if threed:
fnamec = basepath + casename + subdir + casename + '_' + field + str(level) + '_' + timstr + '_ts.nc'
fnamep = basepath + casenamep + subdir + casenamep + '_' + field + str(level) + '_' + timstrp + '_ts.nc'
else:
fnamec = basepath + casename + subdir + casename + '_' + field + '_' + timstr + '_ts.nc'
fnamep = basepath + casenamep + subdir + casenamep + '_' + field + '_' + timstrp + '_ts.nc'
fldc = np.squeeze(cnc.getNCvar(fnamec,ncfield,timesel=timesel))*conv
fldp = np.squeeze(cnc.getNCvar(fnamep,ncfield,timesel=timesel))*conv
lat = cnc.getNCvar(fnamec,'lat')
lon = cnc.getNCvar(fnamec,'lon')
# annual time-series (3d)
anntsc = cutl.annualize_monthlyts(fldc)
anntsp = cutl.annualize_monthlyts(fldp)
#anntsp2 = cutl.annualize_monthlyts(fldp2)
#anntsp3 = cutl.annualize_monthlyts(fldp3)
nt,nlev,nlat = anntsc.shape # @@the var names are "wrong" but work fine in the script as written
#if casename != 'kemctl1' and casename != 'kemhadctl':
if casename not in ('kemctl1','kemhadctl','kemctl1ens','kemctl1r4'):
cmin=cminp; cmax=cmaxp
cminm=cminmp; cmaxm=cmaxmp
cminpct=cminpctp; cmaxpct=cmaxpctp
cminpctm=cminpctmp; cmaxpctm=cmaxpctmp
timeavg = 'ANN'
plt.plot(hadsstp60N-hadsstc60N,'k');
#plt.plot(hurrsstp60N-hurrsstc60N,'b');#SCREWY
plt.plot(cansstp60N-cansstc60N,'g');
plt.legend(('HadISST DIFF','CanESM2 DIFF'),'upper left')
plt.title('Average difference in SST North of ' + str(latlim) + 'N')
if printtofile:
fig.savefig('SST' + str(latlim) + 'NDIFF_seascyc_OBS.pdf')
if testsic:
lat = cnc.getNCvar(fhadsicorig,'lat')
lon = cnc.getNCvar(fhadsicorig,'lon')
sic = cnc.getNCvar(fhadsicorig,'SIC')
sicn = cnc.getNCvar(fhadsicnorig,'SICN')
sicANN = cutl.annualize_monthlyts(sic)
sicANN=sicANN[:,:,0:-1]
sicANNnh = sicANN[:,lat>0,:]
sicnANN = cutl.annualize_monthlyts(sicn)
sicnANN=sicnANN[:,:,0:-1]
sicnANNnh = sicnANN[:,lat>0,:]
areas = cutl.calc_cellareas(lat,lon)
areas = np.tile(areas,(sicANN.shape[0],1,1))
areasnh = areas[:,lat>0,0:-1]
totalareanh=np.sum(np.sum(areasnh,2),1)
siaANNnh = sicnANNnh*areasnh
totalsianh = np.sum(np.sum(siaANNnh,2),1)
totalsianh = np.tile(totalsianh,(sicANNnh.shape[1],sicANNnh.shape[2],1))
ncfilep1 = Dataset(fnamep1, "r") # pert1 ncfilep2 = Dataset(fnamep2, "r") # pert2 ncfilep3 = Dataset(fnamep3, "r") # pert3 lat = ncfilec.variables["lat"][:] lon = ncfilec.variables["lon"][:] fldc = ncfilec.variables[field.upper()][(styr - 1) * 12 : (enyr * 12 + 1), :, :] * conv # time start year to end fldp1 = ncfilep1.variables[field.upper()][(styrp - 1) * 12 : (enyrp * 12 + 1), :, :] * conv # time start year to end fldp2 = ncfilep2.variables[field.upper()][(styrp - 1) * 12 : (enyrp * 12 + 1), :, :] * conv # time start year to end fldp3 = ncfilep3.variables[field.upper()][(styrp - 1) * 12 : (enyrp * 12 + 1), :, :] * conv # time start year to end # # # ##################### Do calculations ################# # annual time-series (3d) anntsc = cutl.annualize_monthlyts(fldc) anntsp1 = cutl.annualize_monthlyts(fldp1) anntsp2 = cutl.annualize_monthlyts(fldp2) anntsp3 = cutl.annualize_monthlyts(fldp3) # annual global mean time-series anngmc = cutl.global_mean_areawgted3d(anntsc, lat, lon) anngmp1 = cutl.global_mean_areawgted3d(anntsp1, lat, lon) anngmp2 = cutl.global_mean_areawgted3d(anntsp2, lat, lon) anngmp3 = cutl.global_mean_areawgted3d(anntsp3, lat, lon) # annual polar (>=60N) mean time-series annpmc = cutl.polar_mean_areawgted3d(anntsc, lat, lon) annpmp1 = cutl.polar_mean_areawgted3d(anntsp1, lat, lon) annpmp2 = cutl.polar_mean_areawgted3d(anntsp2, lat, lon) annpmp3 = cutl.polar_mean_areawgted3d(anntsp3, lat, lon)
ncfilec = Dataset(fnamec,'r') # control #fldc = ncfilec.variables[ncfield][(styr-1)*12:(enyr*12+1),:,:,:]*conv fldc = ncfilec.variables[ncfield][...]*conv ncfilec.close() # super-most vanilla fldctmvan = np.average(fldc,0) fldctmzmvan = np.average(fldctmvan,2) # vanilla but remove extra lon fldctmvanNOLON = fldctmvan[:,:,0:-1] fldctmzmvanNOLON = np.average(fldctmvanNOLON,2) # vanilla but time-weight fldczmvan = np.average(fldc,3) fldczmvanann = cutl.annualize_monthlyts(fldczmvan) fldczmvananntm = np.average(fldczmvanann,0) # vanilla but remove extra lon and time-weight fldcNOLON = fldc[:,:,:,0:-1] fldczmvanNOLON = np.average(fldcNOLON,3) fldczmvanannNOLON = cutl.annualize_monthlyts(fldczmvanNOLON) fldczmvananntmNOLON = np.average(fldczmvanannNOLON,0) ################################# PY/NC ######################################## lat = cnc.getNCvar(fnamec,'lat') lev = cnc.getNCvar(fnamec,'plev') #fldczm = cnc.getNCvar(fnamec,ncfield,calc='zm',timechunk=((styr-1)*12,enyr*12+1) )*conv fldczm = cnc.getNCvar(fnamec,ncfield,calc='zm')*conv #fldpzm = cnc.getNCvar(fnamep2,ncfield,calc='zm',timechunk=((styr-1)*12,enyr*12+1) )*conv
years = np.arange(1850,2013)
hyears = np.arange(1979,2013)
nyears = np.arange(1979,2012)
darkolivegreen1 = np.array([202, 255, 112])/255 # terrible
darkolivegreen3 = np.array([162, 205, 90])/255.
