SOSE

Southern Ocean State Estimate

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["SOSE"].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	http://sose.ucsd.edu/
tags	['ocean', 'model']

Dataset Contents

xarray.Dataset

Dimensions:
- XC: 2160
- XG: 2160
- YC: 320
- YG: 320
- Z: 42
- Zl: 42
- Zp1: 43
- Zu: 42
- time: 438

Coordinates: (26)

Depth

(YC, XC)

float32

dask.array<chunksize=(320, 2160), meta=np.ndarray>

coordinate :: XC YC
long_name :: ocean depth
standard_name :: ocean_depth
units :: m





  
      Array  Chunk 
  
  
     Bytes  2.76 MB   2.76 MB 
     Shape  (320, 2160)   (320, 2160) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

PHrefC

(Z)

float32

dask.array<chunksize=(42,), meta=np.ndarray>

long_name :: Reference Hydrostatic Pressure
standard_name :: cell_reference_pressure
units :: m2 s-2





  
      Array  Chunk 
  
  
     Bytes  168 B   168 B 
     Shape  (42,)   (42,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

PHrefF

(Zp1)

float32

dask.array<chunksize=(43,), meta=np.ndarray>

long_name :: Reference Hydrostatic Pressure
standard_name :: cell_reference_pressure
units :: m2 s-2





  
      Array  Chunk 
  
  
     Bytes  172 B   172 B 
     Shape  (43,)   (43,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

XC
(XC)
float32
0.083333336 0.25 ... 359.9167
axis :
X
coordinate :
YC XC
long_name :
longitude
standard_name :
longitude
units :
degrees_east
```
array([8.333334e-02, 2.500000e-01, 4.166667e-01, ..., 3.595833e+02,
       3.597500e+02, 3.599167e+02], dtype=float32)
```
XG
(XG)
float32
5.551115e-17 ... 359.83334
axis :
X
c_grid_axis_shift :
-0.5
coordinate :
YG XG
long_name :
longitude
standard_name :
longitude_at_f_location
units :
degrees_east
```
array([5.551115e-17, 1.666667e-01, 3.333333e-01, ..., 3.595000e+02,
       3.596667e+02, 3.598333e+02], dtype=float32)
```
YC
(YC)
float32
-77.87497 -77.7083 ... -24.7083
axis :
Y
coordinate :
YC XC
long_name :
latitude
standard_name :
latitude
units :
degrees_north
```
array([-77.87497 , -77.7083  , -77.54163 , ..., -25.041632, -24.874966,
       -24.7083  ], dtype=float32)
```
YG
(YG)
float32
-77.9583 -77.79163 ... -24.791632
axis :
Y
c_grid_axis_shift :
-0.5
long_name :
latitude
standard_name :
latitude_at_f_location
units :
degrees_north
```
array([-77.9583  , -77.79163 , -77.62497 , ..., -25.124966, -24.9583  ,
       -24.791632], dtype=float32)
```

(Z)

float32

-5.0 -15.5 ... -5325.0 -5575.0

axis :: Z
long_name :: vertical coordinate of cell center
positive :: down
standard_name :: depth
units :: m

array([-5.0000e+00, -1.5500e+01, -2.7000e+01, -3.9500e+01, -5.3000e+01,
       -6.8000e+01, -8.5000e+01, -1.0400e+02, -1.2550e+02, -1.5000e+02,
       -1.7750e+02, -2.0850e+02, -2.4350e+02, -2.8300e+02, -3.2800e+02,
       -3.7950e+02, -4.3850e+02, -5.0600e+02, -5.8300e+02, -6.7100e+02,
       -7.7200e+02, -8.8800e+02, -1.0210e+03, -1.1735e+03, -1.3485e+03,
       -1.5495e+03, -1.7805e+03, -2.0460e+03, -2.3190e+03, -2.5750e+03,
       -2.8250e+03, -3.0750e+03, -3.3250e+03, -3.5750e+03, -3.8250e+03,
       -4.0750e+03, -4.3250e+03, -4.5750e+03, -4.8250e+03, -5.0750e+03,
       -5.3250e+03, -5.5750e+03], dtype=float32)

