cesm_mom6_example

CESM MOM6 Ocean Model Example Data

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["cesm_mom6_example"].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

uploader_github	gustavo-marques
uploader_email	gmarques@ucar.edu
url	https://github.com/NCAR/MOM6-cases
tags	['ocean', 'model']

Dataset Contents

xarray.Dataset

Dimensions:
- lath: 458
- latq: 458
- lonh: 540
- lonq: 540
- nv: 2
- scalar_axis: 1
- time: 24
- xh: 540
- xq: 540
- yh: 458
- yq: 458
- z_i: 35
- z_l: 34

Coordinates: (17)

geolat

(yh, xh)

float64

dask.array<chunksize=(458, 540), meta=np.ndarray>

long_name :: latitude at tracer (T) points
units :: degree





  
      Array  Chunk 
  
  
     Bytes  1.98 MB   1.98 MB 
     Shape  (458, 540)   (458, 540) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

geolatb

(yq, xq)

float64

dask.array<chunksize=(458, 540), meta=np.ndarray>

long_name :: latitude at corner (Bu) points
units :: degree





  
      Array  Chunk 
  
  
     Bytes  1.98 MB   1.98 MB 
     Shape  (458, 540)   (458, 540) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

geolon

(yh, xh)

float64

dask.array<chunksize=(458, 540), meta=np.ndarray>

long_name :: longitude at tracer (T) points
units :: degree





  
      Array  Chunk 
  
  
     Bytes  1.98 MB   1.98 MB 
     Shape  (458, 540)   (458, 540) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

geolonb

(yq, xq)

float64

dask.array<chunksize=(458, 540), meta=np.ndarray>

long_name :: longitude at corner (Bu) points
units :: degree





  
      Array  Chunk 
  
  
     Bytes  1.98 MB   1.98 MB 
     Shape  (458, 540)   (458, 540) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

lath
(lath)
float64
-79.2 -79.08 -78.95 ... 87.71 87.74
cartesian_axis :
Y
long_name :
Latitude
units :
degrees_north
```
array([-79.202602, -79.076995, -78.949944, ...,  87.641507,  87.706191,
        87.738663])
```
latq
(latq)
float64
-79.14 -79.01 ... 87.73 87.74
cartesian_axis :
Y
long_name :
Latitude
units :
degrees_north
```
array([-79.139978, -79.013651, -78.885872, ...,  87.67781 ,  87.726499,
        87.7427  ])
```
lonh
(lonh)
float64
-286.7 -286.0 -285.3 ... 72.0 72.67
cartesian_axis :
X
long_name :
Longitude
units :
degrees_east
```
array([-286.666667, -286.      , -285.333333, ...,   71.333333,   72.      ,
         72.666667])
```
lonq
(lonq)
float64
-286.3 -285.7 -285.0 ... 72.33 73.0
cartesian_axis :
X
long_name :
Longitude
units :
degrees_east
```
array([-286.333333, -285.666667, -285.      , ...,   71.666667,   72.333333,
         73.      ])
```
nv
(nv)
float64
1.0 2.0
cartesian_axis :
N
long_name :
vertex number
units :
none
```
array([1., 2.])
```
scalar_axis
(scalar_axis)
float64
0.0
cartesian_axis :
N
long_name :
none
units :
none
```
array([0.])
```

time

(time)

object

0001-01-16 12:00:00 ... 0002-12-16 12:00:00

bounds :: time_bnds
calendar_type :: NOLEAP
cartesian_axis :: T
long_name :: time

array([cftime.DatetimeNoLeap(1, 1, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(1, 2, 15, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(1, 3, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(1, 4, 16, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(1, 5, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(1, 6, 16, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(1, 7, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(1, 8, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(1, 9, 16, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(1, 10, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(1, 11, 16, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(1, 12, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(2, 1, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(2, 2, 15, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(2, 3, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(2, 4, 16, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(2, 5, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(2, 6, 16, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(2, 7, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(2, 8, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(2, 9, 16, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(2, 10, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(2, 11, 16, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(2, 12, 16, 12, 0, 0, 0)], dtype=object)

