model_delta_Cs_cube = global_total_percentage(
model_delta_Cs_cube, landfrac=landfraction, latlon_cons=None)
estimated_delta_Cs_data = estimated_delta_Cs_cube.data
model_delta_Cs_data = model_delta_Cs_cube.data
#%%
# saving delta Cs values
x_array[temp_option, rcp_option, model_i] = estimated_delta_Cs_data
y_array[temp_option, rcp_option, model_i] = model_delta_Cs_data
#%%
# finding the observational derived constraint
# historical model temperature
historical_tas_cube = numpy_to_cube(historical_tas, cSoil_cube, 2)
historical_tas_cube_regrid = regrid_model(historical_tas_cube,
regrid_cube)
historical_tas_data_regrid = historical_tas_cube_regrid.data
# future model temperature
model_future_temp_cube_regrid = regrid_model(
save_model_temp_cube, regrid_cube)
model_future_temp_data_regrid = model_future_temp_cube_regrid.data - 273.15
# deriving future 'real world' temperature (model change + observations)
observational_future_temp = (
model_future_temp_data_regrid -
historical_tas_data_regrid) + observational_temperature
# Calculating new tau_s with observational relationship
historical_tau_obs = poly_relationship_obs(
observational_temperature)
if cube.var_name == 'longitude':
lon = cube
if cube.var_name == 'latitude':
lat = cube
if cube.var_name == 'Mean':
mean_cube = cube
# Takes the latitude and longitude ‘cubes’ and makes them in to coordinates
lat_aux = iris.coords.AuxCoord(lat.data, standard_name=lat.name(), units=lat.units)
lon_aux = iris.coords.AuxCoord(lon.data, standard_name=lon.name(), units=lon.units)
# Add latitude and longitude as coordinates
mean_cube.add_aux_coord(lat_aux, data_dims=(0))
mean_cube.add_aux_coord(lon_aux, data_dims=(1))
iris.util.promote_aux_coord_to_dim_coord(mean_cube, 'latitude')
iris.util.promote_aux_coord_to_dim_coord(mean_cube, 'longitude')
# regrid cube
rh_cube = regrid_model(mean_cube, regrid_modelcube)
rh_data_regridded = rh_cube.data
rh_data_regridded = rh_data_regridded*1e-3*365
rh_data_regridded = ma.masked_invalid(rh_data_regridded)
rh_data_regridded = np.ma.masked_where(rh_data_regridded<=0, rh_data_regridded)
card_rh = rh_data_regridded.copy()
# MODIS Net Primary Production (NPP)
npp_file = Dataset('/home/links/rmv203/obs_datasets/MOD17A3_Science_NPP_mean_00_14_regridhalfdegree.nc')
npp_data = npp_file.variables['npp'][:]*1e-3
#npp_data_new = np.ma.masked_where(rh_data_regridded==0, npp_data)
#npp_data_new = np.ma.masked_where(npp_data_new<=0, npp_data_new)
# Raich 2002 Soil Respiration (Rs)