GFDL_CM2_6_control_ocean_budgets

GFDL CM2.6 climate model control run monthly ocean budgets 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_control_ocean_budgets"].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_ocean: 50
- sw_ocean: 50
- time: 20
- xt_ocean: 3600
- xu_ocean: 3600
- yt_ocean: 2700
- yu_ocean: 2700

Coordinates: (12)

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.])
```

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_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

0181-07-01 17:00:00 ... 0200-07-01 14:00:00

bounds :: time_bounds
calendar_type :: JULIAN
cartesian_axis :: T
long_name :: time

array([cftime.DatetimeJulian(181, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(182, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(183, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(184, 7, 1, 14, 0, 0, 0),
       cftime.DatetimeJulian(185, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(186, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(187, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(188, 7, 1, 14, 0, 0, 0),
       cftime.DatetimeJulian(189, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(190, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(191, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(192, 7, 1, 14, 0, 0, 0),
       cftime.DatetimeJulian(193, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(194, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(195, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(196, 7, 1, 14, 0, 0, 0),
       cftime.DatetimeJulian(197, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(198, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(199, 7, 1, 17, 0, 0, 0),
       cftime.DatetimeJulian(200, 7, 1, 14, 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: (20)

frazil_2d

(time, yt_ocean, xt_ocean)

float32

dask.array<chunksize=(1, 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  777.60 MB   38.88 MB 
     Shape  (20, 2700, 3600)   (1, 2700, 3600) 
     Count  21 Tasks  20 Chunks 
     Type  float32  numpy.ndarray

net_sfc_heating

(time, yt_ocean, xt_ocean)

float32

dask.array<chunksize=(1, 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  777.60 MB   38.88 MB 
     Shape  (20, 2700, 3600)   (1, 2700, 3600) 
     Count  21 Tasks  20 Chunks 
     Type  float32  numpy.ndarray

rho_dzt

(time, st_ocean, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
long_name :: t-cell rho*thickness
standard_name :: sea_water_mass_per_unit_area
time_avg_info :: average_T1,average_T2,average_DT
units :: (kg/m^3)*m
valid_range :: [-100000000.0, 100000000.0]





  
      Array  Chunk 
  
  
     Bytes  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 Chunks 
     Type  float32  numpy.ndarray

sfc_hflux_coupler

(time, yt_ocean, xt_ocean)

float32

dask.array<chunksize=(1, 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  777.60 MB   38.88 MB 
     Shape  (20, 2700, 3600)   (1, 2700, 3600) 
     Count  21 Tasks  20 Chunks 
     Type  float32  numpy.ndarray

sfc_hflux_pme

(time, yt_ocean, xt_ocean)

float32

dask.array<chunksize=(1, 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  777.60 MB   38.88 MB 
     Shape  (20, 2700, 3600)   (1, 2700, 3600) 
     Count  21 Tasks  20 Chunks 
     Type  float32  numpy.ndarray

sw_heat

(time, st_ocean, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
long_name :: penetrative shortwave heating
standard_name :: downwelling_shortwave_flux_in_sea_water
time_avg_info :: average_T1,average_T2,average_DT
units :: W/m^2
valid_range :: [-10000000000.0, 10000000000.0]





  
      Array  Chunk 
  
  
     Bytes  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 Chunks 
     Type  float32  numpy.ndarray

swflx

(time, yt_ocean, xt_ocean)

float32

dask.array<chunksize=(1, 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  777.60 MB   38.88 MB 
     Shape  (20, 2700, 3600)   (1, 2700, 3600) 
     Count  21 Tasks  20 Chunks 
     Type  float32  numpy.ndarray

temp_advection

(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*rho*dzt*advection tendency
time_avg_info :: average_T1,average_T2,average_DT
units :: Watts/m^2
valid_range :: [-9.999999843067494e+17, 9.999999843067494e+17]





  
      Array  Chunk 
  
  
     Bytes  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 Chunks 
     Type  float32  numpy.ndarray

temp_eta_smooth