xh
(xh)
float64
-286.7 -286.0 -285.3 ... 72.0 72.67
cartesian_axis :
X
long_name :
h point nominal longitude
units :
degrees_east
```
array([-286.666667, -286.      , -285.333333, ...,   71.333333,   72.      ,
         72.666667])
```
xq
(xq)
float64
-286.3 -285.7 -285.0 ... 72.33 73.0
cartesian_axis :
X
long_name :
q point nominal longitude
units :
degrees_east
```
array([-286.333333, -285.666667, -285.      , ...,   71.666667,   72.333333,
         73.      ])
```
yh
(yh)
float64
-79.2 -79.08 -78.95 ... 87.71 87.74
cartesian_axis :
Y
long_name :
h point nominal latitude
units :
degrees_north
```
array([-79.202602, -79.076995, -78.949944, ...,  87.641507,  87.706191,
        87.738663])
```
yq
(yq)
float64
-79.14 -79.01 ... 87.73 87.74
cartesian_axis :
Y
long_name :
q point nominal latitude
units :
degrees_north
```
array([-79.139978, -79.013651, -78.885872, ...,  87.67781 ,  87.726499,
        87.7427  ])
```

z_i

(z_i)

float64

0.0 5.0 15.0 ... 5.75e+03 6.25e+03

cartesian_axis :: Z
long_name :: Depth at interface
positive :: down
units :: meters

array([0.000e+00, 5.000e+00, 1.500e+01, 2.500e+01, 4.000e+01, 6.250e+01,
       8.750e+01, 1.125e+02, 1.375e+02, 1.750e+02, 2.250e+02, 2.750e+02,
       3.500e+02, 4.500e+02, 5.500e+02, 6.500e+02, 7.500e+02, 8.500e+02,
       9.500e+02, 1.050e+03, 1.150e+03, 1.250e+03, 1.350e+03, 1.450e+03,
       1.625e+03, 1.875e+03, 2.250e+03, 2.750e+03, 3.250e+03, 3.750e+03,
       4.250e+03, 4.750e+03, 5.250e+03, 5.750e+03, 6.250e+03])

z_l

(z_l)

float64

2.5 10.0 20.0 ... 5.5e+03 6e+03

cartesian_axis :: Z
edges :: z_i
long_name :: Depth at cell center
positive :: down
units :: meters

array([2.5000e+00, 1.0000e+01, 2.0000e+01, 3.2500e+01, 5.1250e+01, 7.5000e+01,
       1.0000e+02, 1.2500e+02, 1.5625e+02, 2.0000e+02, 2.5000e+02, 3.1250e+02,
       4.0000e+02, 5.0000e+02, 6.0000e+02, 7.0000e+02, 8.0000e+02, 9.0000e+02,
       1.0000e+03, 1.1000e+03, 1.2000e+03, 1.3000e+03, 1.4000e+03, 1.5375e+03,
       1.7500e+03, 2.0625e+03, 2.5000e+03, 3.0000e+03, 3.5000e+03, 4.0000e+03,
       4.5000e+03, 5.0000e+03, 5.5000e+03, 6.0000e+03])

Data variables: (23)

(time, z_l, yh, xh)

float64

dask.array<chunksize=(1, 34, 458, 540), meta=np.ndarray>

cell_measures :: area: area_t
cell_methods :: area:mean z_l:mean yh:mean xh:mean time: mean
long_name :: Layer kinetic energy per unit mass
time_avg_info :: average_T1,average_T2,average_DT
units :: m2 s-2





  
      Array  Chunk 
  
  
     Bytes  1.61 GB   67.27 MB 
     Shape  (24, 34, 458, 540)   (1, 34, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

KPP_OBLdepth

(time, yh, xh)

float64

dask.array<chunksize=(1, 458, 540), meta=np.ndarray>

cell_measures :: area: area_t
cell_methods :: area:mean yh:mean xh:mean time: mean
long_name :: Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
time_avg_info :: average_T1,average_T2,average_DT
units :: meter





  
      Array  Chunk 
  
  
     Bytes  47.49 MB   1.98 MB 
     Shape  (24, 458, 540)   (1, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

MLD_0125

(time, yh, xh)

float64

dask.array<chunksize=(1, 458, 540), meta=np.ndarray>

cell_measures :: area: area_t
cell_methods :: area:mean yh:mean xh:mean time: mean
long_name :: Mixed layer depth (delta rho = 0.125)
time_avg_info :: average_T1,average_T2,average_DT
units :: m





  
      Array  Chunk 
  
  
     Bytes  47.49 MB   1.98 MB 
     Shape  (24, 458, 540)   (1, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

SSH

(time, yh, xh)

float64

dask.array<chunksize=(1, 458, 540), meta=np.ndarray>

cell_measures :: area: area_t
cell_methods :: area:mean yh:mean xh:mean time: mean
long_name :: Sea Surface Height
time_avg_info :: average_T1,average_T2,average_DT
units :: m





  
      Array  Chunk 
  
  
     Bytes  47.49 MB   1.98 MB 
     Shape  (24, 458, 540)   (1, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

