geocat.comp.moc_globe_atl#
- geocat.comp.moc_globe_atl(lat_aux_grid, a_wvel, a_bolus, a_submeso, t_lat, rmlak, msg=None, meta=False)#
Facilitates calculating the meridional overturning circulation for the globe and Atlantic.
- Parameters:
lat_aux_grid (
xarray.DataArray
,numpy.ndarray
) – Latitude grid for transport diagnostics.a_wvel (
xarray.DataArray
,numpy.ndarray
) – Area weighted Eulerian-mean vertical velocity [TAREA x WVEL
].a_bolus (
xarray.DataArray
,numpy.ndarray
) – Area weighted Eddy-induced (bolus) vertical velocity [TAREA x WISOP
].a_submeso (
xarray.DataArray
,numpy.ndarray
) – Area weighted submeso vertical velocity [TAREA x WSUBM
].tlat (
xarray.DataArray
,numpy.ndarray
) – Array of t-grid latitudes.rmlak (
xarray.DataArray
,numpy.ndarray
) – Basin index number: [0]=Globe, [1]=Atlanticmsg (
numpy.number
) – A numpy scalar value that represent a missing value. This argument allows a user to use a missing value scheme other than NaN or masked arrays, similar to what NCL allows.meta (
bool
) – If set to True and the input array is an Xarray, the metadata from the input array will be copied to the output array; default is False. Warning: this option is not currently supported.
- Returns:
fo (
xarray.DataArray
,numpy.ndarray
) – A multi-dimensional array of size [moc_comp
] x [n_transport_reg
] x [kdepth
] x [nyaux
] where:moc_comp
refers to the three components returnedn_transport_reg
refers to the Globe and Atlantickdepth
is the the number of vertical levels of the work arraysnyaux
is the size of thelat_aux_grid
Examples
# Input data can be read from a data set as follows: import xarray as xr ds = xr.open_dataset("input.nc") lat_aux_grid = ds.lat_aux_grid a_wvel = ds.a_wvel a_bolus = ds.a_bolus a_submeso = ds.a_submeso tlat = ds.tlat rmlak = ds.rmlak # (1) Calling with xArray inputs and default arguments (Missing value = np.nan, NO meta information) out_arr = moc_globe_atl(lat_aux_grid, a_wvel, a_bolus, a_submeso, tlat, rmlak) # (2) Calling with Numpy inputs and default arguments (Missing value = np.nan, NO meta information) out_arr = moc_globe_atl(lat_aux_grid.values, a_wvel.values, a_bolus.values, a_submeso.values, tlat.values, rmlak.values) # (3) Calling with xArray inputs and user-defined arguments (Missing value = np.nan, NO meta information) out_arr = moc_globe_atl(lat_aux_grid, a_wvel, a_bolus, a_submeso, tlat, rmlak, msg=-99.0, meta=True) # (4) Calling with Numpy inputs and user-defined arguments (Missing value = np.nan, NO meta information) out_arr = moc_globe_atl(lat_aux_grid.values, a_wvel.values, a_bolus.values, a_submeso.values, tlat.values, rmlak.values, msg=-99.0, meta=True)