def read_ohc(filename):
indata = np.genfromtxt(filename, dtype=(float), skip_header=1)
hadley = utils.Timeseries("OHC", indata[:, 0], indata[:, 1])
csiro = utils.Timeseries("OHC", indata[:, 0], indata[:, 3])
pmel = utils.Timeseries("OHC", indata[:, 0], indata[:, 5])
ncei = utils.Timeseries("OHC", indata[:, 0], indata[:, 7])
mri = utils.Timeseries("OHC", indata[:, 0], indata[:, 9])
iap = utils.Timeseries("OHC", indata[:, 0], indata[:, 9])
# reapply updated climatology
dummy, hadley = utils.calculate_climatology_and_anomalies_1d(hadley, 1993, 2016)
dummy, csiro = utils.calculate_climatology_and_anomalies_1d(csiro, 1993, 2016)
dummy, pmel = utils.calculate_climatology_and_anomalies_1d(pmel, 1993, 2016)
dummy, ncei = utils.calculate_climatology_and_anomalies_1d(ncei, 1993, 2016)
dummy, mri = utils.calculate_climatology_and_anomalies_1d(mri, 1993, 2016)
dummy, iap = utils.calculate_climatology_and_anomalies_1d(iap, 1993, 2016)
return hadley, csiro, pmel, ncei, mri, iap # read_ohc
def run_all_plots():
#***********************
# MODIS - centre
cubelist = iris.load(
os.path.join(data_loc,
"MODIS.CMG.{}.SOS.EOS.Anomaly.nc".format(settings.YEAR)))
for c, cube in enumerate(cubelist):
if cube.name() == "SOS":
sos_cube = cubelist[c]
# deal with NANS
sos_cube.data = np.ma.masked_where(sos_cube.data != sos_cube.data,
sos_cube.data)
# read in sites
us_locations = read_us_phenocam(os.path.join(data_loc,
"phenocam_locs.txt"))
fig = plt.figure(figsize=(8, 12))
plt.clf()
# set up plot settings
BOUNDS = [-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100]
LABELS = "(c) Start of Season (SOS)" #, "(b) End of Season (EOS)"]
# boundary circle
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
# axes for polar plot
ax = plt.axes(
[0.05, 0.01, 0.9, 0.65],
projection=cartopy.crs.NorthPolarStereo(central_longitude=300.0))
plot_cube = sos_cube
if settings.OUTFMT in [".eps", ".pdf"]:
if plot_cube.coord("latitude").points.shape[0] > 90 or plot_cube.coord(
"longitude").points.shape[0] > 360:
regrid_size = 1.0
print(
"Regridding cube for {} output to {} degree resolution".format(
settings.OUTFMT, regrid_size))
print("Old Shape {}".format(plot_cube.data.shape))
plot_cube = utils.regrid_cube(plot_cube, regrid_size, regrid_size)
print("New Shape {}".format(plot_cube.data.shape))
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines()
ax.set_boundary(circle, transform=ax.transAxes)
cmap = settings.COLOURMAP_DICT["phenological_r"]
norm = mpl.cm.colors.BoundaryNorm(BOUNDS, cmap.N)
mesh = iris.plot.pcolormesh(plot_cube, cmap=cmap, norm=norm, axes=ax)
# plot scatter
COL = "yellow"
ax.scatter(us_locations[1],
us_locations[0],
c=COL,
s=100,
edgecolor="k",
transform=cartopy.crs.Geodetic(),
zorder=10)
ax.scatter(-2.9376,
54.3739,
c=COL,
s=100,
edgecolor="k",
transform=cartopy.crs.Geodetic(),
zorder=10)
# uk box
region = [-10.0, 49.0, 3.0, 60.0]
ax.plot([region[0], region[0]], [region[1], region[3]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.PlateCarree())
ax.plot([region[2], region[2]], [region[1], region[3]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.PlateCarree())
ax.plot([region[0], region[2]], [region[1], region[1]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.Geodetic())
ax.plot([region[0], region[2]], [region[3], region[3]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.Geodetic())
COL = "k"
ax.plot([region[0], region[0]], [region[1], region[3]],
c=COL,
ls='-',
lw=5,
zorder=9,
transform=cartopy.crs.PlateCarree())
ax.plot([region[2], region[2]], [region[1], region[3]],
c=COL,
ls='-',
lw=5,
zorder=9,
transform=cartopy.crs.PlateCarree())
ax.plot([region[0], region[2]], [region[1], region[1]],
c=COL,
ls='-',
lw=5,
zorder=9,
transform=cartopy.crs.Geodetic())
ax.plot([region[0], region[2]], [region[3], region[3]],
c=COL,
ls='-',
lw=5,
zorder=9,
transform=cartopy.crs.Geodetic())
# label axes
ax.text(-0.1,
1.0,
LABELS,
fontsize=settings.FONTSIZE * 0.8,
transform=ax.transAxes)
cb = plt.colorbar(mesh,
orientation='horizontal',
ticks=BOUNDS[1:-1],
label="Anomaly (days)",
drawedges=True,
fraction=0.1,
pad=0.05,
aspect=15,
shrink=0.8)
# prettify
cb.set_ticklabels(["{:g}".format(b) for b in BOUNDS[1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
ax.set_extent([-180, 180, 30, 90], cartopy.crs.PlateCarree())
for lat in range(30, 100, 10):
ax.text(180,
lat,
'{}$^\circ$N'.format(lat),
transform=cartopy.crs.Geodetic())
fig.subplots_adjust(bottom=0.05,
top=0.95,
left=0.04,
right=0.95,
wspace=0.02)
del sos_cube
del cubelist
#***********************
# MODIS timeseries - 2018
sos_na, sos_ea, sprt_na_orig, sprt_ea_orig = read_modis_ts(
os.path.join(
data_loc,
"MODIS.CMG.{}.SOS.EOS.SPRT.FALT.TS.csv".format(settings.YEAR)))
dummy, sos_na = utils.calculate_climatology_and_anomalies_1d(
sos_na, 2000, 2010)
dummy, sos_ea = utils.calculate_climatology_and_anomalies_1d(
sos_ea, 2000, 2010)
dummy, sprt_na = utils.calculate_climatology_and_anomalies_1d(
sprt_na_orig, 2000, 2010)
dummy, sprt_ea = utils.calculate_climatology_and_anomalies_1d(
sprt_ea_orig, 2000, 2010)
ax = plt.axes([0.1, 0.7, 0.8, 0.15])
label = "(b) North America"
anomalies = [
"2018 SOS Anomaly = 1.86 days",
"2018 Spring T anomaly = -0.75 " + r'$^{\circ}$' + "C"
]
plot_modis_ts(ax, sos_na, sprt_na, sprt_na_orig, label, anomalies,
LEGEND_LOC)
na_trend = -0.64 / 10
ax.plot([sos_na.times[0], sos_na.times[-1]],
utils.trendline(na_trend, sos_na.times),
ls="--",
zorder=10,
c="g",
lw=2)
print(utils.trendline(na_trend, sos_na.times))
ax1 = plt.axes([0.1, 0.85, 0.8, 0.15], sharex=ax)
label = "(a) Eurasia"
anomalies = [
"2018 SOS Anomaly = 2.01 days",
"2018 Spring T anomaly = 0.13 " + r'$^{\circ}$' + "C"
]
plot_modis_ts(ax1, sos_ea, sprt_ea, sprt_ea_orig, label, anomalies, "")
ea_trend = -1.59 / 10
ax1.plot([sos_ea.times[0], sos_ea.times[-1]],
utils.trendline(ea_trend, sos_ea.times),
ls="--",
zorder=10,
c="g",
lw=2)
print(utils.trendline(ea_trend, sos_ea.times))
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax.get_xticklabels(), visible=True)
fig.text(0.05, 0.92, "SOS Anomaly (days)", rotation="vertical")
fig.text(0.95,
0.92,
"Temperature Anomaly (" + r'$^{\circ}$' + "C)",
rotation="vertical")
plt.savefig(image_loc +
"PHEN_modis_{}{}".format(settings.YEAR, settings.OUTFMT))
#***********************
# US timeseries - 2018
fig = plt.figure(figsize=(8, 10))
plt.clf()
barrow, ozarks, turkey = read_us_phenocam_csv(
os.path.join(data_loc, "Richardson PhenoCam plots for Sidebar.csv"))
ax = plt.axes([0.1, 0.03, 0.35, 0.17])
plot_us_phenocam(ax, barrow, [160, 229], "Barrow")
ax.text(0.05, 0.85, "(e) Barrow", transform=ax.transAxes)
ax = plt.axes([0.1, 0.23, 0.35, 0.17])
plot_us_phenocam(ax,
ozarks, [90, 159],
