# loading saved data
for temp_option in range(0, temperature_change_options_length):
# selecting the temperature change
min_temperature = temperature_change_options[temp_option]
mean_ec_y_array[temp_option+1] = np.loadtxt("saved_data/mean_EC_yvalue_"+str(min_temperature)+"_degree_warming_cmip6cmip5.csv", delimiter=",")
lower_ec_limit_array[temp_option+1] = np.loadtxt("saved_data/EC_lower_limit_"+str(min_temperature)+"_degree_warming_cmip6cmip5.csv", delimiter=",")
upper_ec_limit_array[temp_option+1] = np.loadtxt("saved_data/EC_upper_limit_"+str(min_temperature)+"_degree_warming_cmip6cmip5.csv", delimiter=",")
#%% Observational soil carbon
# regrid cube
regrid_cube = iris.load_cube('/home/links/rmv203/obs_datasets/Tair_WFDEI_ann.nc')
regrid_cube = time_average(regrid_cube)
regrid_cube.coord('latitude').guess_bounds()
regrid_cube.coord('longitude').guess_bounds()
regrid_modelcube = regrid_cube.copy()
# observational land frac
landfraction_obs = combine_netCDF_observations('/home/links/rmv203/obs_datasets/luc4c_landmask.nc', 'mask')
# observational soil carbon
observational_Cs_data = np.load('saved_variables/observational_Cs_data.npy')
observational_Cs_mask = np.load('saved_variables/observational_Cs_mask.npy')
observational_Cs0 = np.ma.masked_array(observational_Cs_data, mask=observational_Cs_mask)
# global total of observational soil carbon
observational_Cs0_cube = numpy_to_cube(observational_Cs0, regrid_modelcube, 2)
observational_Cs0_cube = global_total(observational_Cs0_cube, landfrac=landfraction_obs, latlon_cons=None)
observational_Cs0_data = observational_Cs0_cube.data
# Heterotrophic Respiration (RH)
rh_historical_cube = combine_netCDF_cmip5('/home/rmv203/cmip5_data/rh_Lmon_'+model+'_historical*', 'heterotrophic_respiration_carbon_flux', model)
rh_historical_cube = open_netCDF(rh_historical_cube)
# Soil Carbon (cSoil)
cSoil_historical_cube = combine_netCDF_cmip5('/home/rmv203/cmip5_data/cSoil_Lmon_'+model+'_historical*', 'soil_carbon_content', model)
cSoil_historical_cube = open_netCDF(cSoil_historical_cube)
# Near Surface Air Temperature (tas)
tas_historical_cube = combine_netCDF_cmip5('/home/rmv203/cmip5_data/tas_Amon_'+model+'_historical*', 'air_temperature', model)
tas_historical_cube = open_netCDF(tas_historical_cube)
# Select historical time period
rh_historical_cube = select_time(rh_historical_cube, lower_historical, upper_historical)
cSoil_historical_cube = select_time(cSoil_historical_cube, lower_historical, upper_historical)
tas_historical_cube = select_time(tas_historical_cube, lower_historical, upper_historical)
# Time average
rh_historical_cube = time_average(rh_historical_cube)
cSoil_historical_cube = time_average(cSoil_historical_cube)
tas_historical_cube = time_average(tas_historical_cube)
# Converting from cubes to numpy_arrays
rh_historical_data = rh_historical_cube.data
cSoil_historical_data = cSoil_historical_cube.data
tas_historical_data = tas_historical_cube.data
# save to use later
historical_tas_save_data = tas_historical_data - 273.15
cSoil_historical_save_cube = cSoil_historical_cube.copy()
historical_rh_save_data = rh_historical_data*86400.*365.
# Calculating Soil Turnover Time (tau_s)
tau_s_data_historical = cSoil_historical_data / (rh_historical_data*86400.*365.)
cSoil_historical_cube = combine_netCDF_time_overlap(
'/home/rmv203/cmip6_data/cSoil_Emon_' + model + '_historical*', model)
cSoil_historical_cube = open_netCDF(cSoil_historical_cube)
# Near Surface Air Temperature (tas)
tas_historical_cube = combine_netCDF_time_overlap(
'/home/rmv203/cmip6_data/tas_Amon_' + model + '_historical*', model)
tas_historical_cube = open_netCDF(tas_historical_cube)
# Select historical time period
rh_historical_cube = select_time(rh_historical_cube, lower_historical,
upper_historical)
cSoil_historical_cube = select_time(cSoil_historical_cube,
lower_historical, upper_historical)
tas_historical_cube = select_time(tas_historical_cube, lower_historical,
upper_historical)
# Time average
rh_historical_time_av_cube = time_average(rh_historical_cube)
cSoil_historical_time_av_cube = time_average(cSoil_historical_cube)
tas_historical_time_av_cube = time_average(tas_historical_cube)
# Converting from cubes to numpy_arrays
rh_historical_time_av_data = rh_historical_time_av_cube.data
cSoil_historical_time_av_data = cSoil_historical_time_av_cube.data
tas_historical_time_av_data = tas_historical_time_av_cube.data
# Calculating Soil Turnover Time
tau_s_data_historical = cSoil_historical_time_av_data / (
rh_historical_time_av_data * 86400. * 365.)