(Zl)

float32

0.0 -10.0 -21.0 ... -5200.0 -5450.0

axis :: Z
c_grid_axis_shift :: -0.5
long_name :: vertical coordinate of upper cell interface
positive :: down
standard_name :: depth_at_upper_w_location
units :: m

array([    0.,   -10.,   -21.,   -33.,   -46.,   -60.,   -76.,   -94.,  -114.,
        -137.,  -163.,  -192.,  -225.,  -262.,  -304.,  -352.,  -407.,  -470.,
        -542.,  -624.,  -718.,  -826.,  -950., -1092., -1255., -1442., -1657.,
       -1904., -2188., -2450., -2700., -2950., -3200., -3450., -3700., -3950.,
       -4200., -4450., -4700., -4950., -5200., -5450.], dtype=float32)

Zp1

(Zp1)

float32

0.0 -10.0 -21.0 ... -5450.0 -5700.0

axis :: Z
c_grid_axis_shift :: [-0.5, 0.5]
long_name :: vertical coordinate of cell interface
positive :: down
standard_name :: depth_at_w_location
units :: m

array([    0.,   -10.,   -21.,   -33.,   -46.,   -60.,   -76.,   -94.,  -114.,
        -137.,  -163.,  -192.,  -225.,  -262.,  -304.,  -352.,  -407.,  -470.,
        -542.,  -624.,  -718.,  -826.,  -950., -1092., -1255., -1442., -1657.,
       -1904., -2188., -2450., -2700., -2950., -3200., -3450., -3700., -3950.,
       -4200., -4450., -4700., -4950., -5200., -5450., -5700.], dtype=float32)

(Zu)

float32

-10.0 -21.0 ... -5450.0 -5700.0

axis :: Z
c_grid_axis_shift :: 0.5
long_name :: vertical coordinate of lower cell interface
positive :: down
standard_name :: depth_at_lower_w_location
units :: m

array([  -10.,   -21.,   -33.,   -46.,   -60.,   -76.,   -94.,  -114.,  -137.,
        -163.,  -192.,  -225.,  -262.,  -304.,  -352.,  -407.,  -470.,  -542.,
        -624.,  -718.,  -826.,  -950., -1092., -1255., -1442., -1657., -1904.,
       -2188., -2450., -2700., -2950., -3200., -3450., -3700., -3950., -4200.,
       -4450., -4700., -4950., -5200., -5450., -5700.], dtype=float32)

drC

(Zp1)

float32

dask.array<chunksize=(43,), meta=np.ndarray>

long_name :: cell z size
standard_name :: cell_z_size_at_w_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  172 B   172 B 
     Shape  (43,)   (43,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

drF

(Z)

float32

dask.array<chunksize=(42,), meta=np.ndarray>

long_name :: cell z size
standard_name :: cell_z_size
units :: m





  
      Array  Chunk 
  
  
     Bytes  168 B   168 B 
     Shape  (42,)   (42,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dxC

(YC, XG)

float32

dask.array<chunksize=(320, 2160), meta=np.ndarray>

coordinate :: YC XG
long_name :: cell x size
standard_name :: cell_x_size_at_u_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  2.76 MB   2.76 MB 
     Shape  (320, 2160)   (320, 2160) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dxG

(YG, XC)

float32

dask.array<chunksize=(320, 2160), meta=np.ndarray>

coordinate :: YG XC
long_name :: cell x size
standard_name :: cell_x_size_at_v_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  2.76 MB   2.76 MB 
     Shape  (320, 2160)   (320, 2160) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dyC

