marzeion_gic_nat_mb = da.DimArray(marzeion_gic_nat_mb[:, 1:],
axes=[marzeion_gic_nat_mb[:, 0], models], dims=["time", "model"])
fullfile = inputdatadir + "glaciers_marzeion14/global_mean_specific_mb_full_rgi_v4.txt"
marzeion_gic_full_mb = np.loadtxt(fullfile, skiprows=2)
models = np.genfromtxt(fullfile, skip_header=1, dtype=None)[0, 1:]
marzeion_gic_full_mb = da.DimArray(marzeion_gic_full_mb[:, 1:],
axes=[marzeion_gic_full_mb[:, 0], models], dims=["time", "model"])
anthropogenic_frac = (marzeion_gic_full_mb.mean(axis=1)
- marzeion_gic_nat_mb.mean(axis=1)) / marzeion_gic_full_mb.mean(axis=1)
anthropogenic_frac_perc = ((marzeion_gic_full_mb.T.to_pandas().quantile([0.05, 0.5, 0.95])
- marzeion_gic_nat_mb.T.to_pandas().quantile([0.05, 0.5, 0.95]))
/ marzeion_gic_full_mb.T.to_pandas().quantile([0.05, 0.5, 0.95]))
anthropogenic_frac_perc = da.from_pandas(
anthropogenic_frac_perc, dims=[
"percentile", "time"])
# TODO: reference motivate linear increase of anth fraction
#anthropogenic_frac_lin = da.DimArray(np.linspace(0,1.,2056-1870),axes=np.arange(1870,2056,1),dims="time")
# linear fit to fraction since 1900
tlin = np.arange(1870, 2056, 1)
anthropogenic_frac_lin_1900 = da.DimArray(
6.78606933e-03 *
tlin -
1.28443788e+01,
axes=tlin,
dims="time")
######## Greenland surface mass balance ########
divider = make_axes_locatable(ax)
axy = divider.append_axes("right", size=0.6, pad=0.0, sharey=ax, axisbg="grey")
axy.axis("off")
axy.axvspan(0, 10, facecolor="0.5", alpha=0.2, lw=0)
# print name
xloc = 10 # ipcc
oloc = 4 # our estimates
# single_contribs[name] = {}
for k, scen in enumerate(["RCP85", "RCP45", "RCP3PD"]):
contrib = projection_data[scen][name]
# anomaly to 1986-2005
contrib = contrib - contrib[1986:2005, :].mean(axis="time")
contrib = contrib.T.to_pandas()
percentiles = da.from_pandas(contrib.quantile([0.05, 0.5, 0.95]))[:, plot_period]
ax.fill_between(
plot_period, percentiles[0.05] * 1e3, percentiles[0.95] * 1e3, color=rcpcoldict[scen], alpha=0.4, lw=0.5
)
ax.plot(
plot_period, percentiles[0.5] * 1e3, lw=3, color=rcpcoldict[scen], alpha=1.0, label=rcpnamedict[scen]
) # ,label=name)
print "##", name, scen, ": ", percentiles[0.05][2100] * 1e3, percentiles[0.5][2100] * 1e3, percentiles[0.95][
2100
] * 1e3
oloc -= 1.0
xloc -= 1.0
med = percentiles[0.5][np.arange(2081, 2100, 1)].mean() * 1e3
def fig3(projection_data):
labels = {"gic": "Mountain glaciers", "thermexp": "Thermal expansion", "gis_sid": "Greenland SID",
"gis_smb": "Greenland SMB", "ant_sid": "Antarctica SID", "ant_smb": "Antarctica SMB"}
plt.subplots_adjust(left=0.1, bottom=0.06, right=1.0, top=0.97,
wspace=0.1, hspace=None)
# print "## sl contribtioon, scenario, 5th percentiles in 2100, median in
# 2100, 95th percentile in 2100"
plot_period = np.arange(2000, 2101, 1)
axs = []
plotlb = "abcdef"
for i, name in enumerate(contrib_ids[0:6]):
ax = plt.subplot(3, 2, i + 1)
# ax = plt.subplot(111)
divider = make_axes_locatable(ax)
axy = divider.append_axes(
"right",
size=0.6,
pad=0.0,
sharey=ax,
axisbg="grey")
axy.axis("off")
axy.axvspan(0, 10, facecolor='0.5', alpha=0.2, lw=0)
# print name
xloc = 10 # ipcc
oloc = 4 # our estimates
# single_contribs[name] = {}
for k, scen in enumerate(["RCP85", "RCP45", "RCP3PD"]):
contrib = projection_data[scen][name]
# anomaly to 1986-2005
contrib = contrib - contrib[1986:2005, :].mean(axis="time")
# pandas handles NaNs within percentile calculus
contrib = contrib.T.to_pandas()
percentiles = da.from_pandas(contrib.quantile([0.05, 0.5, 0.95]))[
:, plot_period]
ax.fill_between(
plot_period,
percentiles[0.05] * 1e3,
percentiles[0.95] * 1e3,
color=rcpcoldict[scen],
alpha=.4,
lw=.5)
ax.plot(
plot_period,
percentiles[0.5] * 1e3,
lw=3,
color=rcpcoldict[scen],
alpha=1.,
label=rcpnamedict[scen])
oloc -= 1.