"Ozarks",
ylabel="Vegetation Greenness Index",
legend=True)
ax.text(0.05, 0.85, "(d) Ozarks", transform=ax.transAxes)
ax = plt.axes([0.1, 0.43, 0.35, 0.17])
plot_us_phenocam(ax, turkey, [60, 129], "Turkey Point")
ax.text(0.05, 0.85, "(c) Turkey Point", transform=ax.transAxes)
ax = plt.axes([0.48, 0.02, 0.25, 0.2])
plot_images(ax, "barrow_2017_06_27_100002.jpg")
ax = plt.axes([0.48, 0.22, 0.25, 0.2])
plot_images(ax, "missouriozarks_2017_04_30_163113.jpg")
ax = plt.axes([0.48, 0.42, 0.25, 0.2])
plot_images(ax, "turkeypointenf39_2017_04_18_113106.jpg")
ax = plt.axes([0.73, 0.02, 0.25, 0.2])
plot_images(ax, "barrow_2018_06_27_103003.jpg")
ax = plt.axes([0.73, 0.22, 0.25, 0.2])
plot_images(ax, "missouriozarks_2018_04_30_163113.jpg")
ax = plt.axes([0.73, 0.42, 0.25, 0.2])
plot_images(ax, "turkeypointenf39_2018_04_18_113302.jpg")
plt.figtext(0.6, 0.02, "2017")
plt.figtext(0.85, 0.02, "2018")
#***********************
# UK timeseries - 2018
ax = plt.axes([0.1, 0.65, 0.85, 0.15])
oak = read_uk_oak_csv(
os.path.join(data_loc, "UK Oak budburst 2000-2018.csv"))
utils.plot_ts_panel(ax, [oak], "-", "phenological", loc="lower left")
ax.set_ylim([94, 124])
ax.text(0.03, 0.8, "(b) Oak Budburst", transform=ax.transAxes)
# fix the legend label to be italic
handles, labels = ax.get_legend_handles_labels()
labels[
0] = "$\mathregular{\mathit{Quercus}}$" + " " + "$\mathregular{\mathit{robur}}$"
ax.legend(handles, labels, frameon=False, loc="lower left")
# need legend once have other panel's data
north, south = read_windermere_csv(
os.path.join(data_loc, "Windermere_2000-2018.csv"))
ax2 = plt.axes([0.1, 0.8, 0.85, 0.15], sharex=ax)
utils.plot_ts_panel(ax2, [north, south],
"-",
"phenological",
loc="lower right")
ax2.set_ylim([79, 159])
plt.setp(ax2.get_xticklabels(), visible=False)
ax2.text(0.03, 0.8, "(a) Windermere", transform=ax2.transAxes)
ax2.set_xlim([1998, 2020])
ax.set_xlim([1998, 2020])
fig.text(0.03, 0.835, "Day of year", rotation="vertical")
plt.savefig(image_loc +
"PHEN_timeseries_{}{}".format(settings.YEAR, settings.OUTFMT))
plt.close()
return # run_all_plots
def run_all_plots():
if True:
# multipanel timeseries
COLOURS = settings.COLOURS["temperature"]
fig, (ax1, ax2, ax3, ax4, ax5, ax6) = plt.subplots(6,
figsize=(8, 19),
sharex=True)
# ERA5
era5_globe, era5_ocean, era5_land, era5tropics = utils.era5_ts_read(
settings.REANALYSISLOC, "sat", annual=True)
land_era5_clim, land_era5_anoms = utils.calculate_climatology_and_anomalies_1d(
era5_land, 1981, 2010)
ocean_era5_clim, ocean_era5_anoms = utils.calculate_climatology_and_anomalies_1d(
era5_ocean, 1981, 2010)
global_era5_clim, global_era5_anoms = utils.calculate_climatology_and_anomalies_1d(
era5_globe, 1981, 2010)
#*******************
# in situ L+O
noaa, nasa, jma = read_global_t(DATALOC +
"{}_LO.csv".format(IS_timeseries_root))
hadcrut = read_hadcrut_crutem(DATALOC + "hadcrut4.1981-2010.csv")
p0 = ax1.plot(noaa.times,
noaa.data,
c=COLOURS[noaa.name],
ls='-',
label=noaa.name,
lw=LW)
p1 = ax1.plot(nasa.times,
nasa.data,
c=COLOURS[nasa.name],
ls='-',
label=nasa.name,
lw=LW)
# p2 = ax1.plot(jma.times, jma.data, c=COLOURS[jma.name], ls='-', label=jma.name, lw=LW)
p3 = ax1.plot(hadcrut.times,
hadcrut.data,
c=COLOURS[hadcrut.name],
ls='-',
label=hadcrut.name,
lw=LW)
ax1.fill_between(hadcrut.times, hadcrut.lower, hadcrut.upper, \
where=hadcrut.upper > hadcrut.lower, color='0.5', alpha=0.7)
p4 = ax1.fill(np.NaN, np.NaN, '0.5', alpha=0.7)
ax1.axhline(0, c='0.5', ls='--')
ax1.legend([p0[0], p1[0], (p3[0], p4[0])], [noaa.name, nasa.name, hadcrut.name], \
loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX)
ax1.text(0.02,
0.9,
"(a) In Situ Land and Ocean",
transform=ax1.transAxes,
fontsize=settings.FONTSIZE)
utils.thicken_panel_border(ax1)
# ax1.yaxis.set_ticks_position('left')
#*******************
# reanalysis L+O
merra = utils.read_merra(
settings.REANALYSISLOC +
"MERRA-2_SfcAnom{}.dat".format(settings.YEAR), "temperature", "LO")
jra_actuals, jra_anoms = utils.read_jra55(
settings.REANALYSISLOC + "JRA-55_tmp2m_global_ts.txt",
"temperature")
twenty_cr_actuals = utils.read_20cr(
settings.REANALYSISLOC + "global.2mt.skt.txt", "temperature")
dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d(
twenty_cr_actuals, 1981, 2010)
twenty_cr_anoms.zorder = -1
# 2018 no MERRA
utils.plot_ts_panel(ax2, [jra_anoms, global_era5_anoms],
"-",
"temperature",
loc=LEGEND_LOC,
bbox=BBOX)
ax2.text(0.02,
0.9,
"(b) Reanalysis Land and Ocean",
transform=ax2.transAxes,
fontsize=settings.FONTSIZE)
#*******************
# in situ L
noaa, nasa, jma = read_global_t(DATALOC +
"{}_L.csv".format(IS_timeseries_root))
crutem = read_hadcrut_crutem(DATALOC +
"crutem4_new_logo.1981-2010.csv")
p0 = ax3.plot(noaa.times,
noaa.data,
ls='-',
c=COLOURS[noaa.name],
label=noaa.name,
lw=LW)
p1 = ax3.plot(nasa.times,
nasa.data,
ls='-',
c=COLOURS[nasa.name],
label=nasa.name,
lw=LW)
# p2 = ax3.plot(jma.times, jma.data, ls='-', c=COLOURS[jma.name], label=jma.name, lw=LW)
# p3 = ax3.plot(berkeley.times, berkeley.data, ls = '-', c = COLOURS[berkeley.name], label = berkeley.name, lw = LW)
p4 = ax3.plot(crutem.times,
crutem.data,
ls='-',
c=COLOURS[crutem.name],
label=crutem.name,
lw=LW)
ax3.fill_between(crutem.times, crutem.lower, crutem.upper, \
where=crutem.upper > crutem.lower, color='0.5', alpha=0.7)
p5 = ax1.fill(np.NaN, np.NaN, '0.5', alpha=0.7)
ax3.axhline(0, c='0.5', ls='--')
ax3.legend([p0[0], p1[0], (p4[0], p5[0])], [noaa.name, nasa.name, crutem.name], \
loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX)
ax3.text(0.02,
0.9,
"(c) In Situ Land only",
transform=ax3.transAxes,
fontsize=settings.FONTSIZE)
utils.thicken_panel_border(ax3)
# ax3.yaxis.set_ticks_position('left')
#*******************
# reanalysis L
merra = utils.read_merra(
settings.REANALYSISLOC +
"MERRA-2_SfcAnom{}.dat".format(settings.YEAR), "temperature", "L")
jra_actual, jra_anoms = utils.read_jra55(
settings.REANALYSISLOC + "JRA-55_tmp2m_globalland_ts.txt",
"temperature")
twenty_cr_actuals = utils.read_20cr(
settings.REANALYSISLOC + "air2mland.txt", "temperature")
dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d(
twenty_cr_actuals, 1981, 2010)
twenty_cr_anoms.zorder = -1
# 2018 - No MERRA
utils.plot_ts_panel(ax4, [jra_anoms, land_era5_anoms],
"-",
"temperature",
loc=LEGEND_LOC,
bbox=BBOX)
ax4.text(0.02,
0.9,
"(d) Reanalysis Land only",
transform=ax4.transAxes,
fontsize=settings.FONTSIZE)
#*******************
# in situ O
noaa, nasa, jma = read_global_t(DATALOC +
"{}_O.csv".format(IS_timeseries_root))
hadsst = read_hadcrut_crutem(DATALOC +
"hadsst3_new_logo.1981-2010.csv")
p0 = ax5.plot(noaa.times,
noaa.data,
ls='-',
c=COLOURS[noaa.name],
label=noaa.name,
lw=LW)
