MITgcm_channel_flatbottom_02km_run01_phys-mon

MITgcm channel simulations with flat bottom at 2km resolution physics field monthly mean climatology

Load in Python

from intake import open_catalog

cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean/channel.yaml")
ds  = cat["MITgcm_channel_flatbottom_02km_run01_phys-mon"].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	roxyboy
uploader_email	takaya@ldeo.columbia.edu
tags	['ocean', 'model']

Dataset Contents

xarray.Dataset

Dimensions:
- XC: 500
- XG: 500
- YC: 1000
- YG: 1000
- Z: 76
- Zl: 76
- Zp1: 77
- Zu: 76
- time: 12

Coordinates: (26)

Depth

(YC, XC)

float32

dask.array<chunksize=(1000, 500), meta=np.ndarray>

coordinate :: XC YC
long_name :: ocean depth
standard_name :: ocean_depth
units :: m





  
      Array  Chunk 
  
  
     Bytes  2.00 MB   2.00 MB 
     Shape  (1000, 500)   (1000, 500) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

PHrefC

(Z)

float32

dask.array<chunksize=(76,), meta=np.ndarray>

long_name :: Reference Hydrostatic Pressure
standard_name :: cell_reference_pressure
units :: m2 s-2





  
      Array  Chunk 
  
  
     Bytes  304 B   304 B 
     Shape  (76,)   (76,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

PHrefF

(Zp1)

float32

dask.array<chunksize=(77,), meta=np.ndarray>

long_name :: Reference Hydrostatic Pressure
standard_name :: cell_reference_pressure
units :: m2 s-2





  
      Array  Chunk 
  
  
     Bytes  308 B   308 B 
     Shape  (77,)   (77,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

XC
(XC)
float32
1000.0 3000.0 ... 997000.0 999000.0
axis :
X
coordinate :
YC XC
long_name :
longitude
standard_name :
longitude
units :
degrees_east
```
array([  1000.,   3000.,   5000., ..., 995000., 997000., 999000.],
      dtype=float32)
```
XG
(XG)
float32
0.0 2000.0 ... 996000.0 998000.0
axis :
X
c_grid_axis_shift :
-0.5
coordinate :
YG XG
long_name :
longitude
standard_name :
longitude_at_f_location
units :
degrees_east
```
array([     0.,   2000.,   4000., ..., 994000., 996000., 998000.],
      dtype=float32)
```
YC
(YC)
float32
1000.0 3000.0 ... 1999000.0
axis :
Y
coordinate :
YC XC
long_name :
latitude
standard_name :
latitude
units :
degrees_north
```
array([1.000e+03, 3.000e+03, 5.000e+03, ..., 1.995e+06, 1.997e+06, 1.999e+06],
      dtype=float32)
```
YG
(YG)
float32
0.0 2000.0 ... 1996000.0 1998000.0
axis :
Y
c_grid_axis_shift :
-0.5
long_name :
latitude
standard_name :
latitude_at_f_location
units :
degrees_north
```
array([      0.,    2000.,    4000., ..., 1994000., 1996000., 1998000.],
      dtype=float32)
```

(Z)

float32

-0.5 -1.57 ... -2757.325 -2912.665

axis :: Z
long_name :: vertical coordinate of cell center
positive :: down
standard_name :: depth
units :: m

array([-5.000000e-01, -1.570000e+00, -2.790000e+00, -4.185000e+00,
       -5.780000e+00, -7.595000e+00, -9.660000e+00, -1.201000e+01,
       -1.468000e+01, -1.770500e+01, -2.112500e+01, -2.499000e+01,
       -2.934500e+01, -3.424000e+01, -3.972500e+01, -4.585500e+01,
       -5.269000e+01, -6.028000e+01, -6.868500e+01, -7.796500e+01,
       -8.817500e+01, -9.937000e+01, -1.116000e+02, -1.249150e+02,
       -1.393650e+02, -1.549900e+02, -1.718250e+02, -1.899000e+02,
       -2.092350e+02, -2.298550e+02, -2.517700e+02, -2.749850e+02,
       -2.995050e+02, -3.253200e+02, -3.524200e+02, -3.807900e+02,
       -4.104100e+02, -4.412550e+02, -4.733050e+02, -5.065400e+02,
       -5.409350e+02, -5.764650e+02, -6.131100e+02, -6.508550e+02,
       -6.896850e+02, -7.295950e+02, -7.705850e+02, -8.126600e+02,
       -8.558350e+02, -9.001350e+02, -9.455950e+02, -9.922600e+02,
       -1.040180e+03, -1.089425e+03, -1.140080e+03, -1.192235e+03,
       -1.246005e+03, -1.301520e+03, -1.358920e+03, -1.418375e+03,
       -1.480075e+03, -1.544225e+03, -1.611060e+03, -1.680845e+03,
       -1.753875e+03, -1.830475e+03, -1.911015e+03, -1.995905e+03,
       -2.085595e+03, -2.180595e+03, -2.281470e+03, -2.388845e+03,
       -2.503415e+03, -2.625955e+03, -2.757325e+03, -2.912665e+03],
      dtype=float32)