SSS

(time, yh, xh)

float64

dask.array<chunksize=(1, 458, 540), meta=np.ndarray>

cell_measures :: area: area_t
cell_methods :: area:mean yh:mean xh:mean time: mean
long_name :: Sea Surface Salinity
time_avg_info :: average_T1,average_T2,average_DT
units :: psu





  
      Array  Chunk 
  
  
     Bytes  47.49 MB   1.98 MB 
     Shape  (24, 458, 540)   (1, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

SST

(time, yh, xh)

float64

dask.array<chunksize=(1, 458, 540), meta=np.ndarray>

cell_measures :: area: area_t
cell_methods :: area:mean yh:mean xh:mean time: mean
long_name :: Sea Surface Temperature
time_avg_info :: average_T1,average_T2,average_DT
units :: degC





  
      Array  Chunk 
  
  
     Bytes  47.49 MB   1.98 MB 
     Shape  (24, 458, 540)   (1, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

average_DT

(time)

timedelta64[ns]

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

long_name :: Length of average period





  
      Array  Chunk 
  
  
     Bytes  192 B   8 B 
     Shape  (24,)   (1,) 
     Count  25 Tasks  24 Chunks 
     Type  timedelta64[ns]  numpy.ndarray

average_T1

(time)

object

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

long_name :: Start time for average period





  
      Array  Chunk 
  
  
     Bytes  192 B   8 B 
     Shape  (24,)   (1,) 
     Count  25 Tasks  24 Chunks 
     Type  object  numpy.ndarray

average_T2

(time)

object

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

long_name :: End time for average period





  
      Array  Chunk 
  
  
     Bytes  192 B   8 B 
     Shape  (24,)   (1,) 
     Count  25 Tasks  24 Chunks 
     Type  object  numpy.ndarray

friver

(time, yh, xh)

float64

dask.array<chunksize=(1, 458, 540), meta=np.ndarray>

cell_measures :: area: area_t
cell_methods :: area:mean yh:mean xh:mean time: mean
long_name :: Water Flux into Sea Water From Rivers
standard_name :: water_flux_into_sea_water_from_rivers
time_avg_info :: average_T1,average_T2,average_DT
units :: kg m-2 s-1





  
      Array  Chunk 
  
  
     Bytes  47.49 MB   1.98 MB 
     Shape  (24, 458, 540)   (1, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

(time, z_l, yh, xh)

float64

dask.array<chunksize=(1, 34, 458, 540), meta=np.ndarray>

cell_measures :: area: area_t
cell_methods :: area:mean z_l:sum yh:mean xh:mean time: mean
long_name :: Layer Thickness
time_avg_info :: average_T1,average_T2,average_DT
units :: m





  
      Array  Chunk 
  
  
     Bytes  1.61 GB   67.27 MB 
     Shape  (24, 34, 458, 540)   (1, 34, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

hfds

(time, yh, xh)

float64

dask.array<chunksize=(1, 458, 540), meta=np.ndarray>

cell_measures :: area: area_t
cell_methods :: area:mean yh:mean xh:mean time: mean
long_name :: Surface ocean heat flux from SW+LW+latent+sensible+masstransfer+frazil
standard_name :: surface_downward_heat_flux_in_sea_water
time_avg_info :: average_T1,average_T2,average_DT
units :: W m-2





  
      Array  Chunk 
  
  
     Bytes  47.49 MB   1.98 MB 
     Shape  (24, 458, 540)   (1, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

hfsnthermds

(time, yh, xh)

float64

dask.array<chunksize=(1, 458, 540), meta=np.ndarray>

cell_measures :: area: area_t
cell_methods :: area:mean yh:mean xh:mean time: mean
long_name :: Latent Heat to Melt Frozen Precipitation
standard_name :: heat_flux_into_sea_water_due_to_snow_thermodynamics
time_avg_info :: average_T1,average_T2,average_DT
units :: W m-2





  
      Array  Chunk 
  
  
     Bytes  47.49 MB   1.98 MB 
     Shape  (24, 458, 540)   (1, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

rhoinsitu

(time, z_l, yh, xh)

float64

dask.array<chunksize=(1, 34, 458, 540), meta=np.ndarray>

cell_measures :: area: area_t
cell_methods :: area:mean z_l:mean yh:mean xh:mean time: mean
long_name :: In situ density
time_avg_info :: average_T1,average_T2,average_DT
units :: kg m-3