p1 = ax5.plot(nasa.times,
nasa.data,
ls='-',
c=COLOURS[nasa.name],
label=nasa.name,
lw=LW)
# p2 = ax5.plot(jma.times, jma.data, ls='-', c=COLOURS[jma.name], label=jma.name, lw=LW)
p3 = ax5.plot(hadsst.times,
hadsst.data,
ls='-',
c=COLOURS[hadsst.name],
label=hadsst.name,
lw=LW)
ax5.fill_between(hadsst.times, hadsst.lower, hadsst.upper, \
where=hadsst.upper > hadsst.lower, color='0.5', alpha=0.7)
p4 = ax1.fill(np.NaN, np.NaN, '0.5', alpha=0.7)
ax5.axhline(0, c='0.5', ls='--')
ax5.legend([p0[0], p1[0], (p3[0], p4[0])], [noaa.name, nasa.name, hadsst.name], \
loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX)
ax5.text(0.02,
0.9,
"(e) In Situ Ocean only",
transform=ax5.transAxes,
fontsize=settings.FONTSIZE)
utils.thicken_panel_border(ax5)
# ax5.yaxis.set_ticks_position('left')
#*******************
# reanalysis O
merra = utils.read_merra(
settings.REANALYSISLOC +
"MERRA-2_SfcAnom{}.dat".format(settings.YEAR), "temperature", "O")
jra_actual, jra_anoms = utils.read_jra55(
settings.REANALYSISLOC + "JRA-55_tmp2m_globalocean_ts.txt",
"temperature")
twenty_cr_actuals = utils.read_20cr(
settings.REANALYSISLOC + "airsktocean.txt", "temperature")
dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d(
twenty_cr_actuals, 1981, 2010)
twenty_cr_anoms.zorder = -1
# 2018 no MERRA
utils.plot_ts_panel(ax6, [jra_anoms, ocean_era5_anoms],
"-",
"temperature",
loc=LEGEND_LOC,
bbox=BBOX)
ax6.text(0.02,
0.9,
"(f) Reanalysis Ocean only",
transform=ax6.transAxes,
fontsize=settings.FONTSIZE)
#*******************
# prettify
fig.text(0.03,
0.5,
"Anomalies (" + r'$^{\circ}$' + "C)",
va='center',
rotation='vertical',
fontsize=settings.FONTSIZE)
plt.xlim([1900, int(settings.YEAR) + 2])
minorLocator = MultipleLocator(5)
for ax in [ax1, ax2, ax3, ax4, ax5, ax6]:
ax.set_ylim(YLIM)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.xaxis.set_minor_locator(minorLocator)
for tick in ax6.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
fig.subplots_adjust(right=0.96, top=0.995, bottom=0.02, hspace=0.001)
plt.savefig(settings.IMAGELOC + "SAT_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# ERA5 Anomaly figure
if True:
# Read in ERA anomalies
cube_list = iris.load(settings.REANALYSISLOC +
"era5_t2m_{}01-{}12_ann_ano.nc".format(
settings.YEAR, settings.YEAR))
for cube in cube_list:
if cube.var_name == "T2M":
break
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "SAT_{}_anoms_era5".format(settings.YEAR),
cube[0],
settings.COLOURMAP_DICT["temperature"],
bounds,
"Anomalies from 1981-2010 (" + r'$^{\circ}$' + "C)",
title="ERA5")
#************************************************************************
# MERRA2 Anomaly figure
if True:
cube_list = iris.load(settings.REANALYSISLOC +
"MERRA-2_SfcAnom_{}.nc".format(settings.YEAR))
for cube in cube_list:
if cube.var_name == "t2ma":
break
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_merra".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["temperature"], bounds, \
"Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="MERRA-2")
#************************************************************************
# HadCRUT4 Anomaly figure
if True:
cube_list = iris.load(DATALOC + "HadCRUT.4.6.0.0.median.nc")
for cube in cube_list:
if cube.var_name == "temperature_anomaly":
break
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
# restrict to 1851 to last full year
date_constraint = utils.periodConstraint(
cube, dt.datetime(1850, 1, 1),
dt.datetime(int(settings.YEAR) + 1, 1, 1))
cube = cube.extract(date_constraint)
# convert to 1981-2010 climatology.
clim_constraint = utils.periodConstraint(cube, dt.datetime(1981, 1, 1),
dt.datetime(2011, 1, 1))
clim_cube = cube.extract(clim_constraint)
clim_data = clim_cube.data.reshape(-1, 12, clim_cube.data.shape[-2],
clim_cube.data.shape[-1])
# more than 15 years present
climatology = np.ma.mean(clim_data, axis=0)
nyears = np.ma.count(clim_data, axis=0)
climatology = np.ma.masked_where(nyears <= 15,
climatology) # Kate keeps GT 15.
# extract final year
final_year_constraint = utils.periodConstraint(
cube, dt.datetime(int(settings.YEAR), 1, 1),
dt.datetime(int(settings.YEAR) + 1, 1, 1))
final_year_cube = cube.extract(final_year_constraint)
final_year_cube.data = final_year_cube.data - climatology
# more than 6 months present
annual_cube = final_year_cube.collapsed(['time'], iris.analysis.MEAN)
nmonths = np.ma.count(final_year_cube.data, axis=0)
annual_cube.data = np.ma.masked_where(nmonths <= 6, annual_cube.data)
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_hadcrut4".format(settings.YEAR), annual_cube, \
settings.COLOURMAP_DICT["temperature"], bounds, \
"Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="HadCRUT 4.6")
#************************************************************************
# NOAA data Anomaly figure - incl plate 2.1
if True:
cube = read_noaa_mlost(DATALOC + "mlost-box.ytd.12.1981-2010bp.txt",
int(settings.YEAR))
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_SAT_{}_anoms_noaa".format(settings.YEAR), cube, \
settings.COLOURMAP_DICT["temperature"], bounds, \
"Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", \
figtext="(a) Surface Temperature", \
save_netcdf_filename="{}MLOST_for_NOAA_{}.nc".format(DATALOC, dt.datetime.strftime(dt.datetime.now(), "%d-%b-%Y")))
utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_noaa".format(settings.YEAR), cube, \
settings.COLOURMAP_DICT["temperature"], bounds, \
"Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="NOAAGlobalTemp")
#************************************************************************
# JRA55 data Anomaly figure
if True:
cube_list = iris.load(settings.REANALYSISLOC +
"jra55_t2m_{}01-{}12_ann_ano.nc".format(
settings.YEAR, settings.YEAR))
for cube in cube_list:
if cube.var_name == "T2M":
break
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_jra55".format(settings.YEAR), cube[0,0], \
settings.COLOURMAP_DICT["temperature"], bounds, \
"Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="JRA-55")
#************************************************************************
# NASA GISS Anomaly figure
if True:
#cube = read_nasa_giss(DATALOC + "nasa-2015-anomalies-wrt1981-2010bp")
cube = iris.load(DATALOC + "gistemp1200_GHCNv4_ERSSTv5.nc")[0]
# convert to 1981-2010 climatology.
clim_constraint = utils.periodConstraint(cube, dt.datetime(1981, 1, 1),
dt.datetime(2011, 1, 1))
clim_cube = cube.extract(clim_constraint)
clim_data = clim_cube.data.reshape(-1, 12, clim_cube.data.shape[-2],
clim_cube.data.shape[-1])
# more than 15 years present
climatology = np.ma.mean(clim_data, axis=0)
nyears = np.ma.count(clim_data, axis=0)
climatology = np.ma.masked_where(nyears <= 15,
climatology) # Kate keeps GT 15.
