GFDL_CM2_6_one_percent_ocean

GFDL CM2.6 climate model one-percent CO2 increase 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_one_percent_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:

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

Dataset Contents

xarray.Dataset

Dimensions:
- 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

Coordinates: (14)

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

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

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

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

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.      ])
```

Data variables: (31)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

(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

(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

(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

Attributes: (4)
filename :
01810101.ocean.nc
grid_tile :
1
grid_type :
mosaic
title :
CM2.6_miniBling