from netCDF4 import Dataset
matplotlib.rcParams['font.family'] = 'sans-serif'
matplotlib.rcParams['font.sans-serif'] = 'Helvetica'
gdp_year = _env.year
sgdp_year = str(gdp_year)
p_scen = 'No-Sulfate' #aerosol removal scenario
ds = 'ERA-Interim'
if_temp = _env.odir_root + '/summary_' + ds + '/country_specific_statistics_Temp_' + ds + '_' + p_scen + '.csv'
if_gdp = _env.odir_root + '/summary_' + ds + '/country_specific_statistics_GDP_' + ds + '_' + p_scen + '_Burke.xls'
odir_plot = _env.odir_root + '/plot/'
_env.mkdirs(odir_plot)
of_plot = odir_plot + 'SOM_F1.Scatter_Changes_in_Temp_and_GDP_Sulfate.png'
itbl_temp = pd.read_csv(if_temp, index_col=0)
itbl_gdp = pd.read_excel(if_gdp, 'country-lag0')
mtbl_tg = itbl_temp[[
'Temp_mean_climatological', 'Temp_mean_noaero', 'Temp_Changes'
]].copy()
for gc in [
'iso', sgdp_year + '_gdp', sgdp_year + '_pop',
'GDP_median_benefit_ratio'
]:
mtbl_tg[gc] = itbl_gdp[gc].copy()
gdp_year = year
sgdp_year = str(gdp_year)
par = 'TREFHT'
ds = 'ERA-Interim'
rean_ttag = '2001-2018'
if_base = _env.idir_root + '/reanalysis/ERA-Interim_Surface_Temp_' + rean_ttag + '_Regridded.nc'
if_sim = _env.odir_root + '/sim_temperature/Simulated_Global_Gridded_' + par + '.nc'
if_boot_par = _env.idir_root + 'Burke2018_Replication/data/output/bootstrap.csv'
if_pop = _env.idir_root + '/pop/GPW_POP_25x19deg_2000.nc'
odir_gdp = _env.odir_root + '/gdp_' + ds + '/'
_env.mkdirs(odir_gdp)
scenarios = ['With-Aerosol', 'No-Aerosol']
#read gridded temperature
##base temperature from reanalysis data (ERA-Interim)
i_base = xr.open_dataset(if_base)
i_base = i_base.expand_dims({'ensembles': 8})
i_base.transpose('ensembles', 'lat', 'lon')
T_grid_base = i_base['t2m_mean'] - 273.15 #K to C
i_sim = xr.open_dataset(if_sim)
T_grid_wa = i_sim[par + '_' + scenarios[0] + '_ensemble'] #With-Aerosol
T_grid_na = i_sim[par + '_' + scenarios[1] + '_ensemble'] #No-Aerosol
ax.set_yticks(np.arange(-90,91,45))
ax.set_yticklabels('')
parallels = np.arange(-90.,91,45.)
m.drawparallels(parallels,labels=[True,False,False,False],dashes=[3,3],xoffset=5,linewidth = 0)
meridians = np.arange(-160,161,40.)
m.drawmeridians(meridians,labels=[True,False,False,True],dashes=[3,3],yoffset=5,linewidth = 0)
matplotlib.rcParams['font.family'] = 'sans-serif'
matplotlib.rcParams['font.sans-serif'] = 'Helvetica'
odir_plot = _env.odir_root + '/plot/'
_env.mkdirs(odir_plot)
of_plot = odir_plot + 'ED_F4.Map_Climatic_Economic_Impacts_Sulfate.png'
p_scen = 'No-Sulfate' #aerosol removal scenario
ds = 'ERA-Interim'
fig = plt.figure(figsize=(21,20))
##=========================MAP: changes in AOD=====================#
if_aod = _env.odir_root + '/sim_aodvis/Simulated_Global_Gridded_AODVIS.nc'
arr_aod = -Dataset(if_aod)['AODVIS_d_' + p_scen + '_With-Aerosol'][:]
ax = fig.add_subplot(411)
matplotlib.rcParams['font.sans-serif'] = 'Helvetica'
###main
ds = 'ERA-Interim'
p_scen = 'No-Sulfate'
b_m = 'country-lag0'
gdp_year = _env.year
sgdp_year = str(gdp_year)
idir_temp = _env.odir_root + '/sim_temperature/'
idir_gdp = _env.odir_root + '/gdp_'+ds+'/'
odir_summary = _env.odir_root + '/summary_'+ds+'/'
odir_plot = _env.odir_root + '/plot/'
_env.mkdirs(odir_summary)
of_plot = odir_plot + 'ED_F10.Box_Changes_in_Inequality_Sulfate.png'
itbl_gdp_baseline = pd.read_csv(_env.odir_root + 'basic_stats' + '/Country_Basic_Stats.csv')
itbl_gdp_baseline.sort_values([sgdp_year + '_gdpcap'],inplace=True)
tot_pop = itbl_gdp_baseline[sgdp_year + '_pop'].sum()
itbl_gdp_baseline[sgdp_year + '_gdpsum'] = 0
itbl_gdp_baseline[sgdp_year + '_popsum'] = 0
for irow, row in enumerate(itbl_gdp_baseline.index):
if irow == 0:
itbl_gdp_baseline.loc[row,sgdp_year + '_gdpsum'] = itbl_gdp_baseline.loc[row,sgdp_year + '_gdp']
itbl_gdp_baseline.loc[row, sgdp_year + '_popsum'] = itbl_gdp_baseline.loc[row,sgdp_year + '_pop']
else:
itbl_ctry_gdpcap.set_index(['Country Code'], inplace=True)
itbl_ctry_pop.set_index(['Country Code'], inplace=True)
itbl_ctry_info = itbl_ctry.copy()
itbl_ctry_info.set_index(['iso'], inplace=True)
for year in range(gdp_year, gdp_year + 1):
itbl_ctry_info[str(year) + '_gdpcap'] = itbl_ctry_gdpcap[str(year)]
itbl_ctry_info[str(year) + '_pop'] = itbl_ctry_pop[str(year)]
itbl_ctry_info[str(year) + '_gdp'] = itbl_ctry_info[
str(year) + '_gdpcap'] * itbl_ctry_info[str(year) + '_pop']
itbl_ctry_info.drop(['meantemp', 'gdp', 'growth', 'gdpcap', 'pop'],
axis=1,
inplace=True)
_env.mkdirs(_env.odir_root + '/basic_stats/')
itbl_ctry_info.to_csv(_env.odir_root + '/basic_stats/' +
'Country_Basic_Stats.csv')
omtrxs_gdp_all = {}
#calculate changes in gdp and gdp growth rate from aerosol-induced cooling
for ds in datasets:
omtrxs_gdp_all[ds] = {}
itbl_clim_temp = pd.read_csv(if_clim_temp, index_col=0)[['iso', ds]]
odir_gdp = _env.odir_root + '/gdp_' + ds + '/'
_env.mkdirs(odir_gdp)
scenarios = _env.scenarios[0:2] #['With-Aerosol', 'No-Aerosol']
'Percent changes in 80:20 ratio', 'Probability of reduced inequality'
]
models = ['Burke et al.', 'Dell et al.', 'Pretis et al.', 'DICE']
apply2s = ['GDP growth', 'GDP growth', 'GDP growth', 'GDP levels']
speciations = {
'Burke et al.':
['country-lag0', 'country-lag1', 'country-lag5', 'year', 'year-blocks'],
'Dell et al.': ['Dell'],
'Pretis et al.': ['M1', 'M2', 'M3'],
'DICE': ['DICE-2013R', 'DICE-2016R']
}
odir_tbl = _env.odir_root + '/table/'
_env.mkdirs(odir_tbl)
of_tbl = odir_tbl + 'SOM_Table1.xls'
writer = pd.ExcelWriter(of_tbl)
#===================applied functions====================
def text_fmt(median, p5, p95, digits=2):
#formating text for output table cells
tp_min = np.min([p5, p95])
tp_max = np.max([p5, p95])
return str(np.around(median, digits)) + ' (' + str(
np.around(tp_min, digits)) + ', ' + str(np.around(tp_max,
digits)) + ')'
ttag = '1950-2019'
for ens in np.arange(1, nens + 1):
if_nc = idir_cesm + par + '_' + scen + '-' + str(
ens) + '_' + ttag + '.nc'
print(if_nc)
inc = Dataset(if_nc)
par_val = (inc[par][:].data + parameters_info[par]['delta']
) * parameters_info[par]['scale']
inc.close()
i_pars[ens] = np.append(i_pars[ens], par_val, axis=0)
####output concatenated gridded parameter
_env.mkdirs(_env.odir_root + '/' + parameters_info[par]['dir'] + '/')
of_nc = _env.odir_root + '/' + parameters_info[par][
'dir'] + '/' + par + '_' + scen + '_1850-2019_ensemble' + '.nc'
_env.rmfile(of_nc)
onc = Dataset(of_nc, 'w', format='NETCDF4')
d_yr = onc.createDimension('years', (2020 - 1850))
d_lat = onc.createDimension('lat', len(lat))
d_lon = onc.createDimension('lon', len(lon))
v_d_yr = onc.createVariable('years', 'f4', ('years'))
v_d_yr[:] = np.arange(1850, 2020)
v_d_lat = onc.createVariable('lat', 'f4', ('lat'))
v_d_lat[:] = lat.data
sgdp_year = str(gdp_year)
par = 'TREFHT'
ds = 'ERA-Interim'
p_scen = 'No-Aerosol'
if_temp = _env.odir_root + '/sim_temperature/Simulated_Global_and_Country_' + par + '_20yravg.nc'
if_ctry_list = _env.idir_root + '/regioncode/Country_List.xls'
if_ctry_pr = _env.idir_root + '/historical_stat/Ctry_Poor_Rich_from_Burke.csv' #adopt country list from Burke et al. 2018
if_ctry_gdpcap = _env.idir_root + '/historical_stat/' + '/API_NY.GDP.PCAP.KD_DS2_en_csv_v2.csv'
if_ctry_pop = _env.idir_root + '/historical_stat/' + '/API_SP.POP.TOTL_DS2_en_csv_v2.csv'
odir_gdp = _env.odir_root + '/gdp_' + ds + '/'
_env.mkdirs(odir_gdp)
#climatological temperature from three datasets
if_clim_temp = _env.odir_root + 'sim_temperature/Climatological_Temp_Ctry_3ds.csv'
itbl_clim_temp = pd.read_csv(if_clim_temp, index_col=0)[['iso', ds]]
#country list
itbl_ctry_info = pd.read_csv(_env.odir_root + '/basic_stats/' +
'Country_Basic_Stats.csv')
#read global and country-level temperature
T_glob = Dataset(if_temp)['TREFHT_Global'][:, [0, 1]]
T_ctry_full = Dataset(if_temp)['TREFHT_Country'][:, :, [0, 1]]
#extract temperature for analyzed countries
T_ctry = T_ctry_full[((
sgdp_year = str(gdp_year)
par = 'TREFHT'
ds = 'ERA-Interim'
p_scen = 'No-Aerosol'
if_temp = _env.odir_root + '/sim_temperature/Simulated_Global_and_Country_' + par + '_20yravg.nc'
if_ctry_list = _env.idir_root + '/regioncode/Country_List.xls'
if_ctry_pr = _env.idir_root + '/historical_stat/Ctry_Poor_Rich_from_Burke.csv' #adopt country list from Burke et al. 2018
if_ctry_gdpcap = _env.idir_root + '/historical_stat/' + '/API_NY.GDP.PCAP.KD_DS2_en_csv_v2.csv'
if_ctry_pop = _env.idir_root + '/historical_stat/' + '/API_SP.POP.TOTL_DS2_en_csv_v2.csv'
odir_gdp = _env.odir_root + '/gdp_' + ds + '/'
_env.mkdirs(odir_gdp)
#climatological temperature from three datasets
if_clim_temp = _env.odir_root + 'sim_temperature/Climatological_Temp_Ctry_3ds.csv'
itbl_clim_temp = pd.read_csv(if_clim_temp, index_col=0)[['iso', ds]]
#country list
itbl_ctry_info = pd.read_csv(_env.odir_root + '/basic_stats/' +
'Country_Basic_Stats.csv')
#read global and country-level temperature
T_glob = Dataset(if_temp)['TREFHT_Global'][:, [0, 1]]
T_ctry_full = Dataset(if_temp)['TREFHT_Country'][:, :, [0, 1]]
#extract temperature for analyzed countries
T_ctry = T_ctry_full[((
for ctry in i_ctry_grid:
ind_ctry[int(ctry)] = _env.grid_ind_2d(i_ctry_grid[ctry])
o_ctry_pars = {}
for par in parameters:
o_ctry_pars[par] = np.zeros([len(itbl_ctry.index), nens, nscen])
for iscen, scen in enumerate(scenarios):
i_par = i_pars[par][scen].copy()
for ctry in itbl_ctry.index:
ipar_ctry = i_par[:, ind_ctry[ctry][0, :], ind_ctry[ctry][1, :]]
pop_ctry = pop[ind_ctry[ctry][0, :], ind_ctry[ctry][1, :]]
o_ctry_pars[par][ctry, :, iscen] = _env.cal_cty_mean(
ipar_ctry, pop_ctry, ctry)
#output global and country-level results#
_env.mkdirs(_env.odir_root + '/' + parameters_info[par]['dir'] + '/')
of_nc = _env.odir_root + '/' + parameters_info[par][
'dir'] + '/Simulated_Global_and_Country_' + par + '_20yravg.nc'
_env.rmfile(of_nc)
onc = Dataset(of_nc, 'w', format='NETCDF4')
d_ctry = onc.createDimension('countries', len(itbl_ctry.index))
d_year = onc.createDimension('ensembles', nens)
d_ens = onc.createDimension('scenarios', nscen)
v_par_global = onc.createVariable(par + '_Global', 'f4',
('ensembles', 'scenarios'))
v_par_global_pw = onc.createVariable(par + '_Global_PW', 'f4',
('ensembles', 'scenarios'))
v_par_ctry = onc.createVariable(par + '_Country', 'f4',
