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cmip5seaice.py
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""" From Neil:
import pandas as pd
df = pd.DataFrame.from_csv('/raid/ra40/data/ncs/for_edhawkins_sic/rcp45/cmip5_rcp45_sie.csv')
df_CanESM2 = df.filter(regex='CanESM2')
# plot September extend
df[df.index.month==9].resample('A').plot(color='k',alpha=0.5, legend=False)
df_CanESM2[df_CanESM2.index.month==9].resample('A').plot(color='g',
legend=False)
cmip5seaice.py: 10/7/2014
calc the range of sea ice extent trends for within model ensembles, etc.
"""
import pandas as pd
import constants as con
import collections as coll
import scipy as sp
import scipy.stats
import cccmacmaps as ccm
import cccmautils as cutl
import cmip5meta as cm5
plt.close('all')
plt.ion()
printtofile=False
df = pd.DataFrame.from_csv('/raid/ra40/data/ncs/for_edhawkins_sic/rcp45/cmip5_rcp45_sie.csv')
df_CanESM2 = df.filter(regex="CanESM2")
models = ('ACCESS1-0','ACCESS1-3','BNU-ESM','CCSM4','CESM1-BGC','CESM1-CAM5','CMCC-CMS',
'CMCC-CM','CNRM-CM5','CSIRO-Mk3-6-0','CanESM2','EC-EARTH','FGOALS-g2','GFDL-CM3',
'GFDL-ESM2M','GISS-E2-H','GISS-E2-R','HadGEM2-CC','HadGEM2-ES','IPSL-CM5A-LR',
'IPSL-CM5A-MR','IPSL-CM5B-LR','MIROC-ESM-CHEM','MIROC-ESM','MIROC5','MPI-ESM-LR',
'MPI-ESM-MR','MRI-CGCM3','NorESM1-ME')
styr='1979'
endyr='2012' # inclusive
mons = con.get_mon()
montrenddt = coll.OrderedDict() #dict.fromkeys(mons)
monmndt = coll.OrderedDict()
monmxdt = coll.OrderedDict()
monavgdt = coll.OrderedDict()
hmontrenddt = coll.OrderedDict()
nmontrenddt = coll.OrderedDict()
#mondf = pd.DataFrame() # index will be np.arange
totens=len(df.keys()) # total # of ens members
## # trend calc
## mm, bb = np.polyfit(xx, dat, 1)
## ax.plot(onex,mm*onex + bb, color='k')#,linestyle='--')
## better trend
## slope, intercept, r_value, p_value, std_err = stats.linregress(xi,y)
# ================= OBS ===========
basepath2 = '/home/rkm/work/BCs/'
#fhadsicc = basepath2 + 'HadISST/hadisst1.1_bc_128_64_1870_2013m03_sicn_' +\
# ctimstr + 'climo.nc' #SICN, 129x64 CLIMO
fhad = basepath2 + 'HadISST/hadisst1.1_bc_128_64_1870_2013m03_SIEnhfrac_1870010100-2013030100.nc'
#fnsidcsicc = basepath2 + 'NSIDC/nsidc_bt_128x64_1978m11_2011m12_sicn_' + ctimstr + 'climo.nc'
fnsidc = basepath2 + 'NSIDC/nsidc_bt_128x64_1978m11_2011m12_SIEnhfrac_1978111600-2011121612.nc' #SICN, 129x64
had = cnc.getNCvar(fhad,'SICN',timesel='1979-01-01,2012-12-31')
hadtime = cnc.getNCvar(fhad,'time',timesel='1979-01-01,2012-12-31')
nsidc = cnc.getNCvar(fnsidc,'SICN',timesel='1979-01-01,2012-12-31')
earthrad = con.get_earthrad()
totalarea = 4*np.pi*earthrad**2
print had.shape
print nsidc.shape
had=had*(totalarea/2.) # convert to SIE from hemispheric fraction
