def calc_SNR():
SNRs = []
N = 20
for endyear in range(2021, 2096):
print(endyear)
members = []
for i in range(1, N + 1):
ncpath = glob.glob(
"/Volumes/CESM-GLENS/GLENS/b.e15.B5505C5WCCML45BGCR.f09_g16.feedback.0"
+ str(i).zfill(2) +
"/atm/proc/tseries/month_1/Combined/b.e15.B5505C5WCCML45BGCR.f09_g16.feedback.0"
+ str(i).zfill(2) + ".cam.h0." + varcode + ".202001-*.nc")[0]
vartimeobj = vartimeproc.VarTimeProc(ncpath,
tim1=2020,
tim2=endyear,
varcode=varcode)
trend = vartimeobj.trend_lat_lon(season)
members.append(trend)
ensmean, ensstd = ensemble_defs.stats(members)
SNR = abs(ensmean) / (ensstd / np.sqrt(N - 1))
SNRs.append(SNR)
dim = 'endyear'
new_coord = range(2021, 2096)
func = lambda *x: np.stack(x, axis=-1)
xSNR = xr.apply_ufunc(func,
*SNRs,
output_core_dims=[[dim]],
join='outer',
dataset_fill_value=np.nan)
xSNR[dim] = new_coord
xSNR.to_netcdf('SNR_' + varcode + '_trend.nc')
return
def clim_lat_hgt(run, season, varcode):
members = []
# Control Climatology
if run == 'control':
print("Calculating climatology for control")
for i in range(1, 21):
ncpath = glob.glob(
"/Volumes/CESM-GLENS/GLENS/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) +
"/atm/proc/tseries/month_1/Combined/p.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) + ".cam.h0zm." + varcode + ".201001-*.nc")[0]
vartimeobj = vartimeproc.VarTimeProc(ncpath,
tim1=2010,
tim2=2030,
varcode=varcode,
zm=True)
clim = vartimeobj.clim_mean(season)
members.append(clim)
# RCP8.5
if run == 'rcp85':
print("Calculating trend for RCP8.5")
for i in [1, 2, 3]:
ncpath = glob.glob(
"/Volumes/CESM-GLENS/GLENS/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) +
"/atm/proc/tseries/month_1/Combined/p.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) + ".cam.h0zm." + varcode + ".201001-*.nc")[0]
vartimeobj = vartimeproc.VarTimeProc(ncpath,
tim1=2075,
tim2=2095,
varcode=varcode,
zm=True)
clim = vartimeobj.clim_mean(season)
members.append(clim)
# Feedback
elif run == 'feedback':
print("Calculating trend for Feedback")
for i in range(1, 21):
ncpath = glob.glob(
"/Volumes/CESM-GLENS/GLENS/b.e15.B5505C5WCCML45BGCR.f09_g16.feedback.0"
+ str(i).zfill(2) +
"/atm/proc/tseries/month_1/Combined/p.e15.B5505C5WCCML45BGCR.f09_g16.feedback.0"
+ str(i).zfill(2) + ".cam.h0zm." + varcode + ".202001-*.nc")[0]
vartimeobj = vartimeproc.VarTimeProc(ncpath,
tim1=2075,
tim2=2095,
varcode=varcode,
zm=True)
clim = vartimeobj.clim_mean(season)
members.append(clim)
# GEOHEAT_S
elif run == 'geoheats':
print("Calculating trend for GEOHEAT_S")
for i in range(1, 5):
yrvals = []
for yr in range(2011, 2031):
ncpath = glob.glob(
"/Volumes/CESM-GLENS/SUE/" + str(i).zfill(3) +
"/b.e15.B5505C5WCCML45BGCR.f09_g16.GEOHEATSUE." +
str(i).zfill(3) + "_" + str(yr) + "/Combined/" + varcode +
".b.e15.B5505C5WCCML45BGCR.f09_g16.GEOHEATSUE." +
str(i).zfill(3) + "_" + str(yr) + ".zm.nc")[0]