tau_s_masked_data_historical = ma.masked_where(
np.logical_or(tau_s_data_historical < 1, tau_s_data_historical > 1e4),
tau_s_data_historical)
#%%
#
observational_rh_data = np.load('saved_variables/observational_rh_data.npy')
observational_rh_mask = np.load('saved_variables/observational_rh_mask.npy')
observational_rh = np.ma.masked_array(observational_rh_data,
mask=observational_rh_mask)
# loading observational land fraction
landfraction_obs = combine_netCDF_observations(
'/home/links/rmv203/obs_datasets/luc4c_landmask.nc', 'mask')
#%%
# Loading regrid cube
regrid_cube = iris.load_cube(
'/home/links/rmv203/obs_datasets/Tair_WFDEI_ann.nc')
regrid_cube = time_average(regrid_cube)
regrid_cube.coord('latitude').guess_bounds()
regrid_cube.coord('longitude').guess_bounds()
regrid_modelcube = regrid_cube.copy()
# correct lat and lon dimensions
n_lat = regrid_cube.coord('latitude').points
n_lon = regrid_cube.coord('longitude').points
#%%
# inputs
lower_historical = 1995
upper_historical = 2005
region_global = [0, 360, -90, 90]
'xtick.color': 'k',
'ytick.color': 'k',
'axes.labelsize': 34,
'xtick.labelsize': 34,
'ytick.labelsize': 34,
'font.size': 34,
'text.usetex': False,
"svg.fonttype": 'none'
}
plt.rcParams.update(params)
#%% regrid cube
regrid_cube = iris.load_cube(
'/home/links/rmv203/obs_datasets/Tair_WFDEI_ann.nc')
regrid_cube = time_average(regrid_cube)
regrid_cube.coord('latitude').guess_bounds()
regrid_cube.coord('longitude').guess_bounds()
regrid_modelcube = regrid_cube
# correct lat and lon dimensions
n_lat = regrid_cube.coord('latitude').points
n_lon = regrid_cube.coord('longitude').points
#%% Observational datasets
# Soil carbon
# ncscd
ncscd_file = Dataset(
'/home/links/rmv203/obs_datasets/NCSCDV22_soilc_0.5x0.5.nc')
ncscd_data = ncscd_file.variables['soilc'][:]
# hwsd
print(rcp, model)
#%% modelled historical
# Soil Carbon (cSoil)
cSoil_historical_cube_new = combine_netCDF_cmip5('/home/rmv203/cmip5_data/cSoil_Lmon_'+model+'_historical*', 'soil_carbon_content', model)
cSoil_historical_cube_new = open_netCDF(cSoil_historical_cube_new)
# Near Surface Air Temperature (tas)
tas_historical_cube = combine_netCDF_cmip5('/home/rmv203/cmip5_data/tas_Amon_'+model+'_historical*', 'air_temperature', model)
tas_historical_cube = open_netCDF(tas_historical_cube)
# Select historical time period
cSoil_historical_cube_new = select_time(cSoil_historical_cube_new, lower_historical, upper_historical)
tas_historical_cube = select_time(tas_historical_cube, lower_historical, upper_historical)
# Time average
cSoil_historical_time_av_cube_new = time_average(cSoil_historical_cube_new)
tas_historical_cube = time_average(tas_historical_cube)
tas_historical_data = tas_historical_cube.data
# Converting from cubes to numpy_arrays
cSoil_historical_time_av_data_new = cSoil_historical_time_av_cube_new.data
#%% Modelled Future
# Soil Carbon (cSoil)
cSoil_cube = combine_netCDF_cmip5('/home/rmv203/cmip5_data/cSoil_Lmon_'+model+'_'+rcp+'_*', 'soil_carbon_content', model)
cSoil_cube = open_netCDF(cSoil_cube)
# Near Surface Air Temperature (tas)
tas_cube = combine_netCDF_cmip5('/home/rmv203/cmip5_data/tas_Amon_'+model+'_'+rcp+'_*', 'air_temperature', model)
tas_cube = open_netCDF(tas_cube)