# extract final year
final_year_constraint = utils.periodConstraint(cube, dt.datetime(int(settings.YEAR), 1, 1), \
dt.datetime(int(settings.YEAR)+1, 1, 1))
final_year_cube = cube.extract(final_year_constraint)
final_year_cube.data = final_year_cube.data - climatology
# more than 6 months present
annual_cube = final_year_cube.collapsed(['time'], iris.analysis.MEAN)
nmonths = np.ma.count(final_year_cube.data, axis=0)
annual_cube.data = np.ma.masked_where(nmonths <= 6, annual_cube.data)
bounds = [-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "SAT_{}_anoms_nasa".format(settings.YEAR), annual_cube, \
settings.COLOURMAP_DICT["temperature"], bounds, \
"Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="NASA GISS")
return # run_all_plots
# D - SURFACE TEMPERATURE
print("Surface Air Temperature")
import sat
# in situ
noaa, nasa, jma = sat.read_global_t(DATALOC + "{}/{}_LO.csv".format("SAT", sat.IS_timeseries_root))
hadcrut = sat.read_hadcrut_crutem(DATALOC+"{}/hadcrut4.1981-2010.csv".format("SAT"))
make_plot(ax_d, [noaa, nasa, jma, hadcrut], insitu)
# reanalyses
merra = utils.read_merra(settings.REANALYSISLOC + "MERRA-2_SfcAnom{}.dat".format(settings.YEAR), "temperature", "LO")
jra_actuals, jra_anoms = utils.read_jra55(settings.REANALYSISLOC + "JRA-55_tmp2m_global_ts.txt", "temperature")
era5_globe, era5_ocean, era5_land, era5tropics = utils.era5_ts_read(settings.REANALYSISLOC, "sat", annual=True)
global_era5_clim, global_era5_anoms = utils.calculate_climatology_and_anomalies_1d(era5_globe, 1981, 2010)
make_plot(ax_d, [merra, jra_anoms, era5_globe], reanalyses, plot_label="(d) Surface Temperature\n(1981-2010)", ylabel='$^{\circ}$'+"C", ylim=[-0.9, 1.2])
#***************************
# E - LOWER TROP TEMPERATURE
print("Lower Tropospheric Temperature")
import ltt
raobcore, rich, ratpac, UAH, rss, era5, jra, merra= ltt.read_csv(DATALOC + "{}/SotC_AnnTemps_2020_0220_LTTGL.csv".format("LTT"))
# in situ
make_plot(ax_e, [raobcore, rich, ratpac], insitu)
# satellite
make_plot(ax_e, [UAH, rss], satellite)
def run_all_plots():
#************************************************************************
# Read in Australian # Data
# aus_lat, aus_lon, aus_anom_8110, aus_trend_79_pres, aus_years, aus_anomalies, aus_clim_8110 = \
# read_australia("{}/u_Extracted_ANN_{}_v2.csv".format(DATALOC, settings.YEAR), start_year = 1979)
# # read_australia("{}/u_Extracted_ANN_1974_{}_RD.csv".format(DATALOC, settings.YEAR))
# Timeseries figure
if True:
# ERA data
era5_globe, era5_ocean, era5_land, era5tropics = utils.era5_ts_read(
settings.REANALYSISLOC, "wnd", annual=True)
land_era5_clim, land_era5_anoms = utils.calculate_climatology_and_anomalies_1d(
era5_land, CLIMSTART, 2010)
land_merra_anoms = utils.read_merra(settings.REANALYSISLOC + "MERRA-2_SfcAnom{}.dat".format(settings.YEAR), \
"wind", "L", anomalies=True)
jra_actuals, jra_anoms = utils.read_jra55(
settings.REANALYSISLOC + "JRA-55_ws10m_globalland_ts.txt",
"windspeed")
twenty_cr_actuals = utils.read_20cr(
settings.REANALYSISLOC + "wspd10m.land.txt", "wind speed")
dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d(
twenty_cr_actuals, CLIMSTART, 2010)
# Plot timeseries figure
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4,
figsize=(8, 13),
sharex=True)
print("{} {} {} {} {}".format("name", "mean", "anomaly", "trend/dec",
"N station"))
for region in all_regions:
if region.name == "Australia":
# use the Australian data
years = aus_years
anomalies = aus_anomalies
print(anomalies)
region.nstat = len(aus_lat)
region.slope = np.mean(
aus_trend_79_pres
) # just do the mean of all station slopes
region.mean = np.mean(aus_clim_8110)
else:
# Read in the HadISD annual anomalies
years, anomalies, m3, m10 = read_hadisd_annual_anomalies(
region)
# Print data for table
print("{} {} {} {} {}".format(region.name, region.mean,
anomalies[-1], region.slope * 10.,
region.nstat))
order = anomalies.argsort()
ranks = order.argsort() + 1
print("{} highest {} lowest".format(
len(ranks) - ranks[-1], ranks[-1]))
# plot data
if region.fname == "GlobalNoOz":
ax1.plot(years,
anomalies,
c=region.color,
label=region.name,
lw=3,
zorder=10)
else:
ax1.plot(years,
anomalies,
c=region.color,
label=region.name,
lw=2)
if region.fname == "GlobalNoOz":
ax3.plot(years, m3, c=region.color, lw=3, zorder=10)
ax4.plot(years, m10, c=region.color, lw=3)
elif region.name != "Australia":
ax3.plot(years, m3, c=region.color, lw=2)
ax4.plot(years, m10, c=region.color, lw=2)
# plot reanalyses separately
ax2.plot(land_era5_anoms.times, land_era5_anoms.data, c=settings.COLOURS["circulation"]["ERA5"], \
label="ERA5 (land only)", lw=2)
ax2.plot(land_merra_anoms.times, land_merra_anoms.data, c=settings.COLOURS["circulation"]["MERRA-2"], \
label="MERRA-2 (land only)", lw=2)
# ax2.plot(jra_anoms.times, jra_anoms.data, c=settings.COLOURS["circulation"]["JRA-55"], \
# label="JRA-55 (land only)", lw=2)
ax2.plot(twenty_cr_anoms.times, twenty_cr_anoms.data, c=settings.COLOURS["circulation"]["20CRv3"], \
label="20CRv3 (land only)", lw=2)
# finish off plot
ax1.axhline(0, c='0.5', ls='--')
ax2.axhline(0, c='0.5', ls='--')
ax1.text(0.02,
0.9,
"(a) In Situ - all Speeds",
transform=ax1.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02,
0.9,
"(b) Reanalyses - all Speeds",
transform=ax2.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax3.text(0.02, 0.9, "(c) In Situ >3 m s"+r'$^{-1}$'+" Winds", transform=ax3.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
ax4.text(0.02, 0.9, "(d) In Situ >10 m s"+r'$^{-1}$'+" Winds", transform=ax4.transAxes, \
fontsize=settings.LABEL_FONTSIZE)
fig.text(0.02, 0.72, "Wind Anomaly (m s"+r'$^{-1}$'+")", va='center', rotation='vertical', \
fontsize=settings.LABEL_FONTSIZE)
fig.text(0.02, 0.3, "Wind Frequency (% yr"+r'$^{-1}$'+")", va='center', rotation='vertical', \
fontsize=settings.LABEL_FONTSIZE)
ax1.legend(loc="upper right", ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=(1.0, 0.9))
ax2.legend(loc="upper right", ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=(1.0, 0.9))
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
fig.subplots_adjust(right=0.96, top=0.99, bottom=0.03, hspace=0.001)
for tick in ax4.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
plt.xlim([1970, int(settings.YEAR) + 1])
ax1.set_ylim([-0.39, 1.0])
ax2.set_ylim([-0.39, 1.0])
ax3.set_ylim([23, 68])
ax4.set_ylim([0, 6.5])
minorLocator = MultipleLocator(1)
for ax in [ax1, ax2, ax3, ax4]:
utils.thicken_panel_border(ax)
ax.set_yticks(ax.get_yticks()[1:])
ax.xaxis.set_minor_locator(minorLocator)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.yaxis.set_ticks_position('left')
plt.savefig(settings.IMAGELOC +
"WND_land_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# HadISD Anomaly figure
if True:
# Read in HadISD station anomalies
stn_id, hadisd_lons, hadisd_lats, mean8110, hadisd_anomaly_8110, hadisd_trend_79_pres = read_hadisd_global_summary(
)
# combine together
lats = hadisd_lats
lons = hadisd_lons
anom = hadisd_anomaly_8110
trend = hadisd_trend_79_pres * 10
# lats = np.append(hadisd_lats, aus_lat)
# lons = np.append(hadisd_lons, aus_lon)
# anom = combine_arrays((hadisd_anomaly_8110, aus_anom_8110))
# trend = combine_arrays((hadisd_trend_79_pres, aus_trend_79_pres)) * 10.
# bounds = [-100, -0.8, -0.4, -0.2, -0.1, 0, 0.1, 0.2, 0.4, 0.8, 100]
bounds = [-100, -0.4, -0.2, -0.1, -0.05, 0, 0.05, 0.1, 0.2, 0.4, 100]
utils.scatter_plot_map(settings.IMAGELOC + "WND_{}_obs_trend".format(settings.YEAR), trend, \
lons, lats, settings.COLOURMAP_DICT["circulation_r"], bounds, "Trend from {}-{} (m s".format(TRENDSTART, settings.YEAR)+r'$^{-1}$'+" decade"+r'$^{-1}$)')
bounds = [-100, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 100]
utils.scatter_plot_map(settings.IMAGELOC + "WND_{}_obs_anomaly".format(settings.YEAR), anom, \
lons, lats, settings.COLOURMAP_DICT["circulation_r"], bounds, "Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)')
total = float(len(anom.compressed()))
pos, = np.ma.where(anom > 0)
neg, = np.ma.where(anom < 0)
print("Anomalies: positive {:5.3f} negative {:5.3f}".format(
len(pos) / total,
len(neg) / total))
pos, = np.ma.where(anom > 0.5)
neg, = np.ma.where(anom < -0.5)
