CESM_POP_hires_RCP8_5
NCAR CESM POP model run BRCP85C5CN_ne120_t12_pop62.c13b17.asdphys.001
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean/CESM_POP.yaml")
ds = cat["CESM_POP_hires_RCP8_5"].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
time_period | 2000-2050 |
tags | ['ocean', 'model'] |
Dataset Contents
xarray.Dataset
- nlat: 2400
- nlon: 3600
- time: 16401
- z_t: 62
- z_t_150m: 15
- z_w: 62
- z_w_bot: 62
- z_w_top: 62
- ANGLE(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- angle grid makes with latitude line
- units :
- radians
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - ANGLET(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- angle grid makes with latitude line on T grid
- units :
- radians
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - DXT(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- x-spacing centered at T points
- units :
- centimeters
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - DXU(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- x-spacing centered at U points
- units :
- centimeters
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - DYT(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- y-spacing centered at T points
- units :
- centimeters
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - DYU(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- y-spacing centered at U points
- units :
- centimeters
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - HT(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- ocean depth at T points
- units :
- centimeter
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - HTE(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- cell widths on East sides of T cell
- units :
- centimeters
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - HTN(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- cell widths on North sides of T cell
- units :
- centimeters
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - HU(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- ocean depth at U points
- units :
- centimeter
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - HUS(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- cell widths on South sides of U cell
- units :
- centimeters
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - HUW(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- cell widths on West sides of U cell
- units :
- centimeters
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - KMT(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- k Index of Deepest Grid Cell on T Grid
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - KMU(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- k Index of Deepest Grid Cell on U Grid
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - REGION_MASK(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- basin index number (signed integers)
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - T0_Kelvin()float64...
- long_name :
- Zero Point for Celsius
- units :
- degK
array(273.15)
- TAREA(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- area of T cells
- units :
- centimeter^2
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - TLAT(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- array of t-grid latitudes
- units :
- degrees_north
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - TLONG(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- array of t-grid longitudes
- units :
- degrees_east
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - UAREA(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- area of U cells
- units :
- centimeter^2
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - ULAT(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- array of u-grid latitudes
- units :
- degrees_north
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - ULONG(nlat, nlon)float64dask.array<chunksize=(2400, 3600), meta=np.ndarray>
- long_name :
- array of u-grid longitudes
- units :
- degrees_east
Array Chunk Bytes 69.12 MB 69.12 MB Shape (2400, 3600) (2400, 3600) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - cp_air()float64...
- long_name :
- Heat Capacity of Air
- units :
- joule/kg/degK
array(1004.64)
- cp_sw()float64...
- long_name :
- Specific Heat of Sea Water
- units :
- erg/g/K
array(39960000.)
- days_in_norm_year()timedelta64[ns]...
- long_name :
- Calendar Length
array(31536000000000000, dtype='timedelta64[ns]')
- dz(z_t)float32dask.array<chunksize=(62,), meta=np.ndarray>
- long_name :
- thickness of layer k
- units :
- centimeters
Array Chunk Bytes 248 B 248 B Shape (62,) (62,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - dzw(z_w)float32dask.array<chunksize=(62,), meta=np.ndarray>
- long_name :
- midpoint of k to midpoint of k+1
- units :
- centimeters
Array Chunk Bytes 248 B 248 B Shape (62,) (62,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - fwflux_factor()float64...
- long_name :
- Convert Net Fresh Water Flux to Salt Flux (in model units)
array(0.0001)
- grav()float64...
- long_name :
- Acceleration Due to Gravity
- units :
- centimeter/s^2
array(980.616)
- heat_to_PW()float64...
- long_name :
- Convert Heat Flux to Petawatts
array(4.186e-15)
- hflux_factor()float64...
- long_name :
- Convert Heat and Solar Flux to Temperature Flux
array(2.439086e-05)
- latent_heat_fusion()float64...
- long_name :
- Latent Heat of Fusion
- units :
- erg/g
array(3.337e+09)
- latent_heat_vapor()float64...