(YG, XC)

float32

dask.array<chunksize=(320, 2160), meta=np.ndarray>

coordinate :: YG XC
long_name :: cell y size
standard_name :: cell_y_size_at_v_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  2.76 MB   2.76 MB 
     Shape  (320, 2160)   (320, 2160) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dyG

(YC, XG)

float32

dask.array<chunksize=(320, 2160), meta=np.ndarray>

coordinate :: YC XG
long_name :: cell y size
standard_name :: cell_y_size_at_u_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  2.76 MB   2.76 MB 
     Shape  (320, 2160)   (320, 2160) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

hFacC

(Z, YC, XC)

float32

dask.array<chunksize=(42, 320, 2160), meta=np.ndarray>

long_name :: vertical fraction of open cell
standard_name :: cell_vertical_fraction





  
      Array  Chunk 
  
  
     Bytes  116.12 MB   116.12 MB 
     Shape  (42, 320, 2160)   (42, 320, 2160) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

hFacS

(Z, YG, XC)

float32

dask.array<chunksize=(42, 320, 2160), meta=np.ndarray>

long_name :: vertical fraction of open cell
standard_name :: cell_vertical_fraction_at_v_location





  
      Array  Chunk 
  
  
     Bytes  116.12 MB   116.12 MB 
     Shape  (42, 320, 2160)   (42, 320, 2160) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

hFacW

(Z, YC, XG)

float32

dask.array<chunksize=(42, 320, 2160), meta=np.ndarray>

long_name :: vertical fraction of open cell
standard_name :: cell_vertical_fraction_at_u_location





  
      Array  Chunk 
  
  
     Bytes  116.12 MB   116.12 MB 
     Shape  (42, 320, 2160)   (42, 320, 2160) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

iter

(time)

int64

dask.array<chunksize=(438,), meta=np.ndarray>

long_name :: model timestep number
standard_name :: timestep





  
      Array  Chunk 
  
  
     Bytes  3.50 kB   3.50 kB 
     Shape  (438,)   (438,) 
     Count  2 Tasks  1 Chunks 
     Type  int64  numpy.ndarray

(YC, XC)

float32

dask.array<chunksize=(320, 2160), meta=np.ndarray>

coordinate :: YC XC
long_name :: cell area
standard_name :: cell_area
units :: m2





  
      Array  Chunk 
  
  
     Bytes  2.76 MB   2.76 MB 
     Shape  (320, 2160)   (320, 2160) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

rAs

(YG, XC)

float32

dask.array<chunksize=(320, 2160), meta=np.ndarray>

long_name :: cell area
standard_name :: cell_area_at_v_location
units :: m2





  
      Array  Chunk 
  
  
     Bytes  2.76 MB   2.76 MB 
     Shape  (320, 2160)   (320, 2160) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

rAw

(YC, XG)

float32

dask.array<chunksize=(320, 2160), meta=np.ndarray>

coordinate :: YG XC
long_name :: cell area
standard_name :: cell_area_at_u_location
units :: m2





  
      Array  Chunk 
  
  
     Bytes  2.76 MB   2.76 MB 
     Shape  (320, 2160)   (320, 2160) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

rAz

(YG, XG)

float32

dask.array<chunksize=(320, 2160), meta=np.ndarray>

coordinate :: YG XG
long_name :: cell area
standard_name :: cell_area_at_f_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  2.76 MB   2.76 MB 
     Shape  (320, 2160)   (320, 2160) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

time

(time)

datetime64[ns]

2005-01-06 ... 2010-12-31

axis :: T
long_name :: Time
standard_name :: time

array(['2005-01-06T00:00:00.000000000', '2005-01-11T00:00:00.000000000',
       '2005-01-16T00:00:00.000000000', ..., '2010-12-21T00:00:00.000000000',
       '2010-12-26T00:00:00.000000000', '2010-12-31T00:00:00.000000000'],
      dtype='datetime64[ns]')

Data variables: (56)

