if sicn==0:
print 'No SST or SIC dataset for NSIDC!'
exit()
plotflds=nsidcsicd
dset = 'NSIDCbootstrap'
title = ' (' + timeperp2 + ')-(' + timeper + ')'
savstr=timeperp2 + 'min' + timeper
cmlen = float(15)
if diffobs: # difference CanESM and HadISST
plotfld = cansicd - hadsicd
cplt.map_allmonths(plotfld,lat,lon,cmin=cmind,cmax=cmaxd,title='CanESM-HadISST ' + field,type='nh',cmap=cmap)
if printtofile==1:
plt.savefig('CanESM-HadISST_' + field + 'diffs_abt' + savstr + '_allmos_nh.pdf')
cplt.map_allmonths(plotfld,lat,lon,cmin=cmind,cmax=cmaxd,title='CanESM-HadISST ' + field,cmap=cmap)
if printtofile==1:
plt.savefig('CanESM-HadISST_' + field + 'diffs_abt' + savstr + '_allmos_sq.pdf')
if plotseacycmag: # only for CanESM2 SST
maxfld = np.max(plotflds,axis=0)
maxidx = np.argmax(plotflds,axis=0)
minfld = np.min(plotflds,axis=0)
minfld2 = np.min(ma.masked_where(gctsoneyr==1,plotflds),axis=0)
minidx = np.argmin(plotflds,axis=0)
## midx=midx+1
## fig1 = cplt.map_allmonths(fldtest,lat,lon,title='CDO selmon',cmin=clims[0],cmax=clims[1],type='nh',climo=1)
if field == "sic" or field == "sit":
plotfldp = ma.masked_where(fldp <= 0, fldp)
plotfldc = ma.masked_where(fldc <= 0, fldc)
else:
plotfldc = fldc
plotfldp = fldp
pparams = dict(type="nh", cmin=clims[0], cmax=clims[1], cmap=cmapclimo)
if field == "ts":
pparams["conts"] = 271.2
figp = cplt.map_allmonths(plotfldp, bclat, bclon, title="pert " + field, **pparams)
figc = cplt.map_allmonths(plotfldc, bclat, bclon, title="ctrl " + field, **pparams)
figd = cplt.map_allmonths(
fldp - fldc, bclat, bclon, title="diff", type="nh", cmin=climsdiff[0], cmax=climsdiff[1], cmap=cmap
)
if field == "ts":
figd2 = cplt.map_allmonths(
fldp - fldsave,
bclat,
bclon,
title="pert - originalpert",
type="nh",
cmin=climsdiff[0],
cmax=climsdiff[1],
cmap=cmap,
)
landmask = np.tile(landmask,(12,1,1))
#pert1n[pert1n<271.2] = -1000 # @@ just for testing
pert1n = ma.masked_where(landmask==-1,pert1n)
pert1n = ma.masked_where(pert1sic>.15,pert1n)
#pert1[pert1<271.2] = -1000 # @@ just for testing
pert1 = ma.masked_where(landmask==-1,pert1)
pert1 = ma.masked_where(pert1sic>.15,pert1)
#cplt.map_allmonths(pert1n,lat,lon,cmin=271.2-35,cmax=271.2+35,cmap='blue2red_20',type='nh',conts=[271.2],climo=1,lmask=1)
#cplt.map_allmonths(pert1,lat,lon,cmin=271.2-35,cmax=271.2+35,cmap='blue2red_20',type='nh',conts=[271.2],climo=1,lmask=1)
cplt.map_allmonths(pert1n-pert1,lat,lon,cmin=-.1,cmax=.1,cmap='blue2red_20',type='nh',climo=1)
#cplt.map_allmonths(landmask,lat,lon,cmap='blue2red_20',type='nh',climo=1)
if checknewbcs:
deni = 913 # density of ice
if timeperp=='2022-2032':
cming = -5; cmaxg = 5 # GT
cminc = -.4; cmaxc = .4 # SI concentration
cmint = -.7*deni; cmaxt = .7*deni # SI thickness
else:
cming = -6; cmaxg = 6
cminc = -.6; cmaxc = .6 # SI concentration
cmint = -.8*deni; cmaxt = .8*deni # SI thickness
sstfnamep = basepath2 + dset + '/hadisst1.1_bc_128_64_1870_2013m03_gt_' + timeper + 'climo.nc'
sstfnamec = basepath2 + dset + '/hadisst1.1_bc_128_64_1870_2013m03_gt_' + timeperc + 'climo.nc'
sitfnamep = basepath2 + dset + '/hadisst1.1_bc_128_64_1870_2013m03_sic_' + timeper + 'climo.nc'
sitfnamec = basepath2 + dset + '/hadisst1.1_bc_128_64_1870_2013m03_sic_' + timeperc + 'climo.nc'
hsicfnamep = basepath2 + dset + '/hadisst1.1_bc_128_64_1870_2013m03_sicn_' + timeper + 'climo.nc'
hsicfnamec = basepath2 + dset + '/hadisst1.1_bc_128_64_1870_2013m03_sicn_' + timeperc + 'climo.nc'
hsicp = cnc.getNCvar(hsicfnamep,'SICN')
hsicc = cnc.getNCvar(hsicfnamec,'SICN')
cmap='red2blue_w20'
if testsicplots:
# compare sic
# looks like NSIDC has more ice in the control time period and ~less in the pert. so, greater trend
cplt.map_allmonths(nsicp-hsicp,lat,lon,cmin=-.1,cmax=.1,cmap=cmap,type='nh',lmask=1,title='pert sic nsidc-hadisst',latlim=45)
cplt.map_allmonths(nsicc-hsicc,lat,lon,cmin=-.1,cmax=.1,cmap=cmap,type='nh',lmask=1,title='ctl sic nsidc-hadisst',latlim=45)
cplt.map_allmonths((nsicp-nsicc),lat,lon,cmin=-.1,cmax=.1,cmap=cmap,type='nh',lmask=1,title='pert-ctl sic nsidc',latlim=45)
cplt.map_allmonths((hsicp-hsicc),lat,lon,cmin=-.1,cmax=.1,cmap=cmap,type='nh',lmask=1,title='pert-ctl sic hadisst',latlim=45)
cplt.map_allmonths((nsicp-nsicc) - (hsicp-hsicc),lat,lon,cmin=-.1,cmax=.1,cmap=cmap,type='nh',lmask=1,title='pert-ctl sic nsidc-hadisst',latlim=45)
if dossts:
# ########### do SST ####################################
hsstp = cnc.getNCvar(sstfnamep,'GT')
hsstc = cnc.getNCvar(sstfnamec,'GT')
lat = cnc.getNCvar(sstfnamec,'lat')
lon = cnc.getNCvar(sstfnamec,'lon')
timefld = cnc.getNCvar(sstfnamec,'time')
flddt[skey] = cnc.getNCvar(fname, ncfield, timesel=timeseldt[skey]) * conv
# <headingcell level=3>
# SICN: Below are sea ice concentration comparisons for various combinations
# (figure titles are below figures)
# <codecell>
if showmaps:
fldc = flddt["iga"]
# control (iga) climo
fig1 = cplt.map_allmonths(
fldc, lat, lon, type="nh", cmap="blue2blue_bw10", cmin=cminc, cmax=cmaxc, lmask=1, title="preI"
)
if printtofile:
fig1.savefig(field + "_iga_allmos_nh.pdf")
for skey in sims[2:10]: # just the Group I sims, climo and diff
fig2 = cplt.map_allmonths(
flddt[skey],
lat,
lon,
type="nh",
cmap="blue2blue_bw10",
cmin=cminc,
cmax=cmaxc,
lmask=1,
title="2xco2 nudge (" + skey + ")",