vartimeobj = vartimeproc.VarTimeProc(ncpath,
tim1=yr,
tim2=yr + 1,
varcode=varcode,
zm=True)
clim = vartimeobj.clim_mean(season)
yrvals.append(clim)
yrvals_mean, yrvals_std = ensemble_defs.stats(yrvals)
members.append(yrvals_mean)
return members
def clim_lat_lon(run, season, varcode):
members = []
# Control climatology
if run == 'control':
print("Calculating climatology for Control")
for i in range(1, 21):
if varcode == 'precip':
ncpath1 = glob.glob(
"/Volumes/CESM-GLENS/GLENS/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) +
"/atm/proc/tseries/month_1/Combined/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) + ".cam.h0.PRECC.201001-*.nc")[0]
ncpath2 = glob.glob(
"/Volumes/CESM-GLENS/GLENS/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) +
"/atm/proc/tseries/month_1/Combined/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) + ".cam.h0.PRECL.201001-*.nc")[0]
vartimeobj = vartimeproc.PrecipTimeProc(ncpath1,
ncpath2,
tim1=2010,
tim2=2030,
ppt1='PRECC',
ppt2='PRECL')
else:
ncpath = glob.glob(
"/Volumes/CESM-GLENS/GLENS/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) +
"/atm/proc/tseries/month_1/Combined/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) + ".cam.h0." + varcode +
".201001-*.nc")[0]
vartimeobj = vartimeproc.VarTimeProc(ncpath,
tim1=2010,
tim2=2030,
varcode=varcode)
clim = vartimeobj.clim_mean(season)
members.append(clim)
# RCP8.5
if run == 'rcp85':
print("Calculating climatology for RCP8.5")
for i in [1, 2, 3]:
# Precip
if varcode == 'precip':
ncpath1 = glob.glob(
"/Volumes/CESM-GLENS/GLENS/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) +
"/atm/proc/tseries/month_1/Combined/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) + ".cam.h0.PRECC.201001-*.nc")[0]
ncpath2 = glob.glob(
"/Volumes/CESM-GLENS/GLENS/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) +
"/atm/proc/tseries/month_1/Combined/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) + ".cam.h0.PRECL.201001-*.nc")[0]
vartimeobj = vartimeproc.PrecipTimeProc(ncpath1,
ncpath2,
tim1=2075,
tim2=2095,
ppt1='PRECC',
ppt2='PRECL')
# All other variables
else:
ncpath = glob.glob(
"/Volumes/CESM-GLENS/GLENS/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) +
"/atm/proc/tseries/month_1/Combined/b.e15.B5505C5WCCML45BGCR.f09_g16.control.0"
+ str(i).zfill(2) + ".cam.h0." + varcode +
".201001-*.nc")[0]
vartimeobj = vartimeproc.VarTimeProc(ncpath,
tim1=2075,
tim2=2095,
varcode=varcode)
clim = vartimeobj.clim_mean(season)
members.append(clim)
# Feedback
elif run == 'feedback':
print("Calculating climatology for Feedback")
for i in range(1, 21):
# Precip
if varcode == 'precip':
ncpath1 = glob.glob(
"/Volumes/CESM-GLENS/GLENS/b.e15.B5505C5WCCML45BGCR.f09_g16.feedback.0"
+ str(i).zfill(2) +
"/atm/proc/tseries/month_1/Combined/b.e15.B5505C5WCCML45BGCR.f09_g16.feedback.0"