print("Anomalies: positive {:5.3f} negative {:5.3f} (than 0.5)".format(
len(pos) / total,
len(neg) / total))
pos, = np.ma.where(anom > 1.0)
neg, = np.ma.where(anom < -1.0)
print("Anomalies: positive {:5.3f} negative {:5.3f} (than 1.0)".format(
len(pos) / total,
len(neg) / total))
total = float(len(trend.compressed()))
pos, = np.ma.where(trend > 0)
neg, = np.ma.where(trend < 0)
print("Trends: positive {:5.3f} negative {:5.3f}".format(
len(pos) / total,
len(neg) / total))
#************************************************************************
# ERA5 + HadISD Anomaly figure
if True:
# Read in ERA anomalies
cube_list = iris.load(settings.REANALYSISLOC +
"era5_ws10_{}01-{}12_ann_ano.nc".format(
settings.YEAR, settings.YEAR))
cube = cube_list[0]
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [-4, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 4]
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_era5_obs_anomaly".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["circulation_r"], bounds, "Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)', scatter = (lons, lats, anom))
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_era5_anomaly".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["circulation_r"], bounds, "Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)')
#************************************************************************
# MERRA Anomaly figure
if True:
anoms = read_ocean_ncdf(
DATALOC +
"rss_wind_trend_anomaly_SOTC_{}_updated.nc".format(settings.YEAR),
"MERRA2_wind_anomaly_map_{}".format(settings.YEAR))
bounds = [-40, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 40]
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_merra2_anomaly", anoms, settings.COLOURMAP_DICT["circulation_r"], bounds,\
"Anomalies from 1981-2010 (m s"+r'$^{-1}$'+")")
#************************************************************************
# MERRA + HadISD Anomaly figure
if True:
# Read in MERRA anomalies
cube_list = iris.load(
settings.REANALYSISLOC +
"MERRA-2_SfcAnom_{}.nc".format(settings.YEAR),
"10m Wind Speed Anomaly (1981-2010)")
cube = cube_list[0]
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
bounds = [-4, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 4]
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra_obs_anomaly".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)', scatter=(lons, lats, anom))
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_WND_{}_merra_obs_anomaly".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)', figtext="(v) Surface Winds", scatter=(lons, lats, anom))
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra_anomaly".format(settings.YEAR), cube[0], \
settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Anomalies from {}-2010 (m s".format(CLIMSTART)+r'$^{-1}$)')
#************************************************************************
# MERRA/RSS ocean + HadISD Anomaly figure
if True:
# Read in MERRA/RSS trends
anomalies = read_ocean_ncdf(
DATALOC +
"rss_wind_trend_anomaly_SOTC_{}_updated.nc".format(settings.YEAR),
"Merged_wind_anomaly_map_{}".format(settings.YEAR))
bounds = [-4, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 4]
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra-rss_anomaly".format(settings.YEAR), \
anomalies, settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Anomalies from 1981-2010 (m s"+r'$^{-1}$)')
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra-rss_obs_anomaly".format(settings.YEAR), \
anomalies, settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Anomalies from 1981-2010 (m s"+r'$^{-1}$)',
scatter=(lons, lats, anom))
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_WND_{}_merra-rss_obs_anomaly".format(settings.YEAR), \
anomalies, settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Anomalies from 1981-2010 (m s"+r'$^{-1}$)', figtext="(v) Surface Winds", scatter=(lons, lats, anom))
#************************************************************************
# Ocean timeseries
if True:
satellite = read_ts_cube(
DATALOC +
"rss_wind_trend_anomaly_SOTC_{}.nc".format(settings.YEAR),
"RSS_wind_global_annual_anom_ts", "Satellite MW Radiometers")
satellite_clim, satellite_anom = utils.calculate_climatology_and_anomalies_1d(
satellite, 1988, 2010)
# print("NO IN SITU OCEAN DATA FOR 2016, using 2015 data")
# nocs = read_ts_cube(DATALOC + "NOCSv2.0_oceanW_5by5_8110anoms_areaTS_FEB2016.nc", "Globally Average 70S-70N", "NOCSv2.0")
# WASwind = read_ts_cube(DATALOC + "waswind_v1_0_1.monthly_areaTS_19502011.nc","Globally Averaged Anomalies 70S-70N", "WASwind")
# print("FIXING WASWIND TIMES - DATAFILE HAS WRONG DESCRIPTOR")
# WASwind.times = WASwind.times - (1973-1950)
jra_actuals, jra_anoms = utils.read_jra55(
settings.REANALYSISLOC + "JRA-55_ws10m_globalocean_ts.txt", "wind")
era5_globe, era5_ocean, era5_land, era5tropics = utils.era5_ts_read(
settings.REANALYSISLOC, "wnd", annual=True)
ocean_era5_clim, ocean_era5_anoms = utils.calculate_climatology_and_anomalies_1d(
era5_ocean, 1988, 2010)
merra_anoms = utils.read_merra(
settings.REANALYSISLOC +
"MERRA-2_SfcAnom{}.dat".format(settings.YEAR),
"wind",
"O",
anomalies=True)
twenty_cr_actuals = utils.read_20cr(
settings.REANALYSISLOC + "wspd10m.ocean.txt", "wind speed")
dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d(
twenty_cr_actuals, 1988, 2010)
fig, (ax1) = plt.subplots(1, figsize=(8, 5), sharex=True)
# Satellite
# utils.plot_ts_panel(ax1, [satellite_anom], "-", "circulation", loc=LEGEND_LOC, bbox=BBOX)
# In Situ
# utils.plot_ts_panel(ax2, [nocs, WASwind], "-", "circulation", loc=LEGEND_LOC, bbox=BBOX)
# ax2.set_ylabel("Anomaly (m s"+r'$^{-1}$'+")", fontsize = settings.FONTSIZE)
# Reanalyses & Satellite single panel
satellite_anom.lw = 4
satellite_anom.zorder = 10
utils.plot_ts_panel(
ax1,
[satellite_anom, ocean_era5_anoms, merra_anoms, twenty_cr_anoms],
"-",
"circulation",
loc=LEGEND_LOC,
bbox=BBOX)
#*******************
# prettify
ax1.axhline(0, c='0.5', ls='--')
plt.ylabel("Wind Anomaly (m s" + r'$^{-1}$' + ")",
fontsize=settings.LABEL_FONTSIZE)
ax1.legend(loc="upper right", ncol=1, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=(1.0, 0.99))
# sort formatting
plt.xlim([1970, int(settings.YEAR) + 1])
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# for ax in [ax1, ax2, ax3]:
for ax in [ax1]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.set_ylim([-0.28, 0.45])
ax.yaxis.set_ticks([-0.2, 0.0, 0.2, 0.4])
ax.yaxis.set_ticks_position('left')
# sort labelling
ax1.text(0.03,
0.9,
"Satellites & Reanalyses",
transform=ax1.transAxes,
fontsize=settings.LABEL_FONTSIZE)
# ax2.text(0.03, 0.9, "(b) In Situ", transform=ax2.transAxes, fontsize=settings.LABEL_FONTSIZE)
# ax3.text(0.03, 0.9, "(b) Reanalyses", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE
fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001)
plt.savefig(settings.IMAGELOC +
"WND_ocean_ts{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# Ocean maps
if True:
anoms = read_ocean_ncdf(
DATALOC +
"rss_wind_trend_anomaly_SOTC_{}_updated.nc".format(settings.YEAR),
"RSS_wind_anomaly_map_{}".format(settings.YEAR))
bounds = [-40, -1.2, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 1.2, 40]
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_rss_anomaly".format(settings.YEAR), anoms, settings.COLOURMAP_DICT["circulation_r"], bounds,\
"Anomalies from 1981-2010 (m s"+r'$^{-1}$'+")")
#************************************************************************
# MERRA/RSS ocean + HadISD Trend figure
if True:
# Read in MERRA/RSS trends
trends = read_ocean_ncdf(
DATALOC +
"rss_wind_trend_anomaly_SOTC_{}_updated.nc".format(settings.YEAR),
"Wind_trend_map")
bounds = [-4, -0.8, -0.4, -0.2, -0.1, 0, 0.1, 0.2, 0.4, 0.8, 4]
bounds = [-100, -0.4, -0.2, -0.1, -0.05, 0, 0.05, 0.1, 0.2, 0.4, 100]
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra-rss_trend".format(settings.YEAR), \
trends, settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Trend from {}-{} (m s".format(TRENDSTART, settings.YEAR)+r'$^{-1}$'+" decade"+r'$^{-1}$)')
utils.plot_smooth_map_iris(settings.IMAGELOC + "WND_{}_merra-rss_obs_trend".format(settings.YEAR), \
trends, settings.COLOURMAP_DICT["circulation_r"], bounds, \