nsidc=nsidc*(totalarea/2.) # convert to SIE from hemispheric fraction
# =================================
#superii=0 # super index for all ensemble member trends
superslopes = np.zeros((len(mons),totens))
superkeys = ()#np.zeros((len(mons),totens))
for monii,mo in enumerate(mons):
superii=0 # super index for all ensemble member trends
thismonskey=()
modensdt = dict.fromkeys(models)
modtrenddt = dict.fromkeys(models)
mndt = dict.fromkeys(models)
mxdt = dict.fromkeys(models)
avgdt = dict.fromkeys(models)
enumdt={} # number of ensemble members
for mod in models:
onemod = df.filter(regex=mod)[styr:endyr] #DataFrame of DataFrames where e/ sub DF is one model's ensemble
modata = onemod[onemod.index.month==monii+1].resample('A') # resample annually for given month
modensdt[mod] = modata # shape of values is # months x # ens mems
ensnum = modata.values.shape[-1]
enumdt[mod] = ensnum
xx = np.arange(0,modata.values.shape[0])
slope= np.zeros((ensnum,))
for eii in np.arange(0,ensnum):
# calculate trend
dat = np.squeeze(modata.values[...,eii])
#print dat.shape
slope[eii], intercept, r_value, p_value, std_err = sp.stats.linregress(xx,dat)
superslopes[monii,superii] = slope[eii]
thismonskey = thismonskey + (mod,)
#superkeys[monii,superii] = mod
superii=superii+1
modtrenddt[mod] = slope # trends for e/ ens member in the model group
mntrnd = np.min(slope)
mxtrnd = np.max(slope)
avgtrnd = np.mean(slope)
mndt[mod] = mntrnd
mxdt[mod] = mxtrnd
avgdt[mod] = avgtrnd
# do obs e/ month
homon = cutl.seasonalize_monthlyts(had,mo=monii+1)
xx = np.arange(0,homon.shape[0])
hslope, intercept, r_value, p_value, std_err = sp.stats.linregress(xx,homon)
hmontrenddt[mo] = hslope
nomon = cutl.seasonalize_monthlyts(nsidc,mo=monii+1)
xx = np.arange(0,nomon.shape[0])
nslope, intercept, r_value, p_value, std_err = sp.stats.linregress(xx,nomon)
nmontrenddt[mo] = nslope
superkeys = superkeys + (thismonskey,)
montrenddt[mo] = modtrenddt
monmndt[mo]= mndt
monmxdt[mo]= mxdt
monavgdt[mo] = avgdt
montrenddf = pd.DataFrame(montrenddt)
mndf = pd.DataFrame(monmndt)
mxdf = pd.DataFrame(monmxdt)
avgdf = pd.DataFrame(monavgdt)
superensdf=pd.DataFrame(data=superslopes,index=mons,columns=superkeys[0])
canesm=superensdf.CanESM2
# plot seasonal trends for all ensemble members
hmontrenddf = pd.DataFrame(hmontrenddt,index=(1,))
nmontrenddf = pd.DataFrame(nmontrenddt,index=(1,))
superensdf.plot(color='k',alpha=0.5,legend=False)
plt.plot(canesm,color='r',linewidth=3)
plt.plot(hmontrenddf.values[0],'green',linewidth=3) # not sure why but this has an array w/in an array....
plt.plot(nmontrenddf.values[0],color=ccm.get_linecolor('dodgerblue'),linewidth=3)# not sure why but this has an array w/in an array....
