GFDL_CM2_6_one_percent_ocean_surface

GFDL CM2.6 climate model one-percent CO2 increase ocean surface 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_surface"].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_sub01: 1
- time: 7305
- xt_ocean: 3600
- xu_ocean: 3600
- yt_ocean: 2700
- yu_ocean: 2700
Coordinates: (7)
- nv
  (nv)
  float64
  1.0 2.0
  cartesian_axis :
  N
  long_name :
  vertex number
  units :
  none
```
array([1., 2.])
```
- st_ocean_sub01
  (st_ocean_sub01)
  float64
  5.034
  cartesian_axis :
  Z
  long_name :
  tcell zstar depth
  positive :
  down
  units :
  meters
```
array([5.03355])
```
- time
  (time)
  object
  0181-01-01 12:00:00 ... 0200-12-31 12:00:00
  bounds :
  time_bounds
  calendar_type :
  JULIAN
  cartesian_axis :
  T
  long_name :
  time
```
array([cftime.DatetimeJulian(181, 1, 1, 12, 0, 0, 0),
       cftime.DatetimeJulian(181, 1, 2, 12, 0, 0, 0),
       cftime.DatetimeJulian(181, 1, 3, 12, 0, 0, 0), ...,
       cftime.DatetimeJulian(200, 12, 29, 12, 0, 0, 0),
       cftime.DatetimeJulian(200, 12, 30, 12, 0, 0, 0),
       cftime.DatetimeJulian(200, 12, 31, 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: (14)

biomass_p

(time, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_t geolat_t
long_name :: Surface Biomass-P concentration
time_avg_info :: average_T1,average_T2,average_DT
units :: mol kg-1





  
      Array  Chunk 
  
  
     Bytes  284.02 GB   38.88 MB 
     Shape  (7305, 2700, 3600)   (1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

chl

(time, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_t geolat_t
long_name :: Surface Chl concentration
time_avg_info :: average_T1,average_T2,average_DT
units :: mol kg-1





  
      Array  Chunk 
  
  
     Bytes  284.02 GB   38.88 MB 
     Shape  (7305, 2700, 3600)   (1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

dic

(time, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_t geolat_t
long_name :: Surface DIC concentration
time_avg_info :: average_T1,average_T2,average_DT
units :: mol kg-1





  
      Array  Chunk 
  
  
     Bytes  284.02 GB   38.88 MB 
     Shape  (7305, 2700, 3600)   (1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

htotal

(time, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_t geolat_t
long_name :: Surface H+ concentration
time_avg_info :: average_T1,average_T2,average_DT
units :: mol kg-1





  
      Array  Chunk 
  
  
     Bytes  284.02 GB   38.88 MB 
     Shape  (7305, 2700, 3600)   (1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

irr_mix

(time, st_ocean_sub01, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_t geolat_t
long_name :: Mixed layer light
time_avg_info :: average_T1,average_T2,average_DT
units :: W m-2





  
      Array  Chunk 
  
  
     Bytes  284.02 GB   38.88 MB 
     Shape  (7305, 1, 2700, 3600)   (1, 1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

(time, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_t geolat_t
long_name :: Gas Exchange piston velocity fordic
time_avg_info :: average_T1,average_T2,average_DT
units :: m/sec





  
      Array  Chunk 
  
  
     Bytes  284.02 GB   38.88 MB 
     Shape  (7305, 2700, 3600)   (1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

(time, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_t geolat_t
long_name :: Surface O2 concentration
time_avg_info :: average_T1,average_T2,average_DT
units :: mol kg-1





  
      Array  Chunk 
  
  
     Bytes  284.02 GB   38.88 MB 
     Shape  (7305, 2700, 3600)   (1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

po4

(time, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_t geolat_t
long_name :: Surface PO4 concentration
time_avg_info :: average_T1,average_T2,average_DT
units :: mol kg-1





  
      Array  Chunk 
  
  
     Bytes  284.02 GB   38.88 MB 
     Shape  (7305, 2700, 3600)   (1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

sea_level

(time, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_t geolat_t
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  284.02 GB   38.88 MB 
     Shape  (7305, 2700, 3600)   (1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

sea_level_sq

(time, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_t geolat_t
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  284.02 GB   38.88 MB 
     Shape  (7305, 2700, 3600)   (1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

surface_salt

(time, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_t geolat_t
long_name :: Practical Salinity
standard_name :: sea_surface_salinity
time_avg_info :: average_T1,average_T2,average_DT
units :: psu
valid_range :: [-10.0, 100.0]





  
      Array  Chunk 
  
  
     Bytes  284.02 GB   38.88 MB 
     Shape  (7305, 2700, 3600)   (1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

surface_temp

(time, yt_ocean, xt_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_t geolat_t
long_name :: Potential temperature
standard_name :: sea_surface_temperature
time_avg_info :: average_T1,average_T2,average_DT
units :: degrees C
valid_range :: [-10.0, 500.0]





  
      Array  Chunk 
  
  
     Bytes  284.02 GB   38.88 MB 
     Shape  (7305, 2700, 3600)   (1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

usurf

(time, yu_ocean, xu_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_c geolat_c
long_name :: i-surface current
time_avg_info :: average_T1,average_T2,average_DT
units :: m/sec
valid_range :: [-10.0, 10.0]





  
      Array  Chunk 
  
  
     Bytes  284.02 GB   38.88 MB 
     Shape  (7305, 2700, 3600)   (1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

vsurf

(time, yu_ocean, xu_ocean)

float32

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

cell_methods :: time: mean
coordinates :: geolon_c geolat_c
long_name :: j-surface current
time_avg_info :: average_T1,average_T2,average_DT
units :: m/sec
valid_range :: [-10.0, 10.0]





  
      Array  Chunk 
  
  
     Bytes  284.02 GB   38.88 MB 
     Shape  (7305, 2700, 3600)   (1, 2700, 3600) 
     Count  7306 Tasks  7305 Chunks 
     Type  float32  numpy.ndarray

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