lat = file_ctl.variables["lat"]
lon = file_ctl.variables["lon"]
# read data and calculate mean/min/max
for iv in range(0, varnms_toa.size):
dtctl_toa=file_ctl_fssd.variables[varnms_toa[iv]][:,:,:] - \
file_ctl.variables[varnms_sub_toa[iv]][:,:,:]
dtexp_toa=file_exp_fssd.variables[varnms_toa[iv]][:,:,:] - \
file_exp.variables[varnms_sub_toa[iv]][:,:,:]
dtctl_sfc=file_ctl.variables[varnms_sfc[iv]][:,:,:] - \
file_ctl.variables[varnms_sub_sfc[iv]][:,:,:]
dtexp_sfc=file_exp.variables[varnms_sfc[iv]][:,:,:] - \
file_exp.variables[varnms_sub_sfc[iv]][:,:,:]
means_yby_ctl_toa[iy,
iv] = get_area_mean_min_max(dtctl_toa[0, :, :],
lat[:])[0]
means_yby_exp_toa[iy,
iv] = get_area_mean_min_max(dtexp_toa[0, :, :],
lat[:])[0]
means_yby_ctl_sfc[iy,
iv] = get_area_mean_min_max(dtctl_sfc[0, :, :],
lat[:])[0]
means_yby_exp_sfc[iy,
iv] = get_area_mean_min_max(dtexp_sfc[0, :, :],
lat[:])[0]
# compute multi-year mean at TOA -->
means_ctl_toa = np.mean(means_yby_ctl_toa, axis=0)
means_exp_toa = np.mean(means_yby_exp_toa, axis=0)
# stadard deviation at TOA -->
file_ctl=netcdf_dataset(fctl,"r")
file_exp=netcdf_dataset(fexp,"r")
# read lat and lon
lat=file_ctl.variables["lat"]
lon=file_ctl.variables["lon"]
means_yby_exp_fice[iy,:]=means_yby_exp_fice[iy,:]+np.mean(file_exp.variables["ICEFRAC"][0,:,:],axis=1)
# read data and calculate mean/min/max
vn="TMQ"
dtctl_wv=file_ctl.variables[vn][0,:,:]
dtexp_wv=file_exp.variables[vn][0,:,:]
means_yby_ctl_wv[iy,:]= means_yby_ctl_wv[iy,:] + np.mean(dtctl_wv[:,:],axis=1)
means_yby_exp_wv[iy,:]= means_yby_exp_wv[iy,:] + np.mean(dtexp_wv[:,:],axis=1)
gm_yby_ctl_wv[iy]=gm_yby_ctl_wv[iy]+get_area_mean_min_max(dtctl_wv[:,:],lat[:])[0]
gm_yby_exp_wv[iy]=gm_yby_exp_wv[iy]+get_area_mean_min_max(dtexp_wv[:,:],lat[:])[0]
vn="TGCLDCWP"
dtctl_lwp=file_ctl.variables[vn][0,:,:]*1000.
dtexp_lwp=file_exp.variables[vn][0,:,:]*1000.
means_yby_ctl_lwp[iy,:]= means_yby_ctl_lwp[iy,:] + np.mean(dtctl_lwp[:,:],axis=1)
means_yby_exp_lwp[iy,:]= means_yby_exp_lwp[iy,:] + np.mean(dtexp_lwp[:,:],axis=1)
gm_yby_ctl_lwp[iy]=gm_yby_ctl_lwp[iy]+get_area_mean_min_max(dtctl_lwp[:,:],lat[:])[0]
gm_yby_exp_lwp[iy]=gm_yby_exp_lwp[iy]+get_area_mean_min_max(dtexp_lwp[:,:],lat[:])[0]
vn="TS"
dtctl_ts=file_ctl.variables[vn][0,:,:]
dtexp_ts=file_exp.variables[vn][0,:,:]
means_yby_ctl_ts[iy,:]= means_yby_ctl_ts[iy,:] + np.mean(dtctl_ts[:,:],axis=1)
means_yby_exp_ts[iy,:]= means_yby_exp_ts[iy,:] + np.mean(dtexp_ts[:,:],axis=1)
figure_name = 'fig_Arctic_VIS_albedo_CESM2'
latbound1 = np.min(np.where(lat[:] > 55))
latbound2 = nlat
projection = ccrs.NorthPolarStereo(central_longitude=0)
elif pole is 'S':
figure_name = 'fig_Antarctic_VIS_albedo_CESM2'
latbound1 = 0
latbound2 = np.max(np.where(lat[:] < -55)) + 1
projection = ccrs.SouthPolarStereo(central_longitude=0)
#print(latbound2)
#exit()
#-- calculate area mean --
tmp = np.zeros((nlat, nlon))
tmp[:, :] = dtctl[0, :, :-1]
stats_ctl = get_area_mean_min_max(tmp[latbound1:latbound2, :],
lat[latbound1:latbound2])
tmp[:, :] = dtexp[0, :, :-1]
stats_exp = get_area_mean_min_max(tmp[latbound1:latbound2, :],
lat[latbound1:latbound2])
tmp[:, :] = dtdif[0, :, :-1]
stats_dif = get_area_mean_min_max(tmp[latbound1:latbound2, :],
lat[latbound1:latbound2])
print(stats_dif)
#parameters=get_parameters(varnm,season)
#projection = ccrs.PlateCarree(central_longitude=0)
#fig = plt.figure(figsize=[7.0,11.0],dpi=150.)