xloc -= 1.
med = percentiles[0.5][np.arange(2081, 2100, 1)].mean() * 1e3
low = percentiles[0.05][np.arange(2081, 2100, 1)].mean() * 1e3
upp = percentiles[0.95][np.arange(2081, 2100, 1)].mean() * 1e3
axy.plot([oloc - 1, oloc + 1], [med, med],
lw=3, color=rcpcoldict[scen])
axy.fill_between([oloc - 1, oloc + 1], [low, low],
[upp, upp], color=rcpcoldict[scen], alpha=.4, lw=0.)
low, med, upp = ipcc.ipcc_contrib_estimates[name][scen]
axy.fill_between([xloc - 1, xloc + 1], [low, low],
[upp, upp], color=rcpcoldict[scen], alpha=.4, lw=0.)
axy.plot([xloc - 1, xloc + 1], [med, med],
color=rcpcoldict[scen], lw=3, alpha=1.)
axy.set_xlim(-1, 11)
ax.text(0.05, 0.9, plotlb[i], transform=ax.transAxes,
fontdict={'family': 'sans-serif', 'weight': 'bold', "size": 16})
ax.text(0.05, 0.8, labels[name], transform=ax.transAxes,
fontdict={'family': 'sans-serif', 'weight': 'bold'})
if i == 0:
axy.text(1.5, 440, "M16", rotation="vertical", horizontalalignment='center',
verticalalignment='center')
axy.text(7.5, 440, "IPCC", rotation="vertical", horizontalalignment='center',
verticalalignment='center')
l1 = ax.legend(ncol=1, loc="center left")
l1.draw_frame(0)
for l in l1.get_lines():
l.set_alpha(1)
axs.append(ax)
ax.set_xlim(plot_period[0], plot_period[-1])
for ax in axs[-2:]:
ax.set_xlabel("Time in years")
for ax in [axs[0], axs[2], axs[4]]:
ax.set_ylabel("Sea level in mm")
axs[3].set_ylim(0, 500)
sharey=ax,
axisbg="grey")
axy.axis("off")
axy.axvspan(0, 10, facecolor='0.5', alpha=0.2, lw=0)
# print name
xloc = 10 # ipcc
oloc = 4 # our estimates
# single_contribs[name] = {}
for k, scen in enumerate(["RCP85", "RCP45", "RCP3PD"]):
contrib = projection_data[scen][name]
# anomaly to 1986-2005
contrib = contrib - contrib[1986:2005, :].mean(axis="time")
contrib = contrib.T.to_pandas()
percentiles = da.from_pandas(contrib.quantile([0.05, 0.5,
0.95]))[:, plot_period]
ax.fill_between(plot_period,
percentiles[0.05] * 1e3,
percentiles[0.95] * 1e3,
color=rcpcoldict[scen],
alpha=.4,
lw=.5)
ax.plot(plot_period,
percentiles[0.5] * 1e3,
lw=3,
color=rcpcoldict[scen],
alpha=1.,
label=rcpnamedict[scen]) # ,label=name)
print "##", name, scen, ": ", percentiles[0.05][
fullfile = inputdatadir + "glaciers_marzeion14/global_mean_specific_mb_full_rgi_v4.txt"
marzeion_gic_full_mb = np.loadtxt(fullfile, skiprows=2)
models = np.genfromtxt(fullfile, skip_header=1, dtype=None)[0, 1:]
marzeion_gic_full_mb = da.DimArray(marzeion_gic_full_mb[:, 1:],
axes=[marzeion_gic_full_mb[:, 0], models],
dims=["time", "model"])
anthropogenic_frac = (
marzeion_gic_full_mb.mean(axis=1) -
marzeion_gic_nat_mb.mean(axis=1)) / marzeion_gic_full_mb.mean(axis=1)
anthropogenic_frac_perc = (
(marzeion_gic_full_mb.T.to_pandas().quantile([0.05, 0.5, 0.95]) -
marzeion_gic_nat_mb.T.to_pandas().quantile([0.05, 0.5, 0.95])) /
marzeion_gic_full_mb.T.to_pandas().quantile([0.05, 0.5, 0.95]))
anthropogenic_frac_perc = da.from_pandas(anthropogenic_frac_perc,
dims=["percentile", "time"])
# TODO: reference motivate linear increase of anth fraction
#anthropogenic_frac_lin = da.DimArray(np.linspace(0,1.,2056-1870),axes=np.arange(1870,2056,1),dims="time")
# linear fit to fraction since 1900
tlin = np.arange(1870, 2056, 1)
anthropogenic_frac_lin_1900 = da.DimArray(6.78606933e-03 * tlin -
1.28443788e+01,
axes=tlin,
dims="time")
######## Greenland surface mass balance ########
def convert_raw_mb_to_sl(rawdata):