"Trend from {}-{} (m s".format(TRENDSTART, settings.YEAR)+r'$^{-1}$'+" decade"+r'$^{-1}$)',
scatter=(lons, lats, trend))
return # run_all_plots
def run_all_plots():
#************************************************************************
# Timeseries figure (2 panels)
if True:
for region in ["global"]:
if region == "global":
raobcore, rich, ratpac, UAH, rss, era5, merra, jra = \
read_csv(DATALOC + "SotC_AnnTemps_2020_0520_LTTGL.csv")
plt.clf()
fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 10), sharex=True)
# sondes
utils.plot_ts_panel(ax1, [raobcore, rich, ratpac], "-", "temperature", loc=LEGEND_LOC)
# satellites
utils.plot_ts_panel(ax2, [UAH, rss], "-", "temperature", loc=LEGEND_LOC)
ax2.set_ylabel("Anomaly ("+r'$^\circ$'+"C)", fontsize=settings.FONTSIZE)
# reanalyses
if region == "global":
# jra_actuals, jra_anoms = utils.read_jra55(settings.REANALYSISLOC + "JRA-55_MSUch2LT_global_ts.txt", "temperature")
# merra_actuals, merra_anoms = utils.read_merra_LT_LS(settings.REANALYSISLOC + "MERRA2_MSU_Tanom_ann_{}.dat".format(settings.YEAR), LT=True)
# utils.plot_ts_panel(ax3, [erai, era5, jra_anoms, merra_anoms], "-", "temperature", loc=LEGEND_LOC)
twenty_cr_actuals = utils.read_20cr(settings.REANALYSISLOC + "tlt.global.txt", "temperature")
dummy, twenty_cr_anoms = utils.calculate_climatology_and_anomalies_1d(twenty_cr_actuals, 1981, 2010)
utils.plot_ts_panel(ax3, [era5, jra, merra], "-", "temperature", loc=LEGEND_LOC)
else:
utils.plot_ts_panel(ax3, [era5], "-", "temperature", loc=LEGEND_LOC)
# sort formatting
plt.xlim([raobcore.times[0]-1, raobcore.times[-1]+1])
ax1.set_ylim([-0.89, 0.89])
ax2.set_ylim([-0.89, 0.89])
ax3.set_ylim([-0.89, 0.89])
for tick in ax3.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax2.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax3.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# sort labelling
ax1.text(0.02, 0.9, "(a) Radiosondes", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02, 0.9, "(b) Satellites", transform=ax2.transAxes, fontsize=settings.LABEL_FONTSIZE)
ax3.text(0.02, 0.9, "(c) Reanalyses", transform=ax3.transAxes, fontsize=settings.LABEL_FONTSIZE)
fig.subplots_adjust(right=0.98, top=0.98, bottom=0.05, hspace=0.001)
plt.savefig(settings.IMAGELOC+"LTT_ts_{}{}".format(region, settings.OUTFMT))
plt.close()
# #************************************************************************
# # Land Fraction - 2018
# high, low = read_hilo(DATALOC + "high_low.dat")
# fig, ax1 = plt.subplots(1, figsize=(8, 5))
# ax1.plot(high.times, high.data, c="r", ls="-", lw=2, label=high.name)
# ax1.plot(low.times, low.data, c="b", ls="-", lw=2, label=low.name)
# ax1.legend(loc="upper right", ncol=2, frameon=False, prop={'size':settings.FONTSIZE})
# ax1.set_xlim([1978, int(settings.YEAR)+2])
# ax1.set_ylabel("Percentage of Global Area (%)", fontsize=settings.FONTSIZE)
# for tick in ax1.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# for tick in ax1.xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# utils.thicken_panel_border(ax1)
# plt.savefig(settings.IMAGELOC+"LTT_land_area_ts{}".format(settings.OUTFMT))
# plt.close()
#************************************************************************
# Read in ERA5 anomalies
if False:
cube_list = iris.load(DATALOC + "2019TLTAnom.nc")
names = np.array([c.var_name for c in cube_list])
loc, = np.where(names == "tltAnnualAnom")[0]
cube = cube_list[loc]
bounds = np.array([-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100])
bounds = np.array([-100, -1.6, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.6, 100])
bounds = np.array([-100, -2.0, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2.0, 100])
cmap = settings.COLOURMAP_DICT["temperature"]
utils.plot_smooth_map_iris(settings.IMAGELOC + "LTT_{}_anoms_era5".format(settings.YEAR), cube, cmap, \
bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="ERA5")
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_LTT_{}_anoms_era5".format(settings.YEAR), cube, \
cmap, bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", \
figtext="(e) Lower Tropospheric Temperature")
#************************************************************************
# Read in ERA5 anomalies with record pixels
if True:
cube_list = iris.load(DATALOC + "2019TLTAnom_warmest.nc")
names = np.array([c.var_name for c in cube_list])
loc, = np.where(names == "tltAnnualAnom")[0]
cube = cube_list[loc]
loc, = np.where(names == "recordMask")[0]
record = cube_list[loc]
record.coord("latitude").guess_bounds()
record.coord("longitude").guess_bounds()
lats, lons, data = [], [], []
for t, lat in enumerate(record.coord("latitude").points):
for n, lon in enumerate(record.coord("longitude").points):
if record.data[t, n] == 1:
data += [cube.data[t, n]]
lats += [np.mean(record.coord("latitude").bounds[t])]
lons += [np.mean(record.coord("longitude").bounds[n])]
bounds = np.array([-100, -2.0, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2.0, 100])
cmap = settings.COLOURMAP_DICT["temperature"]
utils.plot_smooth_map_iris(settings.IMAGELOC + "LTT_{}_anoms_era5".format(settings.YEAR), cube, cmap, \
bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", title="ERA5", \
scatter=(lons, lats, data), smarker="dots")
utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_LTT_{}_anoms_era5".format(settings.YEAR), cube, \
cmap, bounds, "Anomalies from 1981-2010 ("+r'$^{\circ}$'+"C)", \
figtext="(e) Lower Tropospheric Temperature", \
scatter=(lons, lats, data), smarker="dots")
#************************************************************************
# ERA-I Hovmuller
# times, latitudes, data = utils.erai_2dts_read(settings.REANALYSISLOC, "ltt")
# bounds = np.array([-100, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 100])
# bounds = np.array([-100, -1.6, -1.2, -0.8, -0.4, 0, 0.4, 0.8, 1.2, 1.6, 100])
# # sort the time axis
# start = (times[0] - 101)/10000.
# end = (times[-1] - 1201)/10000.
# year = start
# new_times = []
# months = (np.arange(12))/12.
# while year <= end:
# new_times += [np.array([year for i in range(12)]) + months]
# year += 1
# new_times = np.array(new_times).reshape(-1)
# # sort the climatology to 1981-2010 (monthly!)
# # reshape to deal with monthly
# data = data.reshape(-1, 12, data.shape[-1])
# new_times = new_times.reshape(-1, 12)
# # extract climatology period
# start_loc, = np.where(new_times[:, 0] == CLIMSTART)
# end_loc, = np.where(new_times[:, 0] == CLIMEND)
# clim_data = data[start_loc[0]:end_loc[0] + 1, :, :]
# climatology = np.mean(clim_data, axis=0)
# # make anomalies
# data = np.array([data[i, :, :] - climatology for i in range(data.shape[0])])
# # return to original shapes
# data = data.reshape(-1, data.shape[-1])
# new_times = new_times.reshape(-1)
# utils.plot_hovmuller(settings.IMAGELOC + "LTT_hovmuller_erai", new_times, latitudes, data.T, \
# settings.COLOURMAP_DICT["temperature"], bounds, \
# "Anomaly ("+r'$^{\circ}$'+"C)", cosine=True)
# version with MEI on top
# mei = read_mei(os.path.join(DATALOC, "MEI.dat"))
# utils.plot_hovmuller(settings.IMAGELOC + "LTT_hovmuller_era_MEI", new_times, latitudes, data.T, \
# settings.COLOURMAP_DICT["temperature"], bounds, \
# "Anomaly ("+r'$^{\circ}$'+"C)", cosine=True, extra_ts=mei)
return # run_all_plots
def run_all_plots():
#***********************
# MODIS - centre
if True:
cubelist = iris.load(
os.path.join(
DATALOC,
"MODIS.CMG.{}.SOS.EOS.Anomaly.nc".format(settings.YEAR)))
names = np.array([c.name() for c in cubelist])
# set up plot settings
BOUNDS = [-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100]
LABELS = {
"SOS": "(c) Start of Season (SOS)",
"EOS": "(d) End of Season (EOS)"
}
for season in ["SOS", "EOS"]:
c, = np.where(names == season)[0]
cube = cubelist[c]
# deal with NANS
cube.data = np.ma.masked_where(cube.data != cube.data, cube.data)
fig = plt.figure(figsize=(8, 11))
plt.clf()
# boundary circle
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
# axes for polar plot
ax = plt.axes([0.01, 0.02, 0.98, 0.65],
projection=cartopy.crs.NorthPolarStereo(
central_longitude=300.0))
plot_cube = cube
# regrid depending on output format
if settings.OUTFMT in [".eps", ".pdf"]:
if plot_cube.coord(