darkseagreen = np.array([143, 188, 143])/255.
darkseagreen4 = np.array([105, 139, 105])/255.
dodgerblue = np.array([30, 144, 255])/255.
orangered4 = np.array([139, 37, 0])/255.
# ANNUAL
plt.figure()
plt.plot(years,cutl.annualize_monthlyts(cfldpall[0,:]),'0.25')
plt.plot(years,cutl.annualize_monthlyts(cfldpall[1,:]),'0.4')
plt.plot(years,cutl.annualize_monthlyts(cfldpall[2,:]),'0.55')
plt.plot(years,cutl.annualize_monthlyts(cfldpall[3,:]),'0.7')
plt.plot(years,cutl.annualize_monthlyts(cfldpall[4,:]),'0.85')
plt.plot(years,cutl.annualize_monthlyts( np.mean(cfldpall,axis=0) ),color='k',linewidth=2)
if afield=='sicn':
plt.plot(hyears,cutl.annualize_monthlyts(hfldp),color='green',linewidth=2)
plt.plot(nyears,cutl.annualize_monthlyts(nfldp),color=dodgerblue,linewidth=2)
plt.xlim(xlims)
plt.title('ANN NH SIA')
plt.grid()
if printtofile:
plt.savefig('CanESMens_OBS_' + cfield + '_ANN_timeseries' + pstr + '.pdf')
for skey in sims[2:10]: # Group I
fld = flddt[skey]
# timeseries
flddiffdt[skey] = fldc2x - fld
# climos
fldclimdt[skey], std = cutl.climatologize(fld)
flddiffclimdt[skey] = fldc2xclim - fldclimdt[skey]
rmse = np.sqrt(np.square(flddiffdt[skey]))
rmseclim = np.sqrt(np.square(flddiffclimdt[skey]))
rmsedt[skey] = cutl.calc_regmean(rmse, lat, lon, region)
rmseclimdt[skey] = cutl.calc_regmean(rmseclim, lat, lon, region)
annrmseclimdt[skey] = cutl.annualize_monthlyts(rmseclimdt[skey])
climrmsedt[skey], rmsestd = cutl.climatologize(rmsedt[skey])
for skey in sims[10:]: # Group II
fld = flddt[skey]
# timeseries
flddiffdt[skey] = fldc - fld
# climos
fldclimdt[skey], std = cutl.climatologize(fld)
flddiffclimdt[skey] = fldcclim - fldclimdt[skey]
rmse = np.sqrt(np.square(flddiffdt[skey]))
rmseclim = np.sqrt(np.square(flddiffclimdt[skey]))
rmsedt[skey] = cutl.calc_regmean(rmse, lat, lon, region)
## fldcNOLON = fldc[:,:,:,0:-1]
## fldczmvanNOLON = np.average(fldcNOLON,3)
## fldczmvanannNOLON = cutl.annualize_monthlyts(fldczmvanNOLON)
## fldczmvananntmNOLON = np.average(fldczmvanannNOLON,0)
################################# PY/NC/CDO ###################################
lat = cnc.getNCvar(fnamec, "lat")
lev = cnc.getNCvar(fnamec, "plev")
nlev = len(lev)
nlat = len(lat)
fldczmnew = np.append(
cnc.getNCvar(fnamec, ncfield, calc="zm") * conv, cnc.getNCvar(fnamec2, ncfield, calc="zm") * conv, axis=0
)
fldczmnewtm = np.mean(cutl.annualize_monthlyts(fldczmnew), 0)
fldczmnewtm2 = np.mean(fldczmnew, 0) # not time-weighted
fldczmnewANN = np.append(
cnc.getNCvar(fnamec, ncfield, calc="zm", seas="ANN") * conv,
cnc.getNCvar(fnamec2, ncfield, calc="zm", seas="ANN") * conv,
axis=0,
)
fldczmnewANNtm = np.mean(fldczmnewANN, 0)
fldczmnewdjf = np.append(
cnc.getNCvar(fnamec, ncfield, calc="zm", seas="DJF") * conv,
cnc.getNCvar(fnamec2, ncfield, calc="zm", seas="DJF") * conv,
axis=0,
)
fldczmnewdjftm = np.mean(fldczmnewdjf, 0)