plt.title('SIE 1979-2012 trends (/yr)')
plt.xlabel('Month')
plt.xlim((0,11))
if printtofile:
plt.savefig('SIE_CMIP5_allmemstrend_seacycle_1979-2012_wCanESM2.pdf')
#superensdf.hist()
# min / max histograms each month
fig,axs = plt.subplots(3,4)
fig.set_size_inches(12,9)
for aii,ax in enumerate(axs.flat):
mon = mons[aii]
ax.hist(mndf[mon],color='.5',alpha=0.5)
ax.hist(mxdf[mon],color='orange',alpha=0.5)
ax.axvline(mndf[mon]['CanESM2'],color='k',linewidth=3)
ax.axvline(mxdf[mon]['CanESM2'],color='r',linewidth=3)
ax.set_title(mon)
if printtofile:
fig.savefig('SIE_CMIP5_minmaxtrendhist_allmos_1979-2012_wCanESM2.pdf')
# average trend histograms each month
fig,axs = plt.subplots(3,4)
fig.set_size_inches(12,9)
for aii,ax in enumerate(axs.flat):
mon = mons[aii]
ax.hist(avgdf[mon],color='.5',alpha=0.5)
ax.axvline(avgdf[mon]['CanESM2'],color='k',linewidth=3)
ax.axvline(hmontrenddt[mon],color='green',linewidth=3)
ax.axvline(nmontrenddt[mon],color=ccm.get_linecolor('dodgerblue'),linewidth=3)
ax.set_title(mon)
if printtofile:
fig.savefig('SIE_CMIP5_avgtrendhist_allmos_1979-2012_wCanESM2.pdf')
# want ALL ens trends in one PDF (per month).
fig,axs = plt.subplots(3,4,sharex=True)
fig.set_size_inches(12,9)
for rii,row in enumerate(superensdf.iterrows()):
ax = axs.flat[rii]
mon=row[0] # the name is first in tuple
rseries = row[1] # pandas Series
ax.hist(rseries,color='.5',alpha=0.5)
for vline in rseries.CanESM2:
ax.axvline(vline,color='k',linewidth=3)
ax.axvline(hmontrenddt[mon],color='green',linewidth=3)
ax.axvline(nmontrenddt[mon],color=ccm.get_linecolor('dodgerblue'),linewidth=3)
ax.set_title(mon)
if printtofile:
fig.savefig('SIE_CMIP5_allmemstrendhist_allmos_1979-2012_wCanESM2.pdf')
# CanESM is index 10
cmap='red2blue_w20'
mycmap = plt.cm.get_cmap(cmap)
#plt.rc('axes', color_cycle=mycc) # default colorcycle
canidx=10
canxx=np.squeeze(np.ones((len(mons),1))*canidx)
fig,ax = plt.subplots()
fig.set_size_inches(14,3)
#ax.set_color_cycle(mycc)
mons2= ('Nov','Dec','Jan','Feb',
'Mar','Apr','May',
'Jun','Jul','Aug','Sep',
'Oct')
sep=10
mar=4
colors=('skyblue','steelblue3','steelblue4','mediumblue',
'darkseagreen4','darkseagreen','darkolivegreen3',
'warm5','warm3','warm2','warm1',
'chocolate4')
for mii,mon in enumerate(mons2):
plt.plot(mndf[mon],color=ccm.get_linecolor(colors[mii]),linestyle='None',marker='s')
plt.plot(mxdf[mon],color=ccm.get_linecolor(colors[mii]),linestyle='None',marker='o')
plt.plot(mndf['Sep'],color='orange')
plt.plot(mxdf['Sep'],color=ccm.get_linecolor(colors[sep]))
#plt.plot(mndf,linestyle='None',marker='s')
#plt.plot(mxdf,'r',linestyle='None',marker='o')
plt.plot(canxx,mndf.loc['CanESM2'],color=ccm.get_linecolor(colors[mii]),linestyle='None',marker='s')
plt.plot(canxx,mxdf.loc['CanESM2'],color=ccm.get_linecolor(colors[mii]),linestyle='None',marker='o')
ax.set_xticks(np.arange(0,29))