fig = plt.figure(figsize=(5, 5))
plotTitle = {'fontsize': 13.}
# read lat and lon
lat = file_ctl.variables["lat"]
lon = file_ctl.variables["lon"]
dtctl_fice = file_ctl.variables["ICEFRAC"][0, :, :]
means_yby_ctl_fice[:] = np.mean(dtctl_fice[:, :], axis=1)
# read data and calculate mean/min/max
for vn in varnms_vis_dn:
dtctl_dn = file_ctl.variables[vn][0, :, :]
dtexp_dn = file_exp.variables[vn][0, :, :]
means_yby_ctl_dn[0, :] = means_yby_ctl_dn[0, :] + np.mean(dtctl_dn[:, :],
axis=1)
means_yby_exp_dn[0, :] = means_yby_exp_dn[0, :] + np.mean(dtexp_dn[:, :],
axis=1)
gm_yby_ctl_dn[0] = gm_yby_ctl_dn[0] + get_area_mean_min_max(
dtctl_dn[:, :], lat[:])[0]
gm_yby_exp_dn[0] = gm_yby_exp_dn[0] + get_area_mean_min_max(
dtexp_dn[:, :], lat[:])[0]
for vn in varnms_nir_dn:
dtctl_dn = file_ctl.variables[vn][0, :, :]
dtexp_dn = file_exp.variables[vn][0, :, :]
means_yby_ctl_dn[1, :] = means_yby_ctl_dn[1, :] + np.mean(dtctl_dn[:, :],
axis=1) #[0,:]
means_yby_exp_dn[1, :] = means_yby_exp_dn[1, :] + np.mean(dtexp_dn[:, :],
axis=1) #[0,:]
gm_yby_ctl_dn[1] = gm_yby_ctl_dn[1] + get_area_mean_min_max(
dtctl_dn[:, :], lat[:])[0]
gm_yby_exp_dn[1] = gm_yby_exp_dn[1] + get_area_mean_min_max(
dtexp_dn[:, :], lat[:])[0]
file_ctl_diag=netcdf_dataset(fctl_diag,"r")
file_exp_diag=netcdf_dataset(fexp_diag,"r")
# read lat and lon
lat=file_ctl.variables["lat"]
lon=file_ctl.variables["lon"]
means_yby_exp_fice[iy,:]=means_yby_exp_fice[iy,:]+np.mean(file_exp.variables["ICEFRAC"][0,:,:],axis=1)
# read data and calculate mean/min/max
for vn in varnms_vis_dn:
dtctl_dn=file_ctl.variables[vn][0,:,:]
dtexp_dn=file_exp.variables[vn][0,:,:]
means_yby_ctl_dn[iy,0,:]= means_yby_ctl_dn[iy,0,:] + np.mean(dtctl_dn[:,:],axis=1)
means_yby_exp_dn[iy,0,:]= means_yby_exp_dn[iy,0,:] + np.mean(dtexp_dn[:,:],axis=1)
gm_yby_ctl_dn[0,iy]=gm_yby_ctl_dn[0,iy]+get_area_mean_min_max(dtctl_dn[:,:],lat[:])[0]
gm_yby_exp_dn[0,iy]=gm_yby_exp_dn[0,iy]+get_area_mean_min_max(dtexp_dn[:,:],lat[:])[0]
dtctl_dn_diag=file_ctl_diag.variables[vn][0,:,:]
dtexp_dn_diag=file_exp_diag.variables[vn][0,:,:]
means_yby_ctl_dn_diag[iy,0,:]= means_yby_ctl_dn_diag[iy,0,:] + \
np.mean(dtctl_dn_diag[:,:],axis=1)
means_yby_exp_dn_diag[iy,0,:]= means_yby_exp_dn_diag[iy,0,:] + \
np.mean(dtexp_dn_diag[:,:],axis=1)
gm_yby_ctl_dn_diag[0,iy]=gm_yby_ctl_dn_diag[0,iy] + \
get_area_mean_min_max(dtctl_dn_diag[:,:],lat[:])[0]
gm_yby_exp_dn_diag[0,iy]=gm_yby_exp_dn_diag[0,iy] + \
get_area_mean_min_max(dtexp_dn_diag[:,:],lat[:])[0]
for vn in varnms_nir_dn:
dtctl_dn=file_ctl.variables[vn][0,:,:]
else:
icef_snw = icef_snw_tmp + icef_snw
icef_bare = icef_bare_tmp + icef_bare
ocnf_open = ocnf_open_tmp + ocnf_open
icef_snw = icef_snw / 3.
icef_bare = icef_bare / 3.
ocnf_open = ocnf_open / 3.
