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:

Create a project on GCP; if this is the first time using GCP, a prompt will appear to choose a Google account to link to all GCP-related activities.
Create a Cloud Billing account associated with the project and enable billing for the project through this account.
Using Google Cloud IAM, add the Service Usage Consumer role to your account, which enables it to make billed requests on the behalf of the project.
Through command line, install the Google Cloud SDK; this can be done using conda:
```
conda install -c conda-forge google-cloud-sdk
```
Initialize the gcloud command line interface, logging into the account used to create the aforementioned project and selecting it as the default project; this will allow the project to be used for requester pays access through the command line:
```
gcloud auth login
gcloud init
```
Finally, use gcloud to establish application default credentials; this will allow the project to be used for requester pays access through applications:
```
gcloud auth application-default login
```

Metadata

url	https://ecco-v4-python-tutorial.readthedocs.io/intro.html
tags	['ocean', 'model']

Dataset Contents

xarray.Dataset

Dimensions:
- 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

Coordinates: (37)

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

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

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

(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

(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

(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

(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

(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

(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

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

(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

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

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

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

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

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

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

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

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

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

(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

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

(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)

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])

(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

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

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

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

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]')

Data variables: (36)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

basins

(face, j, i)

int16

dask.array<chunksize=(1, 90, 90), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  210.60 kB   16.20 kB 
     Shape  (13, 90, 90)   (1, 90, 90) 
     Count  14 Tasks  13 Chunks 
     Type  int16  numpy.ndarray

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

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

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

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

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

Attributes: (0)