"latitude").points.shape[0] > 90 or plot_cube.coord(
"longitude").points.shape[0] > 360:
regrid_size = 1.0
print(
"Regridding cube for {} output to {} degree resolution"
.format(settings.OUTFMT, regrid_size))
print("Old Shape {}".format(plot_cube.data.shape))
plot_cube = utils.regrid_cube(plot_cube, regrid_size,
regrid_size)
print("New Shape {}".format(plot_cube.data.shape))
# prettify
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines()
ax.set_boundary(circle, transform=ax.transAxes)
if season == "SOS":
cmap = settings.COLOURMAP_DICT["phenological_r"]
elif season == "EOS":
cmap = settings.COLOURMAP_DICT["phenological"]
norm = mpl.cm.colors.BoundaryNorm(BOUNDS, cmap.N)
mesh = iris.plot.pcolormesh(plot_cube,
cmap=cmap,
norm=norm,
axes=ax)
# # read in sites
if season == "EOS":
pass
elif season == "SOS":
lake_locations = read_us_phenocam(
os.path.join(DATALOC, "lake_coords.csv"))
# scatter
COL = "chartreuse"
ax.scatter(lake_locations[1],
lake_locations[0],
c=COL,
s=100,
edgecolor="k",
transform=cartopy.crs.Geodetic(),
zorder=10)
COL = "m"
# Harvard Forest - 2019
ax.scatter(-72.17,
42.54,
c=COL,
s=100,
edgecolor="k",
transform=cartopy.crs.Geodetic(),
zorder=10)
# uk box
COL = "y"
region = [-10.0, 49.0, 3.0, 60.0]
ax.plot([region[0], region[0]], [region[1], region[3]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.PlateCarree())
ax.plot([region[2], region[2]], [region[1], region[3]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.PlateCarree())
ax.plot([region[0], region[2]], [region[1], region[1]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.Geodetic())
ax.plot([region[0], region[2]], [region[3], region[3]],
c=COL,
ls='-',
lw=4,
zorder=10,
transform=cartopy.crs.Geodetic())
# COL = "k"
# ax.plot([region[0], region[0]], [region[1], region[3]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.PlateCarree())
# ax.plot([region[2], region[2]], [region[1], region[3]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.PlateCarree())
# ax.plot([region[0], region[2]], [region[1], region[1]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.Geodetic())
# ax.plot([region[0], region[2]], [region[3], region[3]], c=COL, ls='-', lw=5, zorder=9, transform=cartopy.crs.Geodetic())
# label axes
ax.text(-0.1,
1.0,
LABELS[season],
fontsize=settings.FONTSIZE,
transform=ax.transAxes)
cb = plt.colorbar(mesh,
orientation='horizontal',
ticks=BOUNDS[1:-1],
drawedges=True,
fraction=0.1,
pad=0.01,
aspect=20,
shrink=0.8)
# prettify
cb.set_label(label="Anomaly (days)", fontsize=settings.FONTSIZE)
cb.ax.tick_params(axis='x',
labelsize=settings.FONTSIZE,
direction='in',
size=0)
cb.set_ticklabels(["{:g}".format(b) for b in BOUNDS[1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
ax.set_extent([-180, 180, 30, 90], cartopy.crs.PlateCarree())
for lat in range(30, 100, 10):
ax.text(180,
lat,
'{}$^\circ$N'.format(lat),
transform=cartopy.crs.Geodetic())
fig.subplots_adjust(bottom=0.05,
top=0.95,
left=0.04,
right=0.95,
wspace=0.02)
del cube
#***********************
# MODIS timeserise
sos_nh, eos_nh, sprt_nh_orig, falt_nh_orig = read_modis_ts(
os.path.join(
DATALOC, "MODIS.CMG.{}.SOS.EOS.SPRT.FALT.TS.csv".format(
settings.YEAR)))
dummy, sos_nh = utils.calculate_climatology_and_anomalies_1d(
sos_nh, 2000, 2010)
dummy, eos_nh = utils.calculate_climatology_and_anomalies_1d(
eos_nh, 2000, 2010)
dummy, sprt_nh = utils.calculate_climatology_and_anomalies_1d(
sprt_nh_orig, 2000, 2010)
dummy, falt_nh = utils.calculate_climatology_and_anomalies_1d(
falt_nh_orig, 2000, 2010)
ax = plt.axes([0.15, 0.73, 0.75, 0.23])
if season == "SOS":
label = "(a) Start of Season"
anomalies = [
"{} SOS Anomaly = -4.3 days".format(settings.YEAR),
"{} Spr. T anomaly = 0.19 ".format(settings.YEAR) +
r'$^{\circ}$' + "C"
]
plot_modis_ts(ax, sos_nh, sprt_nh, sprt_nh_orig, label,
anomalies, LEGEND_LOC)
elif season == "EOS":
label = "(b) End of Season"
anomalies = [
"{} EOS Anomaly = 2.4 days".format(settings.YEAR),
"{} Fall T anomaly = -0.53 ".format(settings.YEAR) +
r'$^{\circ}$' + "C"
]
plot_modis_ts(ax, eos_nh, falt_nh, falt_nh_orig, label,
anomalies, LEGEND_LOC)
plt.savefig(settings.IMAGELOC + "PHEN_modis_{}_{}{}".format(
settings.YEAR, season, settings.OUTFMT))
del cubelist
#***********************
# US timeseries - 2018
if True:
fig = plt.figure(figsize=(8, 9.5))
plt.clf()
modis_sos, modis_eos, pheno_sos, pheno_eos = read_us_phenocam_csv(
os.path.join(DATALOC, "Richardson Data for SOC 2019 Figures.csv"))
# images
ax = plt.axes([0.01, 0.66, 0.49, 0.3])
plot_images(ax, "HarvardForest_20190511.jpg")
ax = plt.axes([0.5, 0.66, 0.49, 0.3])
plot_images(ax, "HarvardForest_20191024.jpg")
# timeseries
ax = plt.axes([0.11, 0.35, 0.84, 0.3])
plot_us_phenocam(ax, modis_eos, pheno_eos, sos=False)
# ax.text(0.05, 0.85, "(a)", transform=ax.transAxes, fontsize=settings.FONTSIZE)
ax.set_ylim([275, 369])
plt.setp(ax.get_xticklabels(), visible=False)
ax = plt.axes([0.11, 0.05, 0.84, 0.3])
plot_us_phenocam(ax, modis_sos, pheno_sos)
# ax.text(0.05, 0.85, "(b)", transform=ax.transAxes, fontsize=settings.FONTSIZE)
ax.set_ylim([101, 144])
fig.text(0.02,
0.97,
"(a)",
transform=ax.transAxes,
fontsize=settings.FONTSIZE)
fig.text(0.02,
0.3,
"Day of year",
rotation="vertical",
fontsize=settings.FONTSIZE)
plt.savefig(
settings.IMAGELOC +
"PHEN_UStimeseries_{}{}".format(settings.YEAR, settings.OUTFMT))
plt.close()
#***********************
# US timeseries - 2018
if False:
fig = plt.figure(figsize=(8, 7))
plt.clf()
modis_sos, modis_eos, pheno_sos, pheno_eos = read_us_phenocam_csv(
os.path.join(DATALOC, "Richardson Data for SOC 2019 Figures.csv"))
# timeseries
ax = plt.axes([0.11, 0.5, 0.64, 0.45])
plot_us_phenocam(ax, modis_eos, pheno_eos, sos=False)
ax.text(0.05,
0.85,
"(c)",
transform=ax.transAxes,
fontsize=settings.FONTSIZE)
ax.set_ylim([275, 369])
plt.setp(ax.get_xticklabels(), visible=False)
ax = plt.axes([0.75, 0.5, 0.25, 0.4])
plot_images(ax, "HarvardForest_20191024.jpg")
ax = plt.axes([0.11, 0.05, 0.64, 0.45])
plot_us_phenocam(ax, modis_sos, pheno_sos)
ax.text(0.05,
0.85,
"(d)",
transform=ax.transAxes,
fontsize=settings.FONTSIZE)
ax.set_ylim([101, 144])
ax = plt.axes([0.75, 0.05, 0.25, 0.4])
plot_images(ax, "HarvardForest_20190511.jpg")
fig.text(0.02,
0.4,
"Day of year",
rotation="vertical",
fontsize=settings.FONTSIZE)
plt.savefig(
settings.IMAGELOC +
"PHEN_UStimeseries_{}{}".format(settings.YEAR, settings.OUTFMT))
plt.close()
#***********************
# UK timeseries - 2018
if True:
from matplotlib.ticker import MultipleLocator
majorLocator = MultipleLocator(5)
fig = plt.figure(figsize=(8, 9.5))
plt.clf()
# images
ax = plt.axes([0.01, 0.66, 0.48, 0.3])
plot_images(ax, "Sarah Burgess first leaf.jpg")
ax = plt.axes([0.5, 0.66, 0.48, 0.3])
plot_images(ax, "Judith Garforth oak bare tree 2019.jpg")
sos_uk, eos_uk = read_modis_uk_ts(
os.path.join(
DATALOC, "MODIS.CMG.{}.SOS.EOS.SPRT.FALT.TS.UK_DH.csv".format(
settings.YEAR)))
oak_sos, oak_eos = read_uk_oak_csv(
os.path.join(DATALOC, "UK_Oakleaf_data.csv"))
# timeseries
ax = plt.axes([0.11, 0.35, 0.84, 0.3])
utils.plot_ts_panel(ax, [oak_eos, eos_uk],
"-",
"phenological",
loc="center left")
# ax.text(0.05, 0.85, "(c)", transform=ax.transAxes, fontsize=settings.FONTSIZE)
ax.set_ylim([210, 354])
plt.setp(ax.get_xticklabels(), visible=False)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.xaxis.set_major_locator(majorLocator)
# naughtily, manually tweak the upper oak plot
for line in ax.get_lines():
if line.get_color() == "g":
line.set_color("#d95f0e")
leg = ax.get_legend()
for line in leg.get_lines():
if line.get_color() == "g":
line.set_color("#d95f0e")
ax = plt.axes([0.11, 0.05, 0.84, 0.3])
utils.plot_ts_panel(ax, [oak_sos, sos_uk],
"-",
"phenological",
loc="center left")
# ax.text(0.05, 0.85, "(d)", transform=ax.transAxes, fontsize=settings.FONTSIZE)
ax.set_ylim([66, 132])
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax.xaxis.set_major_locator(majorLocator)
fig.text(0.02,
0.97,
"(b)",
transform=ax.transAxes,
fontsize=settings.FONTSIZE)
fig.text(0.02,
0.3,
"Day of year",
rotation="vertical",
fontsize=settings.FONTSIZE)
plt.savefig(
settings.IMAGELOC +
"PHEN_UKtimeseries_{}{}".format(settings.YEAR, settings.OUTFMT))
plt.close()
#***********************