ax.set_xlim((-1,30))
ax.set_xticklabels(mxdf['Sep'].keys())
ax.set_title('SIE min (square), max (circle) trends')
fig.autofmt_xdate()
if printtofile:
fig.savefig('SIE_CMIP5_minmaxtrend_allmodels_1979-2012.pdf')
# Trend RANGE: By model
fig,ax = plt.subplots()
fig.set_size_inches(14,4)
for mii,mon in enumerate(mons2):
plt.plot(mxdf[mon]-mndf[mon],color=ccm.get_linecolor(colors[mii]),linestyle='None',marker='s')
plt.plot(mxdf['Sep']-mndf['Sep'],color=ccm.get_linecolor(colors[sep]))
plt.plot(mxdf['Mar']-mndf['Mar'],color=ccm.get_linecolor(colors[mar]))
plt.plot(canxx,mxdf.loc['CanESM2']-mndf.loc['CanESM2'],color=ccm.get_linecolor(colors[mii]),linestyle='None',marker='s')
ax.set_xticks(np.arange(0,29))
ax.set_xlim((-1,30))
ax.set_xticklabels(mxdf['Sep'].keys())
ax.set_title('Within model SIE trend range')
fig.autofmt_xdate()
if printtofile:
fig.savefig('SIE_CMIP5_mnmxtrendrange_allmodels_1979-2012.pdf')
rngdf=mxdf-mndf
rngdf=rngdf[rngdf>0] # get rid of ranges of zero (1 ens member)
# ALL MONTHS hists
rngdf.hist()
plt.title('Within model SIE trend ranges')
# Sep and March hists together
fig,ax = plt.subplots()
rngdf['Sep'].hist(color=ccm.get_linecolor(colors[sep]),alpha=0.5)
rngdf['Mar'].hist(color=ccm.get_linecolor(colors[mar]),alpha=0.5)
ax.axvline(rngdf['Sep']['CanESM2'],color=ccm.get_linecolor(colors[sep]),linewidth=3)
ax.axvline(rngdf['Mar']['CanESM2'],color=ccm.get_linecolor(colors[mar]),linewidth=3)
ax.set_title('Sep/Mar within-CMIP5-model trend range (1979-2012)')
# SEA CYCLE trend RANGE
fig,ax = plt.subplots()
fig.set_size_inches(7,4)
for mii,mod in enumerate(models):
plt.plot(mxdf.loc[mod]-mndf.loc[mod],color='0.7')#,color=mycmap.colors[mii])#,linestyle='None',marker='s')
plt.plot(mxdf.loc['CanESM2']-mndf.loc['CanESM2'],'k',linewidth=3)#,linestyle='None',marker='s')
ax.set_xticks(np.arange(0,12))
ax.set_xlim((0,11))
ax.set_xticklabels(mons)
ax.set_title('Range in within-model SIE trends (1979-2012)')
if printtofile:
fig.savefig('SIE_CMIP5_trendrange_seacyc_1979-2012_wCanESM2.pdf')
## for monkey in monmnmxdt.keys():
## rng = monmnmxdt[monkey][1] - monmnmxdt[monkey][0]
## print rng
## #print ensnum
## modata.plot()
## #mondf[monii] = pd.DataFrame(modensdt) # a dictionary of DataFrames of the model ens (one for each month)
## #mondf = pd.DataFrame(mondt)
## #plt.figure()
## #mondf[8].plot()
###### want cmip5 multi-model mean sea ice concentration maps for
# 2002-12 minus 1979-89 @@@
# will have to concatenate rcp4.5 too
othermodels=('CMCC-CMS','CMCC-CM','EC-EARTH','FGOALS-g2',
'GFDL-CM2p1','GFDL-CM3','GFDL-ESM2M','GFDL-ESM2G',
'MIROC4h')
field='sic'
comp='OImon'
bp='/rd40/data/CMIP5/historical/' + field + '/'
timeselc='1979-01-01,1989-12-31'
timeselp1='2002-01-01,2005-12-31'
timeselp2='2006-01-01,2012-12-31'
## ctldt = dict.fromkeys(models)
## p1dt = dict.fromkeys(models) # 2002-2005
## for model in models:
## enum=enumdt[model] # number of ens members
## ctlmoddt={}; p1moddt={}
## for eii in range(1,enum+1):
## if model in (othermodels): # these models have 5 or 10-year files
## print 'skipping ' + model
## else: # add filenames to
## if model in ('HadCM3',):
## fname=bp+model+'/r'+str(eii)+'i1p1/' + field + '_' + comp + '_' +\
## model + '_historical_r' + str(eii)+'i1p1_195912-198411.nc'
## elif model in ('HadGEM2-AO',):
## fname=bp+model+'/r'+str(eii)+'i1p1/' + field + '_' + comp + '_' +\
## model + '_historical_r' + str(eii)+'i1p1_186001-200512.nc'
## elif model in ('HadGEM2-CC',):
## fname=bp+model+'/r'+str(eii)+'i1p1/' + field + '_' + comp + '_' +\
## model + '_historical_r' + str(eii)+'i1p1_195912-200511.nc'
## elif model in ('HadGEM2-ES',):
## fname=bp+model+'/r'+str(eii)+'i1p1/' + field + '_' + comp + '_' +\
## model + '_historical_r' + str(eii)+'i1p1_195912-200512.nc'
## elif model in ('MIROC5',):
## fname=bp+model+'/r'+str(eii)+'i1p1/' + field + '_' + comp + '_' +\
## model + '_historical_r' + str(eii)+'i1p1_185001-201212.nc'
## elif model in ('MPI-ESM-MR',):
## fname=bp+model+'/r'+str(eii)+'i1p1/' + field + '_' + comp + '_' +\
## model + '_historical_r' + str(eii)+'i1p1_190001-199912.nc'
## elif model in ('MRI-ESM1',):
## fname=bp+model+'/r'+str(eii)+'i1p1/' + field + '_' + comp + '_' +\
## model + '_historical_r' + str(eii)+'i1p1_185101-200512.nc'
## elif model in ('GISS-E2-H',) and eii==6:
## fname=bp+model+'/r'+str(eii)+'i1p1/' + field + '_' + comp + '_' +\
## model + '_historical_r' + str(eii)+'i1p1_195101-200012.nc'
## else:
## fname=bp+model+'/r'+str(eii)+'i1p1/' + field + '_' + comp + '_' +\
## model + '_historical_r' + str(eii)+'i1p1_185001-200512.nc'
## print fname
## ctlmoddt[eii],junk = cutl.climatologize(cnc.getNCvar(fname,field,timesel=timeselc))
## p1moddt[eii],junk = cutl.climatologize(cnc.getNCvar(fname,field,timesel=timeselp1))
## ctldt[model]=ctlmoddt
## p1dt[model]=p1moddt
print len(cm5.models)
#bp='/ra40/data/ncs/sic/'
ctldt = dict.fromkeys(models)
p1dt = dict.fromkeys(models) # 2002-2005
for mod in cm5.models:
print mod
meta=cm5.allmodeldt[mod]
if meta==None:
print mod + ' not yet implemented!' # @@@
elif type(meta['fyears'])!=str:
# skip
print ' skipping models w/ annoying years for now'
else:
fyears=meta['fyears']
enum=meta['numens'] # number of ens members
ctlmoddt={}; p1moddt={}
for eii in range(1,enum+1):
if 'skipens' in meta.keys() and eii in meta['skipens']:
print 'skipping ensemble: ' + str(eii)
else:
fname=bp+mod+'/r'+str(eii)+'i1p1/' + field + '_' + comp + '_' +\
mod + '_historical_r' + str(eii)+'i1p1_' + fyears +'.nc'
print fname
ctlmoddt[eii],junk = cutl.climatologize(cnc.getNCvar(fname,field,timesel=timeselc))
p1moddt[eii],junk = cutl.climatologize(cnc.getNCvar(fname,field,timesel=timeselp1))
ctldt[mod]=ctlmoddt
p1dt[mod]=p1moddt