GFDL_CM2_6_one_percent_ocean_transport

GFDL CM2.6 climate model one-percent CO2 increase monthly ocean transport 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_transport"].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:
- grid_xt_ocean: 3600
- grid_yu_ocean: 2700
- nv: 2
- potrho: 80
- potrho_edges: 81
- st_edges_ocean: 51
- st_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=(338, 450), 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   608.40 kB 
     Shape  (2700, 3600)   (338, 450) 
     Count  65 Tasks  64 Chunks 
     Type  float32  numpy.ndarray

geolat_t

(yt_ocean, xt_ocean)

float32

dask.array<chunksize=(338, 450), 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   608.40 kB 
     Shape  (2700, 3600)   (338, 450) 
     Count  65 Tasks  64 Chunks 
     Type  float32  numpy.ndarray

geolon_c

(yu_ocean, xu_ocean)

float32

dask.array<chunksize=(338, 450), 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   608.40 kB 
     Shape  (2700, 3600)   (338, 450) 
     Count  65 Tasks  64 Chunks 
     Type  float32  numpy.ndarray

geolon_t

(yt_ocean, xt_ocean)

float32

dask.array<chunksize=(338, 450), 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   608.40 kB 
     Shape  (2700, 3600)   (338, 450) 
     Count  65 Tasks  64 Chunks 
     Type  float32  numpy.ndarray

nv
(nv)
float64
1.0 2.0
cartesian_axis :
N
long_name :
vertex number
units :
none
```
array([1., 2.])
```

potrho

(potrho)

float64

1.028e+03 1.028e+03 ... 1.038e+03

cartesian_axis :: Z
edges :: potrho_edges
long_name :: potential density
positive :: down
units :: kg/m^3

array([1028.0625, 1028.1875, 1028.3125, 1028.4375, 1028.5625, 1028.6875,
       1028.8125, 1028.9375, 1029.0625, 1029.1875, 1029.3125, 1029.4375,
       1029.5625, 1029.6875, 1029.8125, 1029.9375, 1030.0625, 1030.1875,
       1030.3125, 1030.4375, 1030.5625, 1030.6875, 1030.8125, 1030.9375,
       1031.0625, 1031.1875, 1031.3125, 1031.4375, 1031.5625, 1031.6875,
       1031.8125, 1031.9375, 1032.0625, 1032.1875, 1032.3125, 1032.4375,
       1032.5625, 1032.6875, 1032.8125, 1032.9375, 1033.0625, 1033.1875,
       1033.3125, 1033.4375, 1033.5625, 1033.6875, 1033.8125, 1033.9375,
       1034.0625, 1034.1875, 1034.3125, 1034.4375, 1034.5625, 1034.6875,
       1034.8125, 1034.9375, 1035.0625, 1035.1875, 1035.3125, 1035.4375,
       1035.5625, 1035.6875, 1035.8125, 1035.9375, 1036.0625, 1036.1875,
       1036.3125, 1036.4375, 1036.5625, 1036.6875, 1036.8125, 1036.9375,
       1037.0625, 1037.1875, 1037.3125, 1037.4375, 1037.5625, 1037.6875,
       1037.8125, 1037.9375])

potrho_edges

(potrho_edges)

float64

1.028e+03 1.028e+03 ... 1.038e+03

cartesian_axis :: Z
long_name :: potential density edges
positive :: down
units :: kg/m^3

array([1028.   , 1028.125, 1028.25 , 1028.375, 1028.5  , 1028.625, 1028.75 ,
       1028.875, 1029.   , 1029.125, 1029.25 , 1029.375, 1029.5  , 1029.625,
       1029.75 , 1029.875, 1030.   , 1030.125, 1030.25 , 1030.375, 1030.5  ,
       1030.625, 1030.75 , 1030.875, 1031.   , 1031.125, 1031.25 , 1031.375,
       1031.5  , 1031.625, 1031.75 , 1031.875, 1032.   , 1032.125, 1032.25 ,
       1032.375, 1032.5  , 1032.625, 1032.75 , 1032.875, 1033.   , 1033.125,
       1033.25 , 1033.375, 1033.5  , 1033.625, 1033.75 , 1033.875, 1034.   ,
       1034.125, 1034.25 , 1034.375, 1034.5  , 1034.625, 1034.75 , 1034.875,
       1035.   , 1035.125, 1035.25 , 1035.375, 1035.5  , 1035.625, 1035.75 ,
       1035.875, 1036.   , 1036.125, 1036.25 , 1036.375, 1036.5  , 1036.625,
       1036.75 , 1036.875, 1037.   , 1037.125, 1037.25 , 1037.375, 1037.5  ,
       1037.625, 1037.75 , 1037.875, 1038.   ])