(Zl)

float32

0.0 -1.0 ... -2689.32 -2825.33

axis :: Z
c_grid_axis_shift :: -0.5
long_name :: vertical coordinate of upper cell interface
positive :: down
standard_name :: depth_at_upper_w_location
units :: m

array([ 0.00000e+00, -1.00000e+00, -2.14000e+00, -3.44000e+00, -4.93000e+00,
       -6.63000e+00, -8.56000e+00, -1.07600e+01, -1.32600e+01, -1.61000e+01,
       -1.93100e+01, -2.29400e+01, -2.70400e+01, -3.16500e+01, -3.68300e+01,
       -4.26200e+01, -4.90900e+01, -5.62900e+01, -6.42700e+01, -7.31000e+01,
       -8.28300e+01, -9.35200e+01, -1.05220e+02, -1.17980e+02, -1.31850e+02,
       -1.46880e+02, -1.63100e+02, -1.80550e+02, -1.99250e+02, -2.19220e+02,
       -2.40490e+02, -2.63050e+02, -2.86920e+02, -3.12090e+02, -3.38550e+02,
       -3.66290e+02, -3.95290e+02, -4.25530e+02, -4.56980e+02, -4.89630e+02,
       -5.23450e+02, -5.58420e+02, -5.94510e+02, -6.31710e+02, -6.70000e+02,
       -7.09370e+02, -7.49820e+02, -7.91350e+02, -8.33970e+02, -8.77700e+02,
       -9.22570e+02, -9.68620e+02, -1.01590e+03, -1.06446e+03, -1.11439e+03,
       -1.16577e+03, -1.21870e+03, -1.27331e+03, -1.32973e+03, -1.38811e+03,
       -1.44864e+03, -1.51151e+03, -1.57694e+03, -1.64518e+03, -1.71651e+03,
       -1.79124e+03, -1.86971e+03, -1.95232e+03, -2.03949e+03, -2.13170e+03,
       -2.22949e+03, -2.33345e+03, -2.44424e+03, -2.56259e+03, -2.68932e+03,
       -2.82533e+03], dtype=float32)

Zp1

(Zp1)

float32

0.0 -1.0 -2.14 ... -2825.33 -3000.0

axis :: Z
c_grid_axis_shift :: [-0.5, 0.5]
long_name :: vertical coordinate of cell interface
positive :: down
standard_name :: depth_at_w_location
units :: m

array([ 0.00000e+00, -1.00000e+00, -2.14000e+00, -3.44000e+00, -4.93000e+00,
       -6.63000e+00, -8.56000e+00, -1.07600e+01, -1.32600e+01, -1.61000e+01,
       -1.93100e+01, -2.29400e+01, -2.70400e+01, -3.16500e+01, -3.68300e+01,
       -4.26200e+01, -4.90900e+01, -5.62900e+01, -6.42700e+01, -7.31000e+01,
       -8.28300e+01, -9.35200e+01, -1.05220e+02, -1.17980e+02, -1.31850e+02,
       -1.46880e+02, -1.63100e+02, -1.80550e+02, -1.99250e+02, -2.19220e+02,
       -2.40490e+02, -2.63050e+02, -2.86920e+02, -3.12090e+02, -3.38550e+02,
       -3.66290e+02, -3.95290e+02, -4.25530e+02, -4.56980e+02, -4.89630e+02,
       -5.23450e+02, -5.58420e+02, -5.94510e+02, -6.31710e+02, -6.70000e+02,
       -7.09370e+02, -7.49820e+02, -7.91350e+02, -8.33970e+02, -8.77700e+02,
       -9.22570e+02, -9.68620e+02, -1.01590e+03, -1.06446e+03, -1.11439e+03,
       -1.16577e+03, -1.21870e+03, -1.27331e+03, -1.32973e+03, -1.38811e+03,
       -1.44864e+03, -1.51151e+03, -1.57694e+03, -1.64518e+03, -1.71651e+03,
       -1.79124e+03, -1.86971e+03, -1.95232e+03, -2.03949e+03, -2.13170e+03,
       -2.22949e+03, -2.33345e+03, -2.44424e+03, -2.56259e+03, -2.68932e+03,
       -2.82533e+03, -3.00000e+03], dtype=float32)

