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plot_boundaryconditions.py
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plot_boundaryconditions.py
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import cccmautils as cutl
import constants as con
import loadmodeldata as lmd
import loadLE as le
import matplotlib.lines as mlines
plt.close('all')
masksicn=True # use present day sicn to mask SST
printtofile=False
addle=True
deni = 913. # kg/m3 density of ice
timesel='0011-01-01,0011-12-31' # don't have to load much b/c constant
#djf=[0,1,11]
sea='DJF'
fields=('sicn','gt','sic')
def load_BCs(fields, season=None, timesel='0011-01-01,0011-12-31'):
simcbase='kemctl1'
simpbase='kem1pert2'
obsbase = 'kemnsidc'
flddat={}; fldcdat={}; fldpdat={}; sicnpdat={}
for fii,field in enumerate(fields):
fnamesc={}; fnamesp={}
flddiff={}; fldcavg={}; fldpavg={}
print field
# Loop through sim data:
# load all 5 sim BCs
for ii in range(0,6):
# do obs data at ii=5
if ii==5:
simname=obsbase+'ctl'
fnamec = con.getBCfilenames(field,sim=simname)
print fnamec
simname=obsbase+'pert'
fnamep = con.getBCfilenames(field,sim=simname)
else:
simname=simcbase+'r'+str(ii+1)
fnamec = con.getBCfilenames(field,sim=simname)
print fnamec
simname=simpbase+'r'+str(ii+1)
fnamep = con.getBCfilenames(field,sim=simname)
fldc = cnc.getNCvar(fnamec,field.upper(),timesel=timesel)
fldp = cnc.getNCvar(fnamep,field.upper(),timesel=timesel)
if field=='sicn':
# save pert data to mask gt
if season==None:
sicnpdat[ii] = fldp
else:
sicnpdat[ii] = cutl.seasonalize_monthlyts(fldp,season=sea,climo=1)
if season==None:
flddiff[ii] = fldp-fldc
fldcavg[ii] = fldc
fldpavg[ii] = fldp
else:
flddiff[ii] = cutl.seasonalize_monthlyts(fldp-fldc,season=sea,climo=1)
fldcavg[ii] = cutl.seasonalize_monthlyts(fldc,season=sea,climo=1)
fldpavg[ii] = cutl.seasonalize_monthlyts(fldp,season=sea,climo=1)
fnamesc[ii]=fnamec
fnamesp[ii]=fnamep
flddat[field] = flddiff
fldcdat[field] = fldcavg
fldpdat[field] = fldpavg
return flddat, fldcdat, fldpdat, sicnpdat
def load_LE(fdict,casename='historical',conv=1,season=None,timesel1='1979-01-01,1989-12-31',timesel2='2002-01-01,2012-12-31'):
ledatc = le.load_LEdata(fdict,casename,timesel=timesel1,
rettype='ndarray',conv=conv,ftype='fullts',local=True,verb=False)
ledatp = le.load_LEdata(fdict,casename,timesel=timesel2,
rettype='ndarray',conv=conv,ftype='fullts',local=True,verb=False)
lediff = ledatp-ledatc
print lediff.shape
leclimo,_ = cutl.climatologize(lediff.T)
print leclimo.shape
return leclimo
# === info for plot:
gt={}; sicn={}; sic={}
gt['cmin'] = -2.; gt['cmax'] = 2.; gt['cmap']='blue2red_w20'
#sicn['cmin'] = -30; sicn['cmax'] = 30; sicn['cmap']='red2blue_w24sic'
sicn['cmin'] = -25; sicn['cmax'] = 25; sicn['cmap']='red2blue_w20sic'
sic['cmin'] = -0.5; sic['cmax'] = 0.5; sic['cmap']='red2blue_w20'
meta = {'gt': gt, 'sicn': sicn, 'sic': sic}
ylabs = {0: 'OBS', 1: 'E1', 2: 'E2', 3: 'E3', 4: 'E4', 5:'E5'}
convdt = {'gt': 1, 'sicn': 100, 'sic': 1/np.float(deni)}
# === load one year for seasonal cycle:
#flddat,fldcdat,fldpdat,sicnpdat = load_BCs(fields, season=None, timesel=timesel)
# ============== PLOT ==============
printtofile=False
lat = con.get_t63lat() #cnc.getNCvar(fnamec,'lat')
lon = con.get_t63lon() #cnc.getNCvar(fnamep,'lon')
fsz=16
fliptohoriz=True
latlim=57
pparams = {'lcol':'0.9', 'coastres': 'c', 'coastwidth': 0.5,
'area_thresh':70000,'lmask':True}
fields=('sicn','sic','gt')
fields=('sicn',)
tlabs = {'gt': '$\Delta$ SST ($^\circ$C)', 'sicn': '$\Delta$ SIC (%)', 'sic': '$\Delta$ SIT (m)'}
if fliptohoriz:
