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)