ADVr_SLT

(time, Zl, YC, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), meta=np.ndarray>

long_name :: Vertical Advective Flux of Salinity
standard_name :: ADVr_SLT
units :: psu.m^3/s





  
      Array  Chunk 
  
  
     Bytes  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

ADVr_TH

(time, Zl, YC, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), meta=np.ndarray>

long_name :: Vertical Advective Flux of Pot.Temperature
standard_name :: ADVr_TH
units :: degC.m^3/s





  
      Array  Chunk 
  
  
     Bytes  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

ADVx_SLT

(time, Z, YC, XG)

float32

dask.array<chunksize=(1, 42, 320, 2160), 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  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

ADVx_TH

(time, Z, YC, XG)

float32

dask.array<chunksize=(1, 42, 320, 2160), 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  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

ADVy_SLT

(time, Z, YG, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), 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  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

ADVy_TH

(time, Z, YG, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), 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  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

DFrE_SLT

(time, Zl, YC, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), meta=np.ndarray>

long_name :: Vertical Diffusive Flux of Salinity (Explicit part)
standard_name :: DFrE_SLT
units :: psu.m^3/s





  
      Array  Chunk 
  
  
     Bytes  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

DFrE_TH

(time, Zl, YC, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), 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  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

DFrI_SLT

(time, Zl, YC, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), meta=np.ndarray>

long_name :: Vertical Diffusive Flux of Salinity (Implicit part)
standard_name :: DFrI_SLT
units :: psu.m^3/s





  
      Array  Chunk 
  
  
     Bytes  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

DFrI_TH

(time, Zl, YC, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), 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  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

DFxE_SLT

(time, Z, YC, XG)

float32

dask.array<chunksize=(1, 42, 320, 2160), 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  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

DFxE_TH

(time, Z, YC, XG)

float32

dask.array<chunksize=(1, 42, 320, 2160), 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  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

DFyE_SLT

(time, Z, YG, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), 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  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

DFyE_TH

(time, Z, YG, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), 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  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

DRHODR

(time, Zl, YC, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), meta=np.ndarray>

long_name :: Stratification: d.Sigma/dr (kg/m3/r_unit)
standard_name :: DRHODR
units :: kg/m^4





  
      Array  Chunk 
  
  
     Bytes  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

ETAN

(time, YC, XC)

float32

dask.array<chunksize=(1, 320, 2160), meta=np.ndarray>

long_name :: Surface Height Anomaly
standard_name :: ETAN
units :: m





  
      Array  Chunk 
  
  
     Bytes  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

EXFswnet

(time, YC, XC)

float32

dask.array<chunksize=(1, 320, 2160), meta=np.ndarray>

long_name :: Net upward shortwave radiation, >0 decreases theta
standard_name :: EXFswnet
units :: W/m^2





  
      Array  Chunk 
  
  
     Bytes  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

KPPg_SLT

(time, Zl, YC, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), meta=np.ndarray>

long_name :: KPP non-local Flux of Salinity
standard_name :: KPPg_SLT
units :: psu.m^3/s





  
      Array  Chunk 
  
  
     Bytes  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

KPPg_TH

(time, Zl, YC, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), meta=np.ndarray>

long_name :: KPP non-local Flux of Pot.Temperature
standard_name :: KPPg_TH
units :: degC.m^3/s





  
      Array  Chunk 
  
  
     Bytes  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

PHIHYD

(time, Z, YC, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), meta=np.ndarray>

long_name :: Hydrostatic Pressure Pot.(p/rho) Anomaly
standard_name :: PHIHYD
units :: m^2/s^2





  
      Array  Chunk 
  
  
     Bytes  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

SALT

(time, Z, YC, XC)

float32

dask.array<chunksize=(1, 42, 320, 2160), meta=np.ndarray>

long_name :: Salinity
standard_name :: SALT
units :: psu





  
      Array  Chunk 
  
  
     Bytes  50.86 GB   116.12 MB 
     Shape  (438, 42, 320, 2160)   (1, 42, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