psuff='_horizor'+str(len(fields))+'b' # horizontal orientation
# rows are fields and cols are sims
if len(fields)==1:
fig,axs = plt.subplots(2,3)
fig.set_size_inches((6,len(fields)*2+2))
fig.subplots_adjust(wspace=0.01,hspace=0.3)
else:
fig,axs = plt.subplots(len(fields),6)
fig.set_size_inches((10,len(fields)*2))
fig.subplots_adjust(wspace=0.05,hspace=0.1)
else:
psuff='_vertor'+str(len(fields))+'b' # vertical orientation
# cols are fields and rows are sims
if len(fields)==1:
fig,axs = plt.subplots(3,2)
fig.set_size_inches((len(fields)*2+1,6))
fig.subplots_adjust(wspace=0.05,hspace=0.05)
else:
fig,axs = plt.subplots(6,len(fields))
fig.set_size_inches((len(fields)*2+1,14))
fig.subplots_adjust(wspace=0.1,hspace=0.05)
for fii,field in enumerate(fields):
metap = meta[field]
print field, metap
conv = convdt[field]
flddiff=flddat[field]
for ii in range(0,6): # 6 sims
if len(fields)==1:
ax=axs.flatten()[ii]
else:
if fliptohoriz:
ax=axs[fii,ii]
else:
ax=axs[ii,fii]
plotfld=flddiff[ii]*conv
plotfld = cutl.seasonalize_monthlyts(plotfld,season=sea, climo=1)
if field=='gt' and masksicn:
tmp = sicnpdat[ii]
tmp = cutl.seasonalize_monthlyts(tmp,season=sea, climo=1)
plotfld[tmp>=0.15] = 0 # mask where model sees grid cell as sea ice (not SST)
bm,pc = cplt.kemmap(plotfld, lat, lon, ptype='nheur',
latlim=latlim, axis=ax, suppcb=True,
round=False,lcol=pparams['lcol'], coastres= 'c',
coastwidth= 0.5, area_thresh=70000,
lmask=True,**metap)
if len(fields)==1:
ax.set_title(ylabs[ii],fontsize=fsz)
else:
if fliptohoriz:
if ax.is_first_col(): ax.set_ylabel(tlabs[field])
if ax.is_first_row(): ax.set_title(ylabs[ii])
else:
if ax.is_first_row(): ax.set_title(tlabs[field])
if ax.is_first_col(): ax.set_ylabel(ylabs[ii])
axposx = ax.get_position().get_points()[0][0] # x position of last ax
axposy = ax.get_position().get_points()[0][1] # y position of last ax
axwi = ax.get_position().width # width of last ax
axht = ax.get_position().height # height of last ax
if fliptohoriz:
if len(fields)==1:
pass
cpos=[.91,.15, .02,.7]
else:
cpos=[0.91, axposy,.02,axht]
cbar_ax,cbar = cplt.add_colorbar(fig,pc,orientation='vertical',pos=cpos,label='(%)')
else:
if len(fields)==1:
cpos=[0.12,0.07,0.8,.02]
else:
cpos=[axposx,.07, axwi,.02]
cbar_ax,cbar = cplt.add_colorbar(fig,pc,orientation='horizontal',pos=cpos)
if field=='sicn':
cbar.set_ticks([metap['cmin'],-20,-15,-10,-5,0,5,10,15,20,metap['cmax']])
cbar.set_ticklabels(('',-20,'',-10,'',0,'',10,'',20,''))
else:
cbar.set_ticks([metap['cmin'],0,metap['cmax']])
cbar.set_ticklabels((metap['cmin'],0,metap['cmax']))
if printtofile:
fig.savefig('suppfig_boundaryconditions' + psuff+'.pdf',bbox_inches='tight')
fig.savefig('suppfig_boundaryconditions' + psuff+'.eps',bbox_inches='tight',format='eps',dpi=600)
#tst=flddat['sicn'][1]
#cplt.map_allmonths(tst,lat,lon,ptype='nheur',
# cmin=-.15,cmax=.15,cmap='red2blue_w20')
printtofile=False
# ======== plot seasonal cycle
sie=False # else it's sea ice area
if addle:
if sie:
fdict = {'field': 'sienh', 'ncfield': 'sienh', 'comp': 'OImon'}
else:
fdict = {'field': 'sianh', 'ncfield': 'sianh', 'comp': 'OImon'}
print '@@@@@@@@@@@@@@'
leclimo = load_LE(fdict)
simslg=mlines.Line2D([],[],color='0.5',linewidth=2)
osimlg=mlines.Line2D([],[],color='r',linewidth=2)
fields=('sicn','sic')
xx=range(0,12)
xxticks=np.arange(0,12)
xxlabs=('Jan','','Mar','','May','','Jul','','Sep','','Nov','')
tlabs = {'gt': 'SST ($^\circ$C)',
'sicn': 'SIE (millions of km$^2$)',
'sic': 'SIV (thousands of km$^3$)'}
fig,axs = plt.subplots(2,1)
fig.set_size_inches((5.2,5))
fig.subplots_adjust(wspace=0.1,hspace=0.1)