#icef_snw = np.where(lndf_ctl < 0.01, icef_snw, np.nan)
#icef_bare = np.where(lndf_ctl < 0.01, icef_bare, np.nan)
#ocnf_open = np.where(lndf_ctl < 0.01, ocnf_open, np.nan)
# compute Arctic and Antarctic averages
# 1. Arctic
latbound1 = np.min(np.where(lat[:] > 55))
latbound2 = lat.size
means_NH[iss,0]=get_area_mean_min_max( \
ocnf_open[0,latbound1:latbound2,:],lat[latbound1:latbound2])[0]
means_NH[iss,1]=get_area_mean_min_max( \
icef_snw[0,latbound1:latbound2,:],lat[latbound1:latbound2])[0]
means_NH[iss,2]=get_area_mean_min_max( \
icef_bare[0,latbound1:latbound2,:],lat[latbound1:latbound2])[0]
means_NH[iss, :] = means_NH[iss, :] / np.sum(means_NH[iss, :])
# 2. Antarctic
latbound1 = 0
latbound2 = np.max(np.where(lat[:] < -55)) + 1
means_SH[iss,0]=get_area_mean_min_max( \
ocnf_open[0,latbound1:latbound2,:],lat[latbound1:latbound2])[0]
means_SH[iss,1]=get_area_mean_min_max( \
icef_snw[0,latbound1:latbound2,:],lat[latbound1:latbound2])[0]
means_SH[iss,2]=get_area_mean_min_max( \
icef_bare[0,latbound1:latbound2,:],lat[latbound1:latbound2])[0]
lat = file_ctl.variables["lat"]
lon = file_ctl.variables["lon"]
means_yby_exp_fice[iy, :] = means_yby_exp_fice[iy, :] + np.mean(
file_exp.variables["ICEFRAC"][0, :, :], axis=1)
# read data and calculate mean/min/max
vn = "CLDHGH"
dtctl_cldhgh = file_ctl.variables[vn][0, :, :] * 100.
dtexp_cldhgh = file_exp.variables[vn][0, :, :] * 100.
means_yby_ctl_cldhgh[iy, :] = means_yby_ctl_cldhgh[iy, :] + np.mean(
dtctl_cldhgh[:, :], axis=1)
means_yby_exp_cldhgh[iy, :] = means_yby_exp_cldhgh[iy, :] + np.mean(
dtexp_cldhgh[:, :], axis=1)
gm_yby_ctl_cldhgh[iy]=gm_yby_ctl_cldhgh[iy] + \
get_area_mean_min_max(dtctl_cldhgh[:,:],lat[:])[0]
gm_yby_exp_cldhgh[iy]=gm_yby_exp_cldhgh[iy] + \
get_area_mean_min_max(dtexp_cldhgh[:,:],lat[:])[0]
vn = "CLDMED"
dtctl_cldmed = file_ctl.variables[vn][0, :, :] * 100.
dtexp_cldmed = file_exp.variables[vn][0, :, :] * 100.
means_yby_ctl_cldmed[iy, :] = means_yby_ctl_cldmed[iy, :] + np.mean(
dtctl_cldmed[:, :], axis=1)
means_yby_exp_cldmed[iy, :] = means_yby_exp_cldmed[iy, :] + np.mean(
dtexp_cldmed[:, :], axis=1)
gm_yby_ctl_cldmed[iy]=gm_yby_ctl_cldmed[iy] + \
get_area_mean_min_max(dtctl_cldmed[:,:],lat[:])[0]
gm_yby_exp_cldmed[iy]=gm_yby_exp_cldmed[iy] + \
get_area_mean_min_max(dtexp_cldmed[:,:],lat[:])[0]
# data path
ctl_name = "CTL" #"CTL" #os.environ["ctl_name"]
ctl_pref = "solar_" + ctl_name + "_cesm211_ETEST-f19_g17-ens_mean_2010-2019"
fpath_ctl = "/raid00/xianwen/cesm211_solar/" + ctl_pref + "/climo/"
f1 = fpath_ctl + ctl_pref + "_climo_ANN.nc"
print(f1)
varlst=["SOLIN","FSNTOA","FSNTOAC","FLUT","FLUTC", \
"FSDS","FSDSC","FSNS","FSNSC","FLDS","FLNS","FLNSC","LHFLX","SHFLX"]
# open data file
file_ctl = netcdf_dataset(f1, "r")
# read lat and lon
lat = file_ctl.variables["lat"]
lon = file_ctl.variables["lon"]
lat_N = np.min(np.where(lat[:] > 60.))
lat_S = np.max(np.where(lat[:] < -60.)) + 1
pole = "N"
for var in varlst:
dtctl = file_ctl.variables[var][0, :, :] #[time,lat,lon]
if pole == "S":
stat = get_area_mean_min_max(dtctl[0:lat_S, :], lat[0:lat_S])
elif pole == "N":
stat = get_area_mean_min_max(dtctl[lat_N:, :], lat[lat_N:])
print(var, round(stat[0], 1))