SFLUX

(time, YC, XC)

float32

dask.array<chunksize=(1, 320, 2160), 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  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

SIarea

(time, YC, XC)

float32

dask.array<chunksize=(1, 320, 2160), meta=np.ndarray>

long_name :: SEAICE fractional ice-covered area [0 to 1]
standard_name :: SIarea
units :: m^2/m^2





  
      Array  Chunk 
  
  
     Bytes  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

SIatmFW

(time, YC, XC)

float32

dask.array<chunksize=(1, 320, 2160), meta=np.ndarray>

long_name :: Net freshwater flux from atmosphere & land (+=down)
standard_name :: SIatmFW
units :: kg/m^2/s





  
      Array  Chunk 
  
  
     Bytes  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

SIatmQnt

(time, YC, XC)

float32

dask.array<chunksize=(1, 320, 2160), meta=np.ndarray>

long_name :: Net atmospheric heat flux, >0 decreases theta
standard_name :: SIatmQnt
units :: W/m^2





  
      Array  Chunk 
  
  
     Bytes  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

SIdHbATC

(time, YC, XC)

float32

dask.array<chunksize=(1, 320, 2160), meta=np.ndarray>

long_name :: HEFF rate of change by atm flux over sea ice
standard_name :: SIdHbATC
units :: m/s





  
      Array  Chunk 
  
  
     Bytes  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

SIdHbATO

(time, YC, XC)

float32

dask.array<chunksize=(1, 320, 2160), meta=np.ndarray>

long_name :: HEFF rate of change by open ocn atm flux
standard_name :: SIdHbATO
units :: m/s





  
      Array  Chunk 
  
  
     Bytes  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

SIdHbOCN

(time, YC, XC)

float32

dask.array<chunksize=(1, 320, 2160), meta=np.ndarray>

long_name :: HEFF rate of change by ocean ice flux
standard_name :: SIdHbOCN
units :: m/s





  
      Array  Chunk 
  
  
     Bytes  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

SIdSbATC

(time, YC, XC)

float32

dask.array<chunksize=(1, 320, 2160), meta=np.ndarray>

long_name :: HSNOW rate of change by atm flux over sea ice
standard_name :: SIdSbATC
units :: m/s





  
      Array  Chunk 
  
  
     Bytes  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

SIdSbOCN

(time, YC, XC)

float32

dask.array<chunksize=(1, 320, 2160), meta=np.ndarray>

long_name :: HSNOW rate of change by ocean ice flux
standard_name :: SIdSbOCN
units :: m/s





  
      Array  Chunk 
  
  
     Bytes  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

SIempmr

(time, YC, XC)

float32

dask.array<chunksize=(1, 320, 2160), meta=np.ndarray>

long_name :: Ocean surface freshwater flux, > 0 increases salt
standard_name :: SIempmr
units :: kg/m^2/s





  
      Array  Chunk 
  
  
     Bytes  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

SIfu

(time, YC, XG)

float32

dask.array<chunksize=(1, 320, 2160), meta=np.ndarray>

long_name :: SEAICE zonal surface wind stress, >0 increases uVel
mate :: SIfv
standard_name :: SIfu
units :: N/m^2





  
      Array  Chunk 
  
  
     Bytes  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray

SIfv

(time, YG, XC)

float32

dask.array<chunksize=(1, 320, 2160), meta=np.ndarray>

long_name :: SEAICE merid. surface wind stress, >0 increases vVel
mate :: SIfu
standard_name :: SIfv
units :: N/m^2





  
      Array  Chunk 
  
  
     Bytes  1.21 GB   2.76 MB 
     Shape  (438, 320, 2160)   (1, 320, 2160) 
     Count  439 Tasks  438 Chunks 
     Type  float32  numpy.ndarray