      (time, face, j, i)
      float32
      dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
      long_name :
      total heat flux (match heat-content variations), >0 increases theta
      standard_name :
      TFLUX
      units :
      W/m^2
      Array Chunk
      Bytes 121.31 MB 421.20 kB
      Shape (288, 13, 90, 90) (1, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      288 1 90 90 13
    • THETA
      (time, k, face, j, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Potential Temperature
      standard_name :
      THETA
      units :
      degC
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • THETA_snp
      (time_snp, k, face, j, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Potential Temperature
      standard_name :
      THETA
      units :
      degC
      Array Chunk
      Bytes 6.04 GB 21.06 MB
      Shape (287, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 288 Tasks 287 Chunks
      Type float32 numpy.ndarray
      50 287 90 90 13
    • UVELMASS
      (time, k, face, j, i_g)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Zonal Mass-Weighted Comp of Velocity (m/s)
      mate :
      VVELMASS
      standard_name :
      UVELMASS
      units :
      m/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • UVELSTAR
      (time, k, face, j, i_g)
      float32
      dask.array<chunksize=(1, 50, 1, 90, 90), meta=np.ndarray>
      coordinates :
      hFacW dt PHrefC Z iter dxC drF rAw dyG
      long_name :
      Zonal Component of Bolus Velocity
      mate :
      VVELSTAR
      standard_name :
      UVELSTAR
      units :
      m/s
      Array Chunk
      Bytes 6.07 GB 1.62 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 1, 90, 90)
      Count 3745 Tasks 3744 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • VVELMASS
      (time, k, face, j_g, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Meridional Mass-Weighted Comp of Velocity (m/s)
      mate :
      UVELMASS
      standard_name :
      VVELMASS
      units :
      m/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • VVELSTAR
      (time, k, face, j_g, i)
      float32
      dask.array<chunksize=(1, 50, 1, 90, 90), meta=np.ndarray>
      coordinates :
      dt PHrefC Z iter dxG rAs hFacS dyC drF
      long_name :
      Meridional Component of Bolus Velocity
      mate :
      UVELSTAR
      standard_name :
      VVELSTAR
      units :
      m/s
      Array Chunk
      Bytes 6.07 GB 1.62 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 1, 90, 90)
      Count 3745 Tasks 3744 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • WVELMASS
      (time, k_l, face, j, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      Vertical Mass-Weighted Comp of Velocity
      standard_name :
      WVELMASS
      units :
      m/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • WVELSTAR
      (time, k_l, face, j, i)
      float32
      dask.array<chunksize=(1, 50, 1, 90, 90), meta=np.ndarray>
      coordinates :
      Zl SN dt iter XC Depth YC CS rA
      long_name :
      Vertical Component of Bolus Velocity
      standard_name :
      WVELSTAR
      units :
      m/s
      Array Chunk
      Bytes 6.07 GB 1.62 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 1, 90, 90)
      Count 3745 Tasks 3744 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • oceFWflx
      (time, face, j, i)
      float32
      dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
      long_name :
      net surface Fresh-Water flux into the ocean (+=down), >0 decreases salinity
      standard_name :
      oceFWflx
      units :
      kg/m^2/s
      Array Chunk
      Bytes 121.31 MB 421.20 kB
      Shape (288, 13, 90, 90) (1, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      288 1 90 90 13
    • oceQsw
      (time, face, j, i)
      float32
      dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
      long_name :
      net Short-Wave radiation (+=down), >0 increases theta
      standard_name :
      oceQsw
      units :
      W/m^2
      Array Chunk
      Bytes 121.31 MB 421.20 kB
      Shape (288, 13, 90, 90) (1, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      288 1 90 90 13
    • oceSPtnd
      (time, k, face, j, i)
      float32
      dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
      long_name :
      salt tendency due to salt plume flux >0 increases salinity
      standard_name :
      oceSPtnd
      units :
      g/m^2/s
      Array Chunk
      Bytes 6.07 GB 21.06 MB
      Shape (288, 50, 13, 90, 90) (1, 50, 13, 90, 90)
      Count 289 Tasks 288 Chunks
      Type float32 numpy.ndarray
      50 288 90 90 13
    • oceTAUX
      (time, face, j, i_g)
      float32
      dask.array<chunksize=(1, 1, 90, 90), meta=np.ndarray>
      coordinates :
      dt iter dxC rAw dyG
      long_name :
      zonal surface wind stress, >0 increases uVel
      mate :
      oceTAUY
      standard_name :
      oceTAUX
      units :
      N/m^2
      Array Chunk
      Bytes 121.31 MB 32.40 kB
      Shape (288, 13, 90, 90) (1, 1, 90, 90)
      Count 3745 Tasks 3744 Chunks
      Type float32 numpy.ndarray
      288 1 90 90 13
    • oceTAUY
      (time, face, j_g, i)
      float32
      dask.array<chunksize=(1, 1, 90, 90), meta=np.ndarray>
      coordinates :
      dt dxG iter rAs dyC
      long_name :
      meridional surf. wind stress, >0 increases vVel
      mate :
      oceTAUX
      standard_name :
      oceTAUY
      units :
      N/m^2
      Array Chunk
      Bytes 121.31 MB 32.40 kB
      Shape (288, 13, 90, 90) (1, 1, 90, 90)
      Count 3745 Tasks 3744 Chunks
      Type float32 numpy.ndarray
      288 1 90 90 13