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])

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: (12)

salt_xflux_adv_int_z

(time, yt_ocean, xu_ocean)

float32

dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>

cell_methods :: time: mean
long_name :: z-integral of rho*dzt*dyt*u*tracer
time_avg_info :: average_T1,average_T2,average_DT
units :: kg/sec
valid_range :: [-9.999999843067494e+17, 9.999999843067494e+17]





  
      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_yflux_adv_int_z

(time, yu_ocean, xt_ocean)

float32

dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>

cell_methods :: time: mean
long_name :: z-integral of rho*dzt*dxt*v*tracer
time_avg_info :: average_T1,average_T2,average_DT
units :: kg/sec
valid_range :: [-9.999999843067494e+17, 9.999999843067494e+17]





  
      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_xflux_adv_int_z

(time, yt_ocean, xu_ocean)

float32

dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>

cell_methods :: time: mean
long_name :: z-integral of cp*rho*dyt*u*temp
time_avg_info :: average_T1,average_T2,average_DT
units :: Watts
valid_range :: [-9.999999843067494e+17, 9.999999843067494e+17]





  
      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_xflux_submeso_int_z

(time, yt_ocean, xu_ocean)

float32

dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>

cell_methods :: time: mean
long_name :: z-integral cp*submeso_xflux*dyt*rho_dzt*temp
time_avg_info :: average_T1,average_T2,average_DT
units :: Watt
valid_range :: [-9.999999843067494e+17, 9.999999843067494e+17]





  
      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_yflux_adv_int_z

(time, yu_ocean, xt_ocean)

float32

dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>

cell_methods :: time: mean
long_name :: z-integral of cp*rho*dxt*v*temp
time_avg_info :: average_T1,average_T2,average_DT
units :: Watts
valid_range :: [-9.999999843067494e+17, 9.999999843067494e+17]





  
      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_yflux_submeso_int_z

(time, yu_ocean, xt_ocean)

float32

dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>

cell_methods :: time: mean
long_name :: z-integral cp*submeso_yflux*dxt*rho_dzt*temp
time_avg_info :: average_T1,average_T2,average_DT
units :: Watt
valid_range :: [-9.999999843067494e+17, 9.999999843067494e+17]





  
      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

tx_trans

(time, st_ocean, yt_ocean, xu_ocean)

float32

dask.array<chunksize=(1, 5, 2700, 3600), meta=np.ndarray>

cell_methods :: time: mean
long_name :: T-cell i-mass transport
standard_name :: ocean_x_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

tx_trans_int_z

(time, yt_ocean, xu_ocean)

float32

dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>

cell_methods :: time: mean
long_name :: T-cell i-mass transport vertically summed
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  9.33 GB   116.64 MB 
     Shape  (240, 2700, 3600)   (3, 2700, 3600) 
     Count  81 Tasks  80 Chunks 
     Type  float32  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

ty_trans_int_z

(time, yu_ocean, xt_ocean)

float32

dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>

cell_methods :: time: mean
long_name :: T-cell j-mass transport vertically summed
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  9.33 GB   116.64 MB 
     Shape  (240, 2700, 3600)   (3, 2700, 3600) 
     Count  81 Tasks  80 Chunks 
     Type  float32  numpy.ndarray

ty_trans_rho

(time, potrho, grid_yu_ocean, grid_xt_ocean)

float32

dask.array<chunksize=(1, 5, 2700, 3600), meta=np.ndarray>

cell_methods :: time: mean
long_name :: T-cell j-mass transport on pot_rho
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  746.50 GB   194.40 MB 
     Shape  (240, 80, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  3841 Tasks  3840 Chunks 
     Type  float32  numpy.ndarray

ty_trans_submeso

(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 mass j-transport from submesoscale param
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

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