(Zu)

float32

-1.0 -2.14 ... -2825.33 -3000.0

axis :: Z
c_grid_axis_shift :: 0.5
long_name :: vertical coordinate of lower cell interface
positive :: down
standard_name :: depth_at_lower_w_location
units :: m

array([-1.00000e+00, -2.14000e+00, -3.44000e+00, -4.93000e+00, -6.63000e+00,
       -8.56000e+00, -1.07600e+01, -1.32600e+01, -1.61000e+01, -1.93100e+01,
       -2.29400e+01, -2.70400e+01, -3.16500e+01, -3.68300e+01, -4.26200e+01,
       -4.90900e+01, -5.62900e+01, -6.42700e+01, -7.31000e+01, -8.28300e+01,
       -9.35200e+01, -1.05220e+02, -1.17980e+02, -1.31850e+02, -1.46880e+02,
       -1.63100e+02, -1.80550e+02, -1.99250e+02, -2.19220e+02, -2.40490e+02,
       -2.63050e+02, -2.86920e+02, -3.12090e+02, -3.38550e+02, -3.66290e+02,
       -3.95290e+02, -4.25530e+02, -4.56980e+02, -4.89630e+02, -5.23450e+02,
       -5.58420e+02, -5.94510e+02, -6.31710e+02, -6.70000e+02, -7.09370e+02,
       -7.49820e+02, -7.91350e+02, -8.33970e+02, -8.77700e+02, -9.22570e+02,
       -9.68620e+02, -1.01590e+03, -1.06446e+03, -1.11439e+03, -1.16577e+03,
       -1.21870e+03, -1.27331e+03, -1.32973e+03, -1.38811e+03, -1.44864e+03,
       -1.51151e+03, -1.57694e+03, -1.64518e+03, -1.71651e+03, -1.79124e+03,
       -1.86971e+03, -1.95232e+03, -2.03949e+03, -2.13170e+03, -2.22949e+03,
       -2.33345e+03, -2.44424e+03, -2.56259e+03, -2.68932e+03, -2.82533e+03,
       -3.00000e+03], dtype=float32)

drC

(Zp1)

float64

dask.array<chunksize=(77,), meta=np.ndarray>

long_name :: cell z size
standard_name :: cell_z_size_at_w_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  616 B   616 B 
     Shape  (77,)   (77,) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

drF

(Z)

float32

dask.array<chunksize=(76,), meta=np.ndarray>

long_name :: cell z size
standard_name :: cell_z_size
units :: m





  
      Array  Chunk 
  
  
     Bytes  304 B   304 B 
     Shape  (76,)   (76,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dxC

(YC, XG)

float32

dask.array<chunksize=(1000, 500), meta=np.ndarray>

coordinate :: YC XG
long_name :: cell x size
standard_name :: cell_x_size_at_u_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  2.00 MB   2.00 MB 
     Shape  (1000, 500)   (1000, 500) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dxG

(YG, XC)

float32

dask.array<chunksize=(1000, 500), meta=np.ndarray>

coordinate :: YG XC
long_name :: cell x size
standard_name :: cell_x_size_at_v_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  2.00 MB   2.00 MB 
     Shape  (1000, 500)   (1000, 500) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dyC

(YG, XC)

float32

dask.array<chunksize=(1000, 500), meta=np.ndarray>

coordinate :: YG XC
long_name :: cell y size
standard_name :: cell_y_size_at_v_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  2.00 MB   2.00 MB 
     Shape  (1000, 500)   (1000, 500) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dyG

(YC, XG)

float32

dask.array<chunksize=(1000, 500), meta=np.ndarray>

coordinate :: YC XG
long_name :: cell y size
standard_name :: cell_y_size_at_u_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  2.00 MB   2.00 MB 
     Shape  (1000, 500)   (1000, 500) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

hFacC

(Z, YC, XC)

float32

dask.array<chunksize=(76, 1000, 500), meta=np.ndarray>

long_name :: vertical fraction of open cell
standard_name :: cell_vertical_fraction





  
      Array  Chunk 
  
  
     Bytes  152.00 MB   152.00 MB 
     Shape  (76, 1000, 500)   (76, 1000, 500) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

hFacS

(Z, YG, XC)

float32

dask.array<chunksize=(76, 1000, 500), meta=np.ndarray>

long_name :: vertical fraction of open cell
standard_name :: cell_vertical_fraction_at_v_location





  
      Array  Chunk 
  
  
     Bytes  152.00 MB   152.00 MB 
     Shape  (76, 1000, 500)   (76, 1000, 500) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

hFacW

(Z, YC, XG)

float32

dask.array<chunksize=(76, 1000, 500), meta=np.ndarray>

long_name :: vertical fraction of open cell
standard_name :: cell_vertical_fraction_at_u_location





