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.

Args:

lat_aux_gridxarray.DataArray, numpy.ndarray:

Latitude grid for transport diagnostics.

a_wvelxarray.DataArray, numpy.ndarray:

Area weighted Eulerian-mean vertical velocity [TAREA*WVEL].

a_bolusxarray.DataArray, numpy.ndarray:

Area weighted Eddy-induced (bolus) vertical velocity [TAREA*WISOP].

a_submesoxarray.DataArray, numpy.ndarray:

Area weighted submeso vertical velocity [TAREA*WSUBM].

tlatxarray.DataArray, numpy.ndarray:

Array of t-grid latitudes.

rmlakxarray.DataArray, numpy.ndarray:

Basin index number: [0]=Globe, [1]=Atlantic

msgnumpy.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.

metabool:

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 – A multi-dimensional array of size [moc_comp] x [n_transport_reg] x [kdepth] x [nyaux] where:

  • moc_comp refers to the three components returned

  • n_transport_reg refers to the Globe and Atlantic

  • kdepth is the the number of vertical levels of the work arrays

  • nyaux is the size of the lat_aux_grid

Return type

xarray.DataArray, numpy.ndarray:

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)