(time, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
long_name :: surface smoother for temp
time_avg_info :: average_T1,average_T2,average_DT
units :: Watts/m^2
valid_range :: [-1.0000000272564224e+16, 1.0000000272564224e+16]





  
      Array  Chunk 
  
  
     Bytes  777.60 MB   38.88 MB 
     Shape  (20, 2700, 3600)   (1, 2700, 3600) 
     Count  21 Tasks  20 Chunks 
     Type  float32  numpy.ndarray

temp_nonlocal_KPP

(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*rho*dzt*nonlocal tendency from KPP
time_avg_info :: average_T1,average_T2,average_DT
units :: Watts/m^2
valid_range :: [-10000000000.0, 10000000000.0]





  
      Array  Chunk 
  
  
     Bytes  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 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  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 Chunks 
     Type  float32  numpy.ndarray

temp_submeso

(time, st_ocean, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
long_name :: rho*dzt*cp*submesoscale tendency (heating)
time_avg_info :: average_T1,average_T2,average_DT
units :: Watts/m^2
valid_range :: [-10000000000.0, 10000000000.0]





  
      Array  Chunk 
  
  
     Bytes  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 Chunks 
     Type  float32  numpy.ndarray

temp_tendency

(time, st_ocean, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
long_name :: time tendency for tracer Potential temperature
time_avg_info :: average_T1,average_T2,average_DT
units :: Watts/m^2
valid_range :: [-1.0000000272564224e+16, 1.0000000272564224e+16]





  
      Array  Chunk 
  
  
     Bytes  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 Chunks 
     Type  float32  numpy.ndarray

temp_vdiffuse_impl

(time, st_ocean, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
long_name :: implicit vert diffusion of heat
time_avg_info :: average_T1,average_T2,average_DT
units :: Watts/m^2
valid_range :: [-1.0000000272564224e+16, 1.0000000272564224e+16]





  
      Array  Chunk 
  
  
     Bytes  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 Chunks 
     Type  float32  numpy.ndarray

temp_xflux_adv

(time, st_ocean, yt_ocean, xu_ocean)

float32

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

cell_methods :: time: mean
long_name :: cp*rho*dzt*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  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 Chunks 
     Type  float32  numpy.ndarray

temp_yflux_adv

(time, st_ocean, yu_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
long_name :: cp*rho*dzt*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  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 Chunks 
     Type  float32  numpy.ndarray

temp_yflux_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 :: 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  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 Chunks 
     Type  float32  numpy.ndarray

temp_zflux_adv

(time, sw_ocean, yt_ocean, xt_ocean)

float32

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

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





  
      Array  Chunk 
  
  
     Bytes  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 Chunks 
     Type  float32  numpy.ndarray

uhrho_et

(time, st_ocean, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
long_name :: uhrho_et on horz face of T-cells
time_avg_info :: average_T1,average_T2,average_DT
units :: (kg/m^3)*m^2/sec
valid_range :: [-1000000.0, 1000000.0]





  
      Array  Chunk 
  
  
     Bytes  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 Chunks 
     Type  float32  numpy.ndarray

wrhot

(time, sw_ocean, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
long_name :: rho*dia-surface velocity T-points
time_avg_info :: average_T1,average_T2,average_DT
units :: (kg/m^3)*m/sec
valid_range :: [-100000.0, 100000.0]





  
      Array  Chunk 
  
  
     Bytes  38.88 GB   194.40 MB 
     Shape  (20, 50, 2700, 3600)   (1, 5, 2700, 3600) 
     Count  201 Tasks  200 Chunks 
     Type  float32  numpy.ndarray

Attributes: (7)
NCO :
4.1.0
filename :
01810101.ocean_budgets.nc
grid_tile :
1
grid_type :
mosaic
history :
Tue Dec 10 21:44:49 2013: ncks --64bit --hdr_pad 15000 -A uhrho_et.tmp.nc wrhot.tmp.nc Tue Dec 10 21:15:48 2013: ncra -O -v uhrho_et,nv,time_bounds 01810101/01810101.ocean_budgets.nc 01810101/uhrho_et.tmp.nc
nco_openmp_thread_number :
1
title :
CM2.6_miniBling