  
      Array  Chunk 
  
  
     Bytes  152.00 MB   152.00 MB 
     Shape  (76, 1000, 500)   (76, 1000, 500) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

iter

(time)

int64

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

long_name :: model timestep number
standard_name :: timestep





  
      Array  Chunk 
  
  
     Bytes  96 B   8 B 
     Shape  (12,)   (1,) 
     Count  13 Tasks  12 Chunks 
     Type  int64  numpy.ndarray

(YC, XC)

float32

dask.array<chunksize=(1000, 500), meta=np.ndarray>

coordinate :: YC XC
long_name :: cell area
standard_name :: cell_area
units :: m2





  
      Array  Chunk 
  
  
     Bytes  2.00 MB   2.00 MB 
     Shape  (1000, 500)   (1000, 500) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

rAs

(YG, XC)

float32

dask.array<chunksize=(1000, 500), meta=np.ndarray>

long_name :: cell area
standard_name :: cell_area_at_v_location
units :: m2





  
      Array  Chunk 
  
  
     Bytes  2.00 MB   2.00 MB 
     Shape  (1000, 500)   (1000, 500) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

rAw

(YC, XG)

float32

dask.array<chunksize=(1000, 500), meta=np.ndarray>

coordinate :: YG XC
long_name :: cell area
standard_name :: cell_area_at_u_location
units :: m2





  
      Array  Chunk 
  
  
     Bytes  2.00 MB   2.00 MB 
     Shape  (1000, 500)   (1000, 500) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

rAz

(YG, XG)

float32

dask.array<chunksize=(1000, 500), meta=np.ndarray>

coordinate :: YG XG
long_name :: cell area
standard_name :: cell_area_at_f_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  2.00 MB   2.00 MB 
     Shape  (1000, 500)   (1000, 500) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

time

(time)

timedelta64[ns]

1650 days 1680 days ... 1980 days

axis :: T
calendar :: gregorian
long_name :: Time
standard_name :: time

array([142560000000000000, 145152000000000000, 147744000000000000,
       150336000000000000, 152928000000000000, 155520000000000000,
       158112000000000000, 160704000000000000, 163296000000000000,
       165888000000000000, 168480000000000000, 171072000000000000],
      dtype='timedelta64[ns]')

Data variables: (5)

PhHytave

(time, Z, YC, XC)

float32

dask.array<chunksize=(1, 76, 1000, 500), meta=np.ndarray>

long_name :: Hydrostatic Pressure Pot.(p/rho) Anomaly
standard_name :: sea_water_dynamic_pressue
units :: m2 s-2





  
      Array  Chunk 
  
  
     Bytes  1.82 GB   152.00 MB 
     Shape  (12, 76, 1000, 500)   (1, 76, 1000, 500) 
     Count  13 Tasks  12 Chunks 
     Type  float32  numpy.ndarray

Ttave

(time, Z, YC, XC)

float32

dask.array<chunksize=(1, 76, 1000, 500), meta=np.ndarray>

long_name :: Potential Temperature
standard_name :: sea_water_potential_temperature
units :: degree_Celcius





  
      Array  Chunk 
  
  
     Bytes  1.82 GB   152.00 MB 
     Shape  (12, 76, 1000, 500)   (1, 76, 1000, 500) 
     Count  13 Tasks  12 Chunks 
     Type  float32  numpy.ndarray

uVeltave

(time, Z, YC, XG)

float32

dask.array<chunksize=(1, 76, 1000, 500), meta=np.ndarray>

long_name :: Zonal Component of Velocity
mate :: vVeltave
standard_name :: sea_water_x_velocity
units :: m s-1





  
      Array  Chunk 
  
  
     Bytes  1.82 GB   152.00 MB 
     Shape  (12, 76, 1000, 500)   (1, 76, 1000, 500) 
     Count  13 Tasks  12 Chunks 
     Type  float32  numpy.ndarray

vVeltave

(time, Z, YG, XC)

float32

dask.array<chunksize=(1, 76, 1000, 500), meta=np.ndarray>

long_name :: Meridional Component of Velocity
mate :: uVeltave
standard_name :: sea_water_y_velocity
units :: m s-1





  
      Array  Chunk 
  
  
     Bytes  1.82 GB   152.00 MB 
     Shape  (12, 76, 1000, 500)   (1, 76, 1000, 500) 
     Count  13 Tasks  12 Chunks 
     Type  float32  numpy.ndarray

wVeltave

(time, Zl, YC, XC)

float32

dask.array<chunksize=(1, 76, 1000, 500), meta=np.ndarray>

long_name :: Vertical Component of Velocity
standard_name :: sea_water_z_velocity
units :: m s-1





  
      Array  Chunk 
  
  
     Bytes  1.82 GB   152.00 MB 
     Shape  (12, 76, 1000, 500)   (1, 76, 1000, 500) 
     Count  13 Tasks  12 Chunks 
     Type  float32  numpy.ndarray

Attributes: (0)