import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm, rcParams, colors
from matplotlib import gridspec as gspec
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib.font_manager import FontProperties
import matplotlib.path as mpat
import matplotlib.ticker as mticker
import matplotlib.patches as mpatches
from matplotlib.lines import Line2D
#%%
# 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 datasets
# Observational temperature
obs_temp_file = iris.load_cube(
'/home/links/rmv203/obs_datasets/Tair_WFDEI_ann.nc')
obs_temp_file = select_time(obs_temp_file, 2001, 2010)
obs_temp_file = time_average(obs_temp_file)
obs_temp = obs_temp_file.data
obs_temp = np.ma.masked_where(obs_temp > 60, obs_temp)
current_temp = obs_temp.copy()
#
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_variable(
'/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]
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.)
from matplotlib import gridspec as gspec
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib.font_manager import FontProperties
import matplotlib.path as mpat
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import matplotlib.ticker as mticker
import matplotlib.patches as mpatches
from matplotlib.lines import Line2D
#%%
# cube for regridding
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 datasets
# Observational soil carbon
# ncscd
ncscd_file = Dataset(
'/home/links/rmv203/obs_datasets/NCSCDV22_soilc_0.5x0.5.nc')
ncscd_data = ncscd_file.variables['soilc'][:]
n_lat_ncscd = ncscd_file.variables['lat'][:]
n_lon_ncscd = ncscd_file.variables['lon'][:]
# 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)
'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_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)
#%%
