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sim_anomaly_mod.py
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import glob
import pandas as pd
import xarray as xr
import statsmodels.api as sm
import numpy as np
import os
import sys
def load_mod_data(model_name, climate_name, anomaly=False):
if anomaly:
if os.path.exists(f_dir_output) == False:
os.makedirs(f_dir_output)
end_string = f'{model_name}_{climate_name}_hist_default_noirr_yield_{crop_mod[0:3]}_annual_anomaly_{yr_start}_{yr_end_model[climate_name]}.nc'
##=== for isimip
# end_string = f'{model_name}_{climate_name}_{ssp}_default_noirr_yield_{crop_mod[0:3]}_annual_anomaly_{yr_start}_{yr_end}.nc'
fn = f'{f_dir_output}/{end_string}'
ds = xr.open_dataset(fn, decode_times=False)
else:
if not flag_adj:
fn_tmp = f'{f_dir_mod}/{model_name}/{model_name}.{crop_mod}/{climate_name}'
##=== for isimip
# fn_tmp = f'{f_dir_mod}/{model_name}/{climate_name}'
else:
fn_tmp = f'{f_dir_mod}/{model_name}'
end_string = f'{model_name}*{climate_name}*_hist_default_noirr_yield_*{crop_mod[0:3]}*'
##=== for isimip
# end_string = f'{model_name}_{climate_name}_w5e5_{ssp}_2015soc_default_yield-{crop_mod[0:3]}*'
fn = glob.glob(f'{fn_tmp}/{end_string}')[0]
yr_s = int(fn.split('_')[-2])
yr_e = int(fn.split('_')[-1].split('.')[0])
ds = xr.open_dataset(fn, decode_times=False)
ds['time'] = np.arange(yr_s,yr_e+1,1)
return ds
def get_yield_ana(ds_mod, var_name, yr_start, yr_end):
mask_mod = ds_mod[var_name].sum(axis=0).values
mask = mask_mod!=0
idx = np.argwhere(mask)
# Get slope and intercept for trend
X = np.arange(yr_start,yr_end+1)
X = sm.add_constant(X)
array_slope1 = np.zeros([360,720])
array_intercept1 = np.zeros([360,720])
for n in range(idx.shape[0]):
lat_n = idx[n][0]
lon_n = idx[n][1]
y1 = ds_mod[var_name][:,lat_n,lon_n].values
mod_fit1 = sm.OLS(y1, X, missing='drop').fit()
array_intercept1[lat_n,lon_n], array_slope1[lat_n,lon_n] = \
mod_fit1.params[0], mod_fit1.params[1]
# Get anomaly
n_yr = yr_end - yr_start + 1
array_year = np.zeros([n_yr,360,720])
for y in range(yr_start,yr_end+1):
array_year[y-yr_start:,:] = y
array_trend = np.zeros([n_yr,360,720])
for y in range(yr_start,yr_end+1):
array_trend[y-yr_start:,:] = array_year[y-yr_start:,:] * array_slope1 + array_intercept1
array_ana = ds_mod[var_name].values - array_trend
mask_3d = np.broadcast_to(mask, array_ana.shape)
array_ana[~mask_3d] = np.nan
array_trend[~mask_3d] = np.nan
return array_ana, array_trend
def save_anomaly_mod(name_model,climate_name):
yr_end = yr_end_model[climate_name]
for m in name_model:
var_name = f'yield_{crop_mod[0:3]}'
##=== for isimip
# var_name = f'yield-{crop_mod[0:3]}-noirr'
ds_mod = load_mod_data(m, climate_name)
data_mod = ds_mod.sel(time=range(yr_start,yr_end+1))
array_ana, array_trend = get_yield_ana(data_mod, var_name, yr_start, yr_end)
yield_mod = ds_mod.sel(time=range(yr_start,yr_end+1))[var_name].values
ds = xr.Dataset({'yield_ana': (['time', 'lat', 'lon'], array_ana),
'yield_trend': (['time', 'lat', 'lon'], array_trend),
'yield': (['time', 'lat', 'lon'], yield_mod),
},
coords={'lon': ds_mod.lon,
'lat': ds_mod.lat,
'time': np.arange(yr_start,yr_end+1,1)
})
end_string = f'{m}_{climate_name}_hist_default_noirr_yield_{crop_mod[0:3]}_annual_anomaly_{yr_start}_{yr_end}.nc'
##=== for isimip
# end_string = f'{m}_{climate_name}_{ssp}_default_noirr_yield_{crop_mod[0:3]}_annual_anomaly_2015_2100.nc'
ds.to_netcdf(f'{f_dir_output}/{end_string}')
print(f'{end_string} saved!')
#===========================================================
if __name__ == '__main__':
# # simulation parameters
name_crop_obs = ['corn','soybeans','wheat']
name_crop_mod = ['maize','soy','wheat']
name_model = ['cgms-wofost','lpj-guess','clm-crop','lpjml','epic-iiasa','gepic', 'orchidee-crop','pdssat','papsim','pegasus']
name_climate = ['wfdei.gpcc','agmerra']
yr_start = 1981
yr_end_model = {'agmerra': 2010, 'wfdei.gpcc': 2009}
flag_adj = 0
# # model data
if not flag_adj:
f_dir_mod = '/tera05/zhangsl/GGCMI_phase1_unzipped'
f_dir_output = f'../output/anomaly_mod/agmip_org/'
else:
f_dir_mod = '../output/AgMIP_adj'
f_dir_output = f'../output/anomaly_mod/agmip_adj/'
os.makedirs(f_dir_output, exist_ok=True)
# ##=== for isimip
# # # simulation parameters
# name_crop_mod = ['maize','soy','wheat']
# name_model = ['crover','epic-iiasa', 'ldndc', 'lpj-guess', 'lpjml', 'pdssat', 'pepic', 'promet', 'simplace-lintul5']
# name_climate = ['gfdl-esm4','ukesm1-0-ll','mri-esm2-0','mpi-esm1-2-hr','ipsl-cm6a-lr']
# yr_start = 2015
# yr_end = 2100
# ssp = 'ssp585'
# flag_adj = 1
# # # model data
# if not flag_adj:
# f_dir_mod = f'/tera07/zhangsl/lianghb21/ISMIP/ISMIP_3b/crop_{ssp}/'
# f_dir_output = f'../output/anomaly_mod/isimip_org/{ssp}'
# else:
# f_dir_mod = f'../output/ISIMIP_adj/{ssp}'
# f_dir_output = f'../output/anomaly_mod/isimip_adj/{ssp}'
# os.makedirs(f_dir_output, exist_ok=True)
#====================================================================
for ic in range(1):
crop_mod = name_crop_mod[ic]
for climate_name in name_climate[0:1]:
save_anomaly_mod(name_model,climate_name)