def compute_targoffset(ncicedt,dosia=True,dospatial=False,verb=True):
""" dospatial supercedes dosia
"""
sies={}
for case in ncicedt.keys():
if verb:
print case
icetmp = np.mean(ncicedt[case],axis=0)#/100.
if dospatial: # this is for spatial scaling
verb=False
sies[case] = icetmp/100.
else:
if dosia:
print 'icetmp.shape ' + str(icetmp.shape) # @@@@@@@@@@@@@@@22
if last=='last200':
sies[case] = icetmp #@@ test
else:
sies[case],_ = cutl.calc_totseaicearea(icetmp/100.,lat,lon,model=None,isarea=False)
else:
if last=='last200':
print 'SIE for last200 not implemented! @@@'
else:
sies[case],_ = cutl.calc_seaiceextent(icetmp/100.,lat,lon,model=None)
fullchange = sies['2xco22xco2ice'] - sies['preipreiice']
pctoffsets={}; offsets={}
offsets['icecold'] = (sies['2xco22xco2ice'] - sies['prei2xco2iceb'])/1e12
pctoffsets['icecold'] = (sies['2xco22xco2ice'] - sies['prei2xco2iceb']) / fullchange * 100
# for co2lo: how much deviation is the sea ice from climo (here an increase
# --> except still means co2 isolation contaminated by sea ice loss)
offsets['co2lo'] = (sies['2xco22xco2ice']-sies['prei2xco2iceb'])/1e12
pctoffsets['co2lo'] = (sies['2xco22xco2ice']-sies['prei2xco2iceb'])/sies['2xco22xco2ice']*100
#pctoffsets['co2lo'] = pctoffsets['icecold']
offsets['icewarm'] = (sies['2xco2preiice'] - sies['preipreiice'])/1e12
pctoffsets['icewarm'] = (sies['2xco2preiice'] - sies['preipreiice']) / fullchange * 100
# for co2hi: how much deviation is the sea ice from climo (here a decrease)
offsets['co2hi'] = (sies['2xco2preiice'] - sies['preipreiice'])/1e12
pctoffsets['co2hi'] = (sies['2xco2preiice'] - sies['preipreiice'])/sies['preipreiice']*100
#pctoffsets['co2hi'] = pctoffsets['icewarm']
if verb:
print '\n CO2hi: CO2 isolation has a effect from ice melt (ice not grown enough): ' +\
str(pctoffsets['co2hi']) + '% of climo'
print ' CO2lo: CO2 isolation has a effect from ice melt (ice not melted enough): ' +\
str(pctoffsets['co2lo']) + '% of climo'
print ' ICEwarm: sea ice melt effect is underestimated (ice not grown enough): ' +\
str(pctoffsets['icewarm']) + '% of full change'
print ' ICEcold: sea ice melt effect is underestimated (ice not melted enough): ' +\
str(pctoffsets['icecold']) + '% of full change\n'
return offsets,pctoffsets
lon=cnc.getNCvar(fname,'lon')
fld = cnc.getNCvar(fname,field,timesel=timesel,seas=sea)
# select a given time period and keep maps
fldsel = cnc.getNCvar(fname,field,timesel=timesel2,seas=sea)
nt = fldsel.shape[0]
nlon=fldsel.shape[2]
nlat=fldsel.shape[1]
fldre = fldsel.reshape((nt,nlon*nlat))
xx=np.arange(0,nt)
#ensseldt[eii] = fldsel
if field=='sic':
ensnhdt[eii],ensshdt[eii] = cutl.calc_totseaicearea(fld/100.,lat,lon,isarea=False)
fldsel=cutl.calc_seaicearea(fldsel,lat,lon)
fldre=fldsel.reshape((nt,nlon*nlat))
else:
ensgmdt[eii] = cutl.global_mean_areawgted3d(fld,lat,lon)
ensrmdt[eii] = cutl.calc_regmean(fld,lat,lon,region)
#slope[eii], intercept, r_value, p_value, std_err = sp.stats.linregress(xx,dat) # not good for 3d data?
# this is just the second timesel (ie 2002-2012)
slope,intercept = np.polyfit(xx,fldre,1) # supposedly can do w/ higher dims?
enstrnddt[eii] = slope #.reshape((nlat,nlon)) # reshape later @@
# also save all trends into one dictionary (don't differentiate by seed/base run)
alltrnddt[superii] = slope
# RMSE averaged >60N for each relaxation run (preI nudged to 2xco2 ice) compared to gregory_2xco2
# <codecell>
# calc total sea ice area/volume, plot it
mons = np.arange(1, 13)
fldpltdt = {}
fldplttsdt = {}
fldpltclimdt = {}
if field == "sicn":
for skey in sims:
fldpltclimdt[skey], sh = cutl.calc_totseaicearea(fldclimdt[skey], lat, lon)
fldplttsdt[skey], sh = cutl.calc_totseaicearea(flddt[skey], lat, lon)
tstr = "SIA (millions of km^2)"
divby = np.float(1e12) # convert sea ice area to millions of km2
elif field == "sic":
# need sea ice area to calc volume
field = "sicn"
ncfield = field.upper()
conv = 1
for skey in sims:
fname = basepath + skey + subdir + skey + "_" + field + "_" + timeperdt[skey] + "_ts.nc"
flds = cnc.getNCvar(fname, ncfield, timesel=timeseldt[skey]) * conv # sea ice conc
fldplttsdt[skey], sh = cutl.calc_totseaicevol(flddt[skey], flds, lat, lon) # time series of ice vol
for ii in range(0,6):
simfld=flddiff[ii]
simcfld=fldcavg[ii]
simpfld=fldpavg[ii]
if field=='sicn':
if sie:
pstr='SIEnh'
plotfldc,_ = cutl.calc_seaiceextent(simcfld,lat,lon,model=None)
plotfldp,_ = cutl.calc_seaiceextent(simpfld,lat,lon,model=None)
else: # sia
pstr='SIAnh'
tlabs['sicn'] = 'SIA (millions of km$^2$)'
plotfldc,_ = cutl.calc_totseaicearea(simcfld,lat,lon,model=None)
plotfldp,_ = cutl.calc_totseaicearea(simpfld,lat,lon,model=None)
plotfld = plotfldp-plotfldc
#plotfld,_ = cutl.calc_totseaicearea(simfld,lat,lon,model=None)
elif field=='sic':
tmpc=fldcdat['sicn'][ii]
tmpp=fldpdat['sicn'][ii]
plotfldc,_ = cutl.calc_totseaicevol(simcfld*conv,tmpc,lat,lon)
plotfldp,_ = cutl.calc_totseaicevol(simpfld*conv,tmpp,lat,lon)
plotfld = plotfldp-plotfldc
#print conv
#print 'plotfldc,plotfldp' + str(plotfldc)+',' + str(plotfldp)
else:
print '@@ field ' + field + ' not recognized'
break
