GFDL_CM2_6_one_percent_ocean_boundary_flux
GFDL CM2.6 climate model one-percent CO2 increase monthly ocean boundary flux 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_boundary_flux"].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
gcloudcommand 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
gcloudto 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
- nv: 2
- time: 240
- xt_ocean: 3600
- xu_ocean: 3600
- yt_ocean: 2700
- yu_ocean: 2700
- geolat_c(yu_ocean, xu_ocean)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float641.0 2.0
- cartesian_axis :
- N
- long_name :
- vertex number
- units :
- none
array([1., 2.])
- time(time)object0181-01-16 12:00:00 ... 0200-12-16 12:00:00
- bounds :
- time_bounds
- calendar_type :
- JULIAN
- cartesian_axis :
- T
- long_name :
- time
array([cftime.DatetimeJulian(181, 1, 16, 12, 0, 0, 0), cftime.DatetimeJulian(181, 2, 15, 0, 0, 0, 0), cftime.DatetimeJulian(181, 3, 16, 12, 0, 0, 0), ..., cftime.DatetimeJulian(200, 10, 16, 12, 0, 0, 0), cftime.DatetimeJulian(200, 11, 16, 0, 0, 0, 0), cftime.DatetimeJulian(200, 12, 16, 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. ])
- bottom_power_u(time, yu_ocean, xu_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Power dissipation to bottom drag in i-direction
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Watt
- valid_range :
- [-999999986991104.0, 999999986991104.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - bottom_power_v(time, yu_ocean, xu_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Power dissipation to bottom drag in j-direction
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Watt
- valid_range :
- [-999999986991104.0, 999999986991104.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - calving_melt_heat(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- heat flux needed to melt calving ice (<0 cools ocean)
- standard_name :
- heat_flux_into_sea_water_due_to_iceberg_thermodynamics
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W/m^2
- valid_range :
- [-10000000000.0, 10000000000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - ekman_heat(time, yu_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Ekman Component to heat transport
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Watts
- valid_range :
- [-10000.0, 10000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - ekman_we(time, yu_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Ekman vertical velocity averaged to wt-point
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m/s
- valid_range :
- [-100.0, 100.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - evap(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- mass flux from evaporation/condensation (>0 enters ocean)
- standard_name :
- water_evaporation_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- (kg/m^3)*(m/sec)
- valid_range :
- [-1000000.0, 1000000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - evap_heat(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- latent heat flux into ocean (<0 cools ocean)
- standard_name :
- surface_downward_latent_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W/m^2
- valid_range :
- [-10000000000.0, 10000000000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - fprec(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- snow falling onto ocean (>0 enters ocean)
- standard_name :
- snowfall_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- (kg/m^3)*(m/sec)
- valid_range :
- [-10000000000.0, 10000000000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - fprec_melt_heat(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- heat flux to melt frozen precip (<0 cools ocean)
- standard_name :
- heat_flux_into_sea_water_due_to_snow_thermodynamics
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W/m^2
- valid_range :
- [-10000000000.0, 10000000000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - frazil_2d(time, yt_ocean, xt_ocean)float32dask.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 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - ice_calving(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- mass flux of land ice calving into ocean
- standard_name :
- water_flux_into_sea_water_from_icebergs
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- (kg/m^3)*(m/sec)
- valid_range :
- [-1000000.0, 1000000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - lprec(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- liquid precip (including ice melt/form) into ocean (>0 enters ocean)
- standard_name :
- rainfall_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- (kg/m^3)*(m/sec)
- valid_range :
- [-10000000000.0, 10000000000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - lw_heat(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- longwave flux into ocean (<0 cools ocean)
- standard_name :
- surface_net_downward_longwave_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W/m^2
- valid_range :
- [-10000000000.0, 10000000000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - melt(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- water flux transferred with sea ice form/melt (>0 enters ocean)
- standard_name :
- water_flux_into_sea_water_due_to_sea_ice_thermodynamics
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- (kg/m^3)*(m/sec)
- valid_range :
- [-1000000.0, 1000000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - net_sfc_heating(time, yt_ocean, xt_ocean)float32dask.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 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - river(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- mass flux of river (runoff + calving) entering ocean
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- (kg/m^3)*(m/sec)
- valid_range :
- [-1000000.0, 1000000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - sens_heat(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- sensible heat into ocean (<0 cools ocean)
- standard_name :
- surface_downward_sensible_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W/m^2
- valid_range :
- [-10000000000.0, 10000000000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - sfc_hflux_from_calving(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- heat flux (relative to 0C) from solid land ice entering ocean
- standard_name :
- temperature_flux_due_to_icebergs_expressed_as_heat_flux_into_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Watts/m^2
- valid_range :
- [-10000.0, 10000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - sfc_hflux_from_runoff(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- heat flux (relative to 0C) from liquid river runoff
- standard_name :
- temperature_flux_due_to_runoff_expressed_as_heat_flux_into_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Watts/m^2
- valid_range :
- [-10000.0, 10000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - sfc_hflux_from_water_evap(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- heat flux from evap transfer of water across ocean surface
- standard_name :
- temperature_flux_due_to_evaporation_expressed_as_heat_flux_into_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Watts/m^2
- valid_range :
- [-10000.0, 10000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - sfc_hflux_from_water_prec(time, yt_ocean, xt_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- heat flux from precip transfer of water across ocean surface
- standard_name :
- temperature_flux_due_to_rainfall_expressed_as_heat_flux_into_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Watts/m^2
- valid_range :
- [-10000.0, 10000.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - sfc_hflux_pme(time, yt_ocean, xt_ocean)float32dask.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 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - swflx(time, yt_ocean, xt_ocean)float32dask.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 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - tau_x(time, yu_ocean, xu_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- i-directed wind stress forcing u-velocity
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- N/m^2
- valid_range :
- [-10.0, 10.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - tau_y(time, yu_ocean, xu_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- j-directed wind stress forcing v-velocity
- standard_name :
- surface_downward_y_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- N/m^2
- valid_range :
- [-10.0, 10.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - wind_power_u(time, yu_ocean, xu_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Power from wind stress in i-direction
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Watt
- valid_range :
- [-999999986991104.0, 999999986991104.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray - wind_power_v(time, yu_ocean, xu_ocean)float32dask.array<chunksize=(1, 2700, 3600), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Power from wind stress in j-direction
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Watt
- valid_range :
- [-999999986991104.0, 999999986991104.0]
Array Chunk Bytes 9.33 GB 38.88 MB Shape (240, 2700, 3600) (1, 2700, 3600) Count 241 Tasks 240 Chunks Type float32 numpy.ndarray
- filename :
- 01810101.ocean_bdy_flux.nc
- grid_tile :
- 1
- grid_type :
- mosaic
- title :
- CM2.6_miniBling