print "scenario", scen
projection_data[scen] = {}
gmt = mag.magicc_gmt[scen]
for i, contrib_name in enumerate(project_these):
print "conribution", contrib_name
calibdata = pickle.load(
open(
"../data/calibration/" +
contrib_name +
".pkl",
"rb"))
proj = np.zeros([len(proj_period), nrealizations])
for n in realizations:
slr,driving_temp = sl.project(gmt, proj_period, calibdata, n)
proj[:, n] = slr
pdata = da.DimArray(proj, axes=[proj_period, realizations],
dims=["time", "runnumber"])
projection_data[scen][contrib_name] = pdata
fname = "../data/projection/projected_slr_" + \
str(nrealizations) + "samples.pkl"
print "save to pickle."
pickle.dump(projection_data, open(fname, "wb"), protocol=2)
realizations = np.arange(nrealizations)
all_contributions = {}
# Monte Carlo Sampling from all the different observational datasets and
# fitting/calibrated parametes.
for i, contrib_name in enumerate(contrib_ids):
print "conribution", contrib_name
calibdata = pickle.load(
open("../data/calibration/" + contrib_name + ".pkl", "rb"))
proj = np.zeros([nrealizations, len(ggd.giss_temp.time)])
for n in realizations:
proj[n, :] = sl.project(ggd.giss_temp, ggd.giss_temp.time, calibdata,
n)
pdata = da.DimArray(proj,
axes=[realizations, ggd.giss_temp.time],
dims=["runnumber", "time"])
all_contributions[contrib_name] = pdata
all_contributions = da.DimArray(all_contributions,
dims=["contribution", "runnumber", "time"])
obs_period = np.arange(1900, 2009)
fig = plt.figure(2)
plt.clf()
ax = plt.subplot(111)
## sampling from MAGICC 600 member ensemble as in paper
## (not done here as data not openly available.)
#gmt = mag.magicc_gmt[nd[scen]]
for i, contrib_name in enumerate(project_these):
print "conribution", contrib_name
calibdata = pickle.load(
open(calibdatadir + contrib_name + ".pkl", "rb"))
pdata = da.DimArray(None,
axes=[proj_period, realizations],
dims=["time", "runnumber"])
for n in realizations:
slr, gmt_i = sl.project(gmt, proj_period, calibdata, n + 1)
pdata[:, n] = slr
projection_data[nd[scen]][contrib_name] = pdata
### Figure 3
## Plot like Fig. 3 in the PNAS manuscript.
## Note the thinner uncertainty bands for certain contributions
## as we do not sample climate uncertainty here
## (MAGICC ensemble is not publicly available).
plt.figure(1)
plotting.fig3(projection_data)
plt.draw()
plt.show()
### Fig. 4
projection_data[nd[scen]] = {}
gmt = tas_data[scen]
## sampling from MAGICC 600 member ensemble as in paper
## (not done here as data not openly available.)
#gmt = mag.magicc_gmt[nd[scen]]
for i,contrib_name in enumerate(project_these):
print "conribution", contrib_name
calibdata = pickle.load(open(calibdatadir+contrib_name+".pkl","rb"))
pdata = da.DimArray(None, axes=[proj_period,realizations],
dims=["time","runnumber"])
for n in realizations:
slr,gmt_i = sl.project(gmt,proj_period,calibdata,n+1)
pdata[:,n] = slr
projection_data[nd[scen]][contrib_name] = pdata
### Figure 3
## Plot like Fig. 3 in the PNAS manuscript.
## Note the thinner uncertainty bands for certain contributions
## as we do not sample climate uncertainty here
## (MAGICC ensemble is not publicly available).
plt.figure(1)
plotting.fig3(projection_data)
plt.draw()
plt.show()
nrealizations = 10000
realizations = np.arange(nrealizations)
all_contributions = {}
# Monte Carlo Sampling from all the different observational datasets and
# fitting/calibrated parametes.
for i, contrib_name in enumerate(contrib_ids):
print "conribution", contrib_name
calibdata = pickle.load(open("../data/calibration/" + contrib_name + ".pkl", "rb"))
proj = np.zeros([nrealizations, len(ggd.giss_temp.time)])
for n in realizations:
proj[n, :] = sl.project(ggd.giss_temp, ggd.giss_temp.time, calibdata, n)
pdata = da.DimArray(proj, axes=[realizations, ggd.giss_temp.time], dims=["runnumber", "time"])
all_contributions[contrib_name] = pdata
all_contributions = da.DimArray(all_contributions, dims=["contribution", "runnumber", "time"])
obs_period = np.arange(1900, 2009)
fig = plt.figure(2)
plt.clf()
ax = plt.subplot(111)
natural_gic_contrib = gd.marzeion_gic_nat_up.mean(axis="model") / 1.0e3
# gd.
past_sl_anom = gd.church_past_sl[obs_period] - natural_gic_contrib[obs_period]
projection_data = {}
for scen in ["RCP3PD", "RCP45", "RCP85"]:
print "scenario", scen
projection_data[scen] = {}
gmt = mag.magicc_gmt[scen]
for i, contrib_name in enumerate(project_these):
print "conribution", contrib_name
calibdata = pickle.load(
open("../data/calibration/" + contrib_name + ".pkl", "rb"))
proj = np.zeros([len(proj_period), nrealizations])
for n in realizations:
slr, driving_temp = sl.project(gmt, proj_period, calibdata, n)
proj[:, n] = slr
pdata = da.DimArray(proj,
axes=[proj_period, realizations],
dims=["time", "runnumber"])
projection_data[scen][contrib_name] = pdata
fname = "../data/projection/projected_slr_" + \
str(nrealizations) + "samples.pkl"
print "save to pickle."
pickle.dump(projection_data, open(fname, "wb"), protocol=2)