+ str(i).zfill(2) + ".cam.h0.PRECC.202001-*.nc")[0]
ncpath2 = glob.glob(
"/Volumes/CESM-GLENS/GLENS/b.e15.B5505C5WCCML45BGCR.f09_g16.feedback.0"
+ str(i).zfill(2) +
"/atm/proc/tseries/month_1/Combined/b.e15.B5505C5WCCML45BGCR.f09_g16.feedback.0"
+ str(i).zfill(2) + ".cam.h0.PRECL.202001-*.nc")[0]
vartimeobj = vartimeproc.PrecipTimeProc(ncpath1,
ncpath2,
tim1=2075,
tim2=2095,
ppt1='PRECC',
ppt2='PRECL')
# All other variables
else:
ncpath = glob.glob(
"/Volumes/CESM-GLENS/GLENS/b.e15.B5505C5WCCML45BGCR.f09_g16.feedback.0"
+ str(i).zfill(2) +
"/atm/proc/tseries/month_1/Combined/b.e15.B5505C5WCCML45BGCR.f09_g16.feedback.0"
+ str(i).zfill(2) + ".cam.h0." + varcode +
".202001-*.nc")[0]
vartimeobj = vartimeproc.VarTimeProc(ncpath,
tim1=2075,
tim2=2095,
varcode=varcode)
clim = vartimeobj.clim_mean(season)
members.append(clim)
# GEOHEAT_S
elif run == 'geoheats':
print("Calculating climatology for GEOHEAT_S")
for i in range(1, 5):
yrvals = []
for yr in range(2011, 2031):
# Precip
if varcode == 'precip':
ncpath1 = glob.glob(
"/Volumes/CESM-GLENS/SUE/" + str(i).zfill(3) +
"/b.e15.B5505C5WCCML45BGCR.f09_g16.GEOHEATSUE." +
str(i).zfill(3) + "_" + str(yr) +
"/Combined/PRECC.b.e15.B5505C5WCCML45BGCR.f09_g16.GEOHEATSUE."
+ str(i).zfill(3) + "_" + str(yr) + ".nc")[0]
ncpath2 = glob.glob(
"/Volumes/CESM-GLENS/SUE/" + str(i).zfill(3) +
"/b.e15.B5505C5WCCML45BGCR.f09_g16.GEOHEATSUE." +
str(i).zfill(3) + "_" + str(yr) +
"/Combined/PRECL.b.e15.B5505C5WCCML45BGCR.f09_g16.GEOHEATSUE."
+ str(i).zfill(3) + "_" + str(yr) + ".nc")[0]
vartimeobj = vartimeproc.PrecipTimeProc(ncpath1,
ncpath2,
tim1=yr,
tim2=yr + 1,
ppt1='PRECC',
ppt2='PRECL')
# All other variables
else:
ncpath = glob.glob(
"/Volumes/CESM-GLENS/SUE/" + str(i).zfill(3) +
"/b.e15.B5505C5WCCML45BGCR.f09_g16.GEOHEATSUE." +
str(i).zfill(3) + "_" + str(yr) + "/Combined/" +
varcode +
".b.e15.B5505C5WCCML45BGCR.f09_g16.GEOHEATSUE." +
str(i).zfill(3) + "_" + str(yr) + ".nc")[0]
vartimeobj = vartimeproc.VarTimeProc(ncpath,
tim1=yr,
tim2=yr + 1,
varcode=varcode)
clim = vartimeobj.clim_mean(season)
yrvals.append(clim)
yrvals_mean, yrvals_std = ensemble_defs.stats(yrvals)
members.append(yrvals_mean)
# Convert to hPa for PSL
if varcode == 'PSL':
members = [x * 0.01 for x in members]
return members
'T' :{'rcp85':(5,1), 'feedback':(5,1)}}
contours = {'U' :{'rcp85':(60,10), 'feedback':(60,10)},\
'T' :{'rcp85':(300,10), 'feedback':(300,10)}}
cdp = {'U' :{'rcp85':0, 'feedback':0},\
'T' :{'rcp85':0, 'feedback':0}}
clabel = {'U' :'Zonal mean zonal wind (ms$^{-1}$ per 30 yrs)',\
'T' :'Temperature ($^{\circ}$C per 30 yrs)'}
#********************************************************************************************************
# Control climatology
members_control = clim_defs.clim_lat_hgt('control', season, varcode)
nmembers_control = len(members_control)
ensmean_control, ensstd_control = ensemble_defs.stats(members_control)
# Perturbation climatology
members = trend_defs.trend_lat_hgt(run, season, varcode)
nmembers = len(members)
ensmean, ensstd = ensemble_defs.stats(members)
ttest = ensemble_defs.t_test_onesample(alpha, ensmean, ensstd, nmembers)
areawgt = np.cos(np.deg2rad(ensmean.lat))
#ensmean_weighted = ensmean.weighted(areawgt)
T50trop = (ensmean.sel(lat=slice(-30, 30), level=50) * areawgt.sel(
lat=slice(-30, 30))).sum() / areawgt.sel(lat=slice(-30, 30)).sum()
T50pole = (ensmean.sel(lat=slice(60, 90), level=50) * areawgt.sel(
lat=slice(60, 90))).sum() / areawgt.sel(lat=slice(60, 90)).sum()
print(T50trop.values - T50pole.values)
'''
'precip' :{'rcp85':(0.4,0.1), 'feedback':(1,0.4), 'geoheats':(0.4,0.1)},\
'PSL' :{'rcp85':(1.6,0.4), 'feedback':(1.6,0.4), 'geoheats':(1.6,0.4)}}
colorscale= {'TREFHT' :'BlueRed',\
'precip' :'BrownGreen',\
'PSL' :'BlueRed'}
clabel = {'TREFHT' :'$^{\circ}$C per 30 yrs',\
'precip' :'mm/day per 30 yrs',\
'PSL' :'hPa per 30 yrs'}
#********************************************************************************************************
# Base climatology
members_base = clim_defs.clim_lat_lon('control', season, varcode)
nmembers_base = len(members_base)
ensmean_base, ensstd_base = ensemble_defs.stats(members_base)
# Perturbation climatology
members = clim_defs.clim_lat_lon(run, season, varcode)
nmembers = len(members)
ensmean, ensstd = ensemble_defs.stats(members)
# Difference to Base
ensdiff = ensmean - ensmean_base
ttest = ensemble_defs.t_test_twosample(alpha, ensdiff, ensstd_base, ensstd,
nmembers_base, nmembers)
# Convert to trend
ensdiff = ensdiff / 65. * 30.
# Plot ensemble mean
dataset_fill_value=np.nan)
stack[dim] = new_coord
# Interannual sigma from residuals of annual means from member average gives similar results
ensmean_control = stack.mean(dim=('time', 'member'))
ensstd_control = stack.std(dim=('time', 'member'))
plot_defs.plot_ToE(ensstd_control, ensstd_control['lat'],
ensstd_control['lon'], '(b) Interannual $\sigma$',
outdir + 'stdcontrol.png', 0.4, 3.6, 0.4, '$\circ$C')
#********************************************************************************************************
# 2) Signal: end of century Feedback response
members = clim_defs.clim_lat_lon('feedback', season, varcode)
ensmean, ensstd = ensemble_defs.stats(members)
ensdiff = ensmean - ensmean_control
plot_defs.plot_single_lat_lon(
ensdiff, ensmean['lat'], ensmean['lon'],
'(a) GEO8.5$_{2075-2095}$ - Base$_{2010-2030}$',
outdir + varcode + '_clim_feedback-control_' + season + '.png', 3.6, 0.4,
3.6, 0.4, '$^{\circ}$C')
#********************************************************************************************************
# 3) Signal-to-noise ratio
SNR = abs(ensdiff) / ensstd_control
plot_defs.plot_ToE(SNR, ensmean['lat'], ensmean['lon'],
'(a) End-of-century\nSNR', outdir + 'SNR.png', 0.2, 2.2,
0.2, '')