- long_name :
- Latent Heat of Vaporization
- units :
- J/kg
array(2501000.)
- mass_to_Sv()float64...
- long_name :
- Convert Mass Flux to Sverdrups
array(1.e-12)
- momentum_factor()float64...
- long_name :
- Convert Windstress to Velocity Flux
array(10.)
- nsurface_t()float64...
- long_name :
- Number of Ocean T Points at Surface
array(5413142.)
- nsurface_u()float64...
- long_name :
- Number of Ocean U Points at Surface
array(5371534.)
- ocn_ref_salinity()float64...
- long_name :
- Ocean Reference Salinity
- units :
- g/kg
array(34.7)
- omega()float64...
- long_name :
- Earths Angular Velocity
- units :
- 1/second
array(7.292124e-05)
- ppt_to_salt()float64...
- long_name :
- Convert Salt in g/kg to gram/gram
array(0.001)
- radius()float64...
- long_name :
- Earths Radius
- units :
- centimeters
array(6.37122e+08)
- rho_air()float64...
- long_name :
- Ambient Air Density
- units :
- kg/m^3
array(1.292318)
- rho_fw()float64...
- long_name :
- Density of Fresh Water
- units :
- gram/centimeter^3
array(1.)
- rho_sw()float64...
- long_name :
- Density of Sea Water
- units :
- gram/centimeter^3
array(1.026)
- salinity_factor()float64...
array(-0.00347)
- salt_to_Svppt()float64...
- long_name :
- Convert Salt Flux to Sverdrups*g/kg
array(1.e-09)
- salt_to_mmday()float64...
- long_name :
- Convert Salt to Water (millimeters/day)
array(315360.)
- salt_to_ppt()float64...
- long_name :
- Convert Salt in gram/gram to g/kg
array(1000.)
- sea_ice_salinity()float64...
- long_name :
- Salinity of Sea Ice
- units :
- g/kg
array(4.)
- sflux_factor()float64...
- long_name :
- Convert Salt Flux to Salt Flux (in model units)
array(0.1)
- sound()float64...
- long_name :
- Speed of Sound
- units :
- centimeter/s
array(150000.)
- stefan_boltzmann()float64...
- long_name :
- Stefan-Boltzmann Constant
- units :
- watt/m^2/degK^4
array(5.67e-08)
- time(time)object2006-01-02 00:00:00 ... 2051-01-01 00:00:00
- bounds :
- time_bound
- long_name :
- time
array([cftime.DatetimeNoLeap(2006, 1, 2, 0, 0, 0, 0), cftime.DatetimeNoLeap(2006, 1, 3, 0, 0, 0, 0), cftime.DatetimeNoLeap(2006, 1, 4, 0, 0, 0, 0), ..., cftime.DatetimeNoLeap(2050, 12, 30, 0, 0, 0, 0), cftime.DatetimeNoLeap(2050, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(2051, 1, 1, 0, 0, 0, 0)], dtype=object)
- vonkar()float64...