# climos!
fig2,axs2 = plt.subplots(2,1)
fig2.set_size_inches((5.2,5))
fig2.subplots_adjust(wspace=0.1,hspace=0.1)
savedjf={}
obsdat={}
for fii,field in enumerate(fields):
metap = meta[field]
print field, metap
conv = convdt[field]
flddiff=flddat[field]
fldcavg=fldcdat[field]
fldpavg=fldpdat[field]
ax=axs[fii]
ax2=axs2[fii]
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
if ii==5:
clr='r'
print 'obs field,dat ' + field,str(plotfld)
# save obs:
obsdat[field]=plotfld
else:
clr='0.5'
ax.plot(xx,plotfld, color=clr, linewidth=2)
if addle and field=='sicn':
ax.plot(xx,leclimo,color='0.7',alpha=0.5)
if field=='sicn':
print ii, plotfld[[0,1,11]] # @@@@
print ' MEAN: ' + str(plotfld[[0,1,11]].mean())
savedjf[ii] = plotfld[[0,1,11]].mean()
ax2.plot(xx,plotfldc,color=clr,linewidth=2)
ax2.plot(xx,plotfldp,color=clr,linewidth=2,linestyle='--')
yticks=ax.get_yticks()
ax.set_yticklabels(yticks/1e12)
ax.set_xticks(xxticks)
if ax.is_last_row():
ax.set_xticklabels(xxlabs)
else:
ax.legend((simslg,osimlg),('Model BCs','Observational BC'),loc='lower left',
frameon=False)
ax.set_xticklabels('')
ax.set_xlim(0,11)
ax.set_ylabel('$\Delta$ ' + tlabs[field])
yticks=ax2.get_yticks()
ax2.set_yticklabels(yticks/1e12)
ax2.set_xticks(xxticks)
if ax2.is_last_row():
ax2.set_xticklabels(xxlabs)
else:
ax2.set_xticklabels('')
ax2.set_xlim(0,11)
ax2.set_ylabel(tlabs[field])
if printtofile:
fig.savefig(pstr+'_SITnh_diff_seacycle_BCs.pdf',bbox_inches='tight')
fig.savefig(pstr+'_SITnh_diff_seacycle_BCs.eps',bbox_inches='tight',format='eps',dpi=600)
fig2.savefig(pstr+'_SITnh_climo_seacycle_BCs.pdf',bbox_inches='tight')
fig2.savefig(pstr+'_SITnh_climo_seacycle_BCs.eps',bbox_inches='tight',format='eps',dpi=600)
#tst=flddat['sicn'][1]
#cplt.map_allmonths(tst,lat,lon,ptype='nheur',
# cmin=-.15,cmax=.15,cmap='red2blue_w20')
if addle:
# not a boundary condition figure: shows how LE compares to obs
cisiglevel=0.05
fig,ax = plt.subplots(1,1)
fig.set_size_inches((5.2,3))
#fig.subplots_adjust(wspace=0.1,hspace=0.1)
lemean = leclimo.mean(axis=1)
ci = sp.stats.t.interval(1-cisiglevel, leclimo.shape[1]-1,
loc=lemean, scale=leclimo.std(axis=1))
print len(ci), ci[0].shape
ax.plot(xx,leclimo,color='0.7',alpha=0.5)
ax.plot(xx,obsdat['sicn'],color='r',linewidth=2)
yticks=ax.get_yticks()
ax.set_yticklabels(yticks/1e12)
ax.set_xticks(xxticks)
ax.set_xticklabels(xxlabs)
#ax.legend((simslg,osimlg),('Model BCs','Observational BC'),loc='lower left',
# frameon=False)
ax.set_xlim(0,11)
ax.set_ylabel('Change in ' + tlabs['sicn'])
# or fill between
maxle = leclimo.max(axis=1)
minle = leclimo.min(axis=1)
printtofile=True
fig,ax = plt.subplots(1,1)
fig.set_size_inches((6,5))
#fig.subplots_adjust(wspace=0.1,hspace=0.1)
#ax.plot(xx,leclimo,color='0.7',alpha=0.5)
ax.plot(xx,lemean,color='0.6')
ax.plot(xx,obsdat['sicn'],color='r',linewidth=2)
#ax.fill_between(xx,maxle,minle,color='0.8',alpha=0.5)
ax.fill_between(xx,ci[0], ci[1],color='0.8',alpha=0.5)
yticks=ax.get_yticks()
ax.set_yticklabels(yticks/1e12)
ax.set_xticks(xxticks)
ax.set_xticklabels(xxlabs)
#ax.legend((simslg,osimlg),('Model BCs','Observational BC'),loc='lower left',
# frameon=False)
ax.set_xlim(0,11)
ax.set_ylabel('Change in sea ice area (million square km)', fontsize=16)
ax.set_xlabel('Month',fontsize=16)
if printtofile:
fig.savefig('SuppFig_seacycle_LE2.pdf')
printtofile=False
# @@@@@@@@@@@@@ also, plot regions for paper: supp fig (save code)
cplt.plot_regions(('bksmori','eurasiamori'),colors=('k','blue'),ptype='nheur');
if printtofile:
plt.savefig('suppfig_defineregions.pdf',bbox_inches='tight')
plt.savefig('suppfig_defineregions.eps',bbox_inches='tight',format='eps',dpi=600)