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
import ensemble_functions
import plot_functions
import vartimeproc
#********************************************************************************************************
season = 'DJF'
outdir="/Users/abanerjee/scripts/glens/output/"
varcode = 'TREFHT'
#********************************************************************************************************
# 1) Plot interannual sigma from Base variability
# annual, seasonal mean
members_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/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)
ann = vartimeobj.annual_mean(season)
members_control.append(ann)
# adding coordinate
dim = 'member'
new_coord = range(len(members_control))
func = lambda *x: np.stack(x, axis=-1)
stack = xr.apply_ufunc(func, *members_control,
output_core_dims=[[dim]],
join='outer',
dataset_fill_value=np.nan)
stack[dim] = new_coord
# Interannual sigma from residuals of annual means from member average gives similar results
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
"/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=2020,
tim2=2095,
ppt1='PRECC',
ppt2='PRECL')
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=2020,
tim2=2095,
varcode=varcode)
tseries_surf = vartimeobj.annual_mean(season) * scaler[varcode]
trend_surf = vartimeobj.trend_lat_lon(season) * scaler[varcode]
# NAM index at 50hPa: timeseries and trend
ilev = np.where(nplevel == 50)[0][0]
tseries_NAM = np.load(npydir + 'NAM-Z3_PC_feedback_' + str(i) +
'_DJF.npy')[:, ilev]
nyrs = tseries_NAM.shape[0]
trend_NAM = ss.linregress(range(nyrs), tseries_NAM)[0] * 30
# congruent trend
regress = lambda y: ss.linregress(tseries_NAM, y)[0] * trend_NAM
tseries_surf_flat = tseries_surf.values.reshape(
[tseries_surf.shape[0], tseries_surf.shape[1] * tseries_surf.shape[2]])