- long_name :
- von Karman Constant
array(0.4)
- z_t(z_t)float32500.0 1500.0 ... 587499.06
- long_name :
- depth from surface to midpoint of layer
- positive :
- down
- units :
- centimeters
- valid_max :
- 587499.0625
- valid_min :
- 500.0
array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03, 5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03, 1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04, 1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04, 2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04, 2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04, 4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04, 6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04, 1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05, 1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05, 3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05, 4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05, 5.624991e+05, 5.874991e+05], dtype=float32)
- z_t_150m(z_t_150m)float32500.0 1500.0 ... 13500.0 14500.0
- long_name :
- depth from surface to midpoint of layer
- positive :
- down
- units :
- centimeters
- valid_max :
- 14500.0
- valid_min :
- 500.0
array([ 500., 1500., 2500., 3500., 4500., 5500., 6500., 7500., 8500., 9500., 10500., 11500., 12500., 13500., 14500.], dtype=float32)
- z_w(z_w)float320.0 1000.0 ... 549999.06 574999.06
- long_name :
- depth from surface to top of layer
- positive :
- down
- units :
- centimeters
- valid_max :
- 574999.0625
- valid_min :
- 0.0
array([ 0. , 1000. , 2000. , 3000. , 4000. , 5000. , 6000. , 7000. , 8000. , 9000. , 10000. , 11000. , 12000. , 13000. , 14000. , 15000. , 16000. , 17019.682, 18076.129, 19182.125, 20349.932, 21592.344, 22923.312, 24358.453, 25915.58 , 27615.26 , 29481.47 , 31542.373, 33831.227, 36387.473, 39258.047, 42498.887, 46176.656, 50370.688, 55174.91 , 60699.668, 67072.86 , 74439.805, 82960.695, 92804.35 , 104136.82 , 117104.016, 131809.36 , 148290.08 , 166499.2 , 186301.44 , 207487.39 , 229803.9 , 252990.4 , 276809.84 , 301067.06 , 325613.84 , 350344.88 , 375189.2 , 400101.16 , 425052.47 , 450026.06 , 475012. , 500004.7 , 525000.94 , 549999.06 , 574999.06 ], dtype=float32)
- z_w_bot(z_w_bot)float321000.0 2000.0 ... 599999.06
- long_name :
- depth from surface to bottom of layer
- positive :
- down
- units :
- centimeters
- valid_max :
- 599999.0625
- valid_min :
- 1000.0
array([ 1000. , 2000. , 3000. , 4000. , 5000. , 6000. , 7000. , 8000. , 9000. , 10000. , 11000. , 12000. , 13000. , 14000. , 15000. , 16000. , 17019.682, 18076.129, 19182.125, 20349.932, 21592.344, 22923.312, 24358.453, 25915.58 , 27615.26 , 29481.47 , 31542.373, 33831.227, 36387.473, 39258.047, 42498.887, 46176.656, 50370.688, 55174.91 , 60699.668, 67072.86 , 74439.805, 82960.695, 92804.35 , 104136.82 , 117104.016, 131809.36 , 148290.08 , 166499.2 , 186301.44 , 207487.39 , 229803.9 , 252990.4 , 276809.84 , 301067.06 , 325613.84 , 350344.88 , 375189.2 , 400101.16 , 425052.47 , 450026.06 , 475012. , 500004.7 , 525000.94 , 549999.06 , 574999.06 , 599999.06 ], dtype=float32)
- z_w_top(z_w_top)float320.0 1000.0 ... 549999.06 574999.06
- long_name :
- depth from surface to top of layer
- positive :
- down
- units :
- centimeters
- valid_max :
- 574999.0625
- valid_min :
- 0.0
array([ 0. , 1000. , 2000. , 3000. , 4000. , 5000. , 6000. , 7000. , 8000. , 9000. , 10000. , 11000. , 12000. , 13000. , 14000. , 15000. , 16000. , 17019.682, 18076.129, 19182.125, 20349.932, 21592.344, 22923.312, 24358.453, 25915.58 , 27615.26 , 29481.47 , 31542.373, 33831.227, 36387.473, 39258.047, 42498.887, 46176.656, 50370.688, 55174.91 , 60699.668, 67072.86 , 74439.805, 82960.695, 92804.35 , 104136.82 , 117104.016, 131809.36 , 148290.08 , 166499.2 , 186301.44 , 207487.39 , 229803.9 , 252990.4 , 276809.84 , 301067.06 , 325613.84 , 350344.88 , 375189.2 , 400101.16 , 425052.47 , 450026.06 , 475012. , 500004.7 , 525000.94 , 549999.06 , 574999.06 ], dtype=float32)
- SHF_2(time, nlat, nlon)float32dask.array<chunksize=(1, 2400, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- grid_loc :
- 2110
- long_name :
- Total Surface Heat Flux, Including SW
- units :
- watt/m^2
Array Chunk Bytes 566.82 GB 34.56 MB Shape (16401, 2400, 3600) (1, 2400, 3600) Count 16402 Tasks 16401 Chunks Type float32 numpy.ndarray