  
      Array  Chunk 
  
  
     Bytes  1.61 GB   67.27 MB 
     Shape  (24, 34, 458, 540)   (1, 34, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

salt

(time, z_l, yh, xh)

float64

dask.array<chunksize=(1, 34, 458, 540), meta=np.ndarray>

cell_measures :: area: area_t
cell_methods :: area:mean z_l:mean yh:mean xh:mean time: mean
long_name :: Salinity
time_avg_info :: average_T1,average_T2,average_DT
units :: psu





  
      Array  Chunk 
  
  
     Bytes  1.61 GB   67.27 MB 
     Shape  (24, 34, 458, 540)   (1, 34, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

soga

(time, scalar_axis)

float64

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

cell_methods :: time: mean
long_name :: Global Mean Ocean Salinity
standard_name :: sea_water_salinity
time_avg_info :: average_T1,average_T2,average_DT
units :: psu





  
      Array  Chunk 
  
  
     Bytes  192 B   8 B 
     Shape  (24, 1)   (1, 1) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

taux

(time, yh, xq)

float64

dask.array<chunksize=(1, 458, 540), meta=np.ndarray>

cell_methods :: yh:mean xq:point time: mean
interp_method :: none
long_name :: Zonal surface stress from ocean interactions with atmos and ice
standard_name :: surface_downward_x_stress
time_avg_info :: average_T1,average_T2,average_DT
units :: Pa





  
      Array  Chunk 
  
  
     Bytes  47.49 MB   1.98 MB 
     Shape  (24, 458, 540)   (1, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

tauy

(time, yq, xh)

float64

dask.array<chunksize=(1, 458, 540), meta=np.ndarray>

cell_methods :: yq:point xh:mean time: mean
interp_method :: none
long_name :: Meridional surface stress ocean interactions with atmos and ice
standard_name :: surface_downward_y_stress
time_avg_info :: average_T1,average_T2,average_DT
units :: Pa





  
      Array  Chunk 
  
  
     Bytes  47.49 MB   1.98 MB 
     Shape  (24, 458, 540)   (1, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

temp

(time, z_l, yh, xh)

float64

dask.array<chunksize=(1, 34, 458, 540), meta=np.ndarray>

cell_measures :: area: area_t
cell_methods :: area:mean z_l:mean yh:mean xh:mean time: mean
long_name :: Potential Temperature
time_avg_info :: average_T1,average_T2,average_DT
units :: degC





  
      Array  Chunk 
  
  
     Bytes  1.61 GB   67.27 MB 
     Shape  (24, 34, 458, 540)   (1, 34, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

thetaoga

(time, scalar_axis)

float64

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

cell_methods :: time: mean
long_name :: Global Mean Ocean Potential Temperature
standard_name :: sea_water_potential_temperature
time_avg_info :: average_T1,average_T2,average_DT
units :: degC





  
      Array  Chunk 
  
  
     Bytes  192 B   8 B 
     Shape  (24, 1)   (1, 1) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

time_bnds

(time, nv)

timedelta64[ns]

dask.array<chunksize=(1, 2), meta=np.ndarray>

long_name :: time axis boundaries
calendar :: NOLEAP





  
      Array  Chunk 
  
  
     Bytes  384 B   16 B 
     Shape  (24, 2)   (1, 2) 
     Count  25 Tasks  24 Chunks 
     Type  timedelta64[ns]  numpy.ndarray

(time, z_l, yh, xq)

float64

dask.array<chunksize=(1, 34, 458, 540), meta=np.ndarray>

cell_methods :: z_l:mean yh:mean xq:point time: mean
interp_method :: none
long_name :: Zonal velocity
time_avg_info :: average_T1,average_T2,average_DT
units :: m s-1





  
      Array  Chunk 
  
  
     Bytes  1.61 GB   67.27 MB 
     Shape  (24, 34, 458, 540)   (1, 34, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

(time, z_l, yq, xh)

float64

dask.array<chunksize=(1, 34, 458, 540), meta=np.ndarray>

cell_methods :: z_l:mean yq:point xh:mean time: mean
interp_method :: none
long_name :: Meridional velocity
time_avg_info :: average_T1,average_T2,average_DT
units :: m s-1





  
      Array  Chunk 
  
  
     Bytes  1.61 GB   67.27 MB 
     Shape  (24, 34, 458, 540)   (1, 34, 458, 540) 
     Count  25 Tasks  24 Chunks 
     Type  float64  numpy.ndarray

Attributes: (5)
associated_files :
area_t: g.c2b6.GNYF.T62_t061.melt_potential.003.mom6.static.nc
filename :
g.c2b6.GNYF.T62_t061.melt_potential.003.mom6.h_0001_01.nc
grid_tile :
N/A
grid_type :
regular
title :
MOM6 g.c2b6.GNYF.T62_t061.melt_potential.003 Experiment