# Lake Boxplot
if True:
import pandas as pd
df = pd.read_csv(DATALOC + "LakeData_forRobert.csv")
# rename columns
cols = []
for col in df.columns:
if len(col.split()) >= 2:
df.rename(columns={col: col.split()[0]}, inplace=True)
cols += [col.split()[0]]
fig = plt.figure(figsize=(8, 7))
plt.clf()
ax = plt.axes([0.1, 0.25, 0.89, 0.74])
df.boxplot(
column=cols,
ax=ax,
grid=False,
)
# messily pull out 2019
this_year = df.iloc[-1]
this_year = this_year.to_frame()
plt.plot(np.arange(11) + 1, this_year[19][1:], "ro")
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
tick.label.set_rotation("vertical")
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
utils.thicken_panel_border(ax)
plt.ylabel("Day of year", fontsize=settings.FONTSIZE)
plt.savefig(
settings.IMAGELOC +
"PHEN_lakes_boxplot_{}{}".format(settings.YEAR, settings.OUTFMT))
plt.close()
return # run_all_plots
def run_all_plots():
# #***********************
# # Three panel timeseries - UK, DWD, Windermere
# chlorophyll = read_lake_csv(os.path.join(data_loc, "timeseries", "Windermere.csv"))
# betula_out = read_dwd_betula(os.path.join(data_loc, "timeseries", "dwd-data_per180112_Betula_pendula_leaf_out.csv"))
# quercus_out, quercus_fall = read_dwd_quercus(os.path.join(data_loc, "timeseries", "dwd-data_per-180112_Quercus robur leave unf.csv"))
# betula_fall, fagus_out, fagus_fall = read_dwd_fagus(os.path.join(data_loc, "timeseries", "dwd-data_per180112_Fagus sylvatica and Betula pendula leaf data.csv"))
# alder, chestnut, oak, beech = read_uk_csv(os.path.join(data_loc, "timeseries", "details_for_climate_report.csv"))
# long_oak = read_uk_oak_csv(os.path.join(data_loc, "timeseries", "NatureCalendar_pedunculate_oak_first_leaf_1753_1999.csv"))
# fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 7), sharex=True)
# # DWD data - budburst
# utils.plot_ts_panel(ax1, [betula_out, fagus_out, quercus_out], "-", "phenological", loc="")
# lines, labels = ax1.get_legend_handles_labels()
# newlabels = []
# for ll in labels:
# newlabels += [r'${}$'.format(ll)]
# ax1.legend(lines, newlabels, loc="lower left", ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5)
# ax4 = plt.axes([0.84, 0.84, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, 'DWD_oak_leaf.jpg'))
# ax4.imshow(img)
# ax4.set_xticks([])
# ax4.set_yticks([])
# # UK data - budburst
# utils.plot_ts_panel(ax2, [alder, chestnut, beech, long_oak, oak], "-", "phenological", loc="")
# ax2.plot(long_oak.times, long_oak.data, ls=None, marker="o", c=settings.COLOURS["phenological"][long_oak.name], markeredgecolor=settings.COLOURS["phenological"][long_oak.name])
# lines, labels = ax2.get_legend_handles_labels()
# newlabels = []
# for ll in labels:
# newlabels += [r'${}$'.format(ll)]
# ax2.legend(lines, newlabels, loc="lower left", ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5)
# ax5 = plt.axes([0.84, 0.54, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, '61d1c433-b65c-47bc-b831-7e44ab74a895.jpg'))
# ax5.imshow(img)
# ax5.set_xticks([])
# ax5.set_yticks([])
# # Windermere
# utils.plot_ts_panel(ax3, [chlorophyll], "-", "phenological", loc="lower left")
# ax6 = plt.axes([0.84, 0.23, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, 'Asterionella and aulacoseira 05 May 2017.jpg'))
# ax6.imshow(img)
# ax6.set_xticks([])
# ax6.set_yticks([])
# # prettify
# ax1.set_ylim([70, 160])
# ax2.set_ylim([70, 160])
# ax3.set_ylim([70, 160])
# ax2.set_ylabel("Day of year", fontsize=settings.FONTSIZE)
# ax1.text(0.02, 0.88, "(a) Germany - tree leaf unfurling", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
# ax2.text(0.02, 0.88, "(b) UK - tree bud burst & leaf out", transform=ax2.transAxes, fontsize=settings.FONTSIZE)
# ax3.text(0.02, 0.88, "(c) UK - Windermere - plankton", transform=ax3.transAxes, fontsize=settings.FONTSIZE)
# for tick in ax3.xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# minorLocator = MultipleLocator(100)
# for ax in [ax1, ax2, ax3]:
# utils.thicken_panel_border(ax)
# ax.set_yticks(ax.get_yticks()[1:-1])
# ax.xaxis.set_minor_locator(minorLocator)
# for tick in ax.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# ax1.set_xlim([1950, 2020])
# plt.setp([a.get_xticklabels() for a in [ax1, ax2]], visible=False)
# fig.subplots_adjust(right=0.95, top=0.95, bottom=0.05, hspace=0.001)
# plt.savefig(image_loc+"PHEN_tree_ts{}".format(settings.OUTFMT))
# plt.close()
# #***********************
# # Barlett start/end timeseries - image inset
# # Hand-copied data from "Bartlett Gcc Transition Dates 2008-2017.xlsx"
# # up, down, diff = read_bartlett_green(os.path.join(data_loc, "timeseries", "Bartlett_greenupdown.dat"))
# # fig = plt.figure(figsize = (8, 9))
# # plt.clf()
# # # make axes by hand
# # ax1 = plt.axes([0.15,0.3,0.8,0.65])
# # ax2 = plt.axes([0.15,0.1,0.8,0.2], sharex = ax1)
# # print "need inset axes for images"
# # # plot data
# # ax1.plot(up.times, up.data, c = "g", ls = "-", lw = 2, label = "Green up")
# # ax1.plot(down.times, down.data, c = "brown", ls = "-", lw = 2, label = "Green down")
# # ax1.fill_between(up.times, up.data, down.data, color = "0.75")
# # ax2.plot(diff.times, diff.data, c = "c", ls = "-", lw = 2)
# # ax1.legend(loc="upper right", ncol = 1, frameon = False, prop = {'size':settings.LEGEND_FONTSIZE}, labelspacing = 0.1, columnspacing = 0.5)
# # # sort axes
# # ax1.set_xlim([2008, 2018])
# # ax2.set_ylim([140,180])
# # ax1.text(0.02, 0.93, "(a) Bartlett seasons", transform = ax1.transAxes, fontsize = settings.FONTSIZE)
# # ax2.text(0.02, 0.85, "(b) Difference", transform = ax2.transAxes, fontsize = settings.FONTSIZE)
# # # prettify
# # ax1.set_ylabel("Day of Year", fontsize = settings.FONTSIZE)
# # ax2.set_ylabel("Days", fontsize = settings.FONTSIZE)
# # minorLocator = MultipleLocator(100)
# # for ax in [ax1, ax2]:
# # utils.thicken_panel_border(ax)
# # ax.set_yticks(ax.get_yticks()[1:-1])
# # ax.xaxis.set_minor_locator(minorLocator)
# # for tick in ax.yaxis.get_major_ticks():
# # tick.label.set_fontsize(settings.FONTSIZE)
# # for tick in ax.xaxis.get_major_ticks():
# # tick.label.set_fontsize(settings.FONTSIZE)
# # plt.setp(ax1.get_xticklabels(), visible=False)
# # plt.savefig(image_loc+"PHEN_bartlett_ts{}".format(settings.OUTFMT))
# # plt.close()
# #***********************
# # Barlett start/end timeseries - image inset
# # Hand-copied data from "Bartlett Gcc Transition Dates 2008-2017.xlsx"
# # https://matplotlib.org/examples/pylab_examples/broken_axis.html
# up, down, diff = read_bartlett_green(os.path.join(data_loc, "timeseries", "Bartlett_greenupdown.dat"))
# fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 9), sharex=True)
# print("need inset axes for images")
# # plot same on both
# ax1.plot(up.times, up.data, c="g", ls="-", lw=2, label="Green up", marker="o")
# ax1.plot(down.times, down.data, c="brown", ls="-", lw=2, label="Green down", marker="o")
# ax1.fill_between(up.times, up.data, down.data, color="lightgreen")
# ax2.plot(up.times, up.data, c="g", ls="-", lw=2, marker="o")
# ax2.plot(down.times, down.data, c="brown", ls="-", lw=2, marker="o")
# ax2.fill_between(up.times, up.data, down.data, color="lightgreen")
# ax1.invert_yaxis()
# ax2.invert_yaxis()
# # hide the spines between ax and ax2
# ax1.spines['bottom'].set_visible(False)
# ax2.spines['top'].set_visible(False)
# ax1.xaxis.tick_top()
# ax1.tick_params(labeltop='off') # don't put tick labels at the top
# ax2.xaxis.tick_bottom()
# # and the difference
# ax3.plot(diff.times, diff.data, c="c", ls="-", lw=2, marker="o")
# # plot the legend
# ax1.legend(loc="upper right", ncol=1, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5)
# # prettify
# ax1.set_xlim([2007, int(settings.YEAR)+1])
# ax2.set_ylabel("Day of Year", fontsize=settings.FONTSIZE)
# ax3.set_ylabel("Days", fontsize=settings.FONTSIZE)
# ax1.text(0.02, 0.88, "(a) Bartlett seasons", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
# ax3.text(0.02, 0.85, "(b) Difference", transform=ax3.transAxes, fontsize=settings.FONTSIZE)
# # zoom in
# ax1.set_ylim([150, 100])
# ax2.set_ylim([300, 250])
# ax3.set_ylim([136, 179])
# minorLocator = MultipleLocator(100)
# for ax in [ax1, ax2, ax3]:
# utils.thicken_panel_border(ax)
# ax.set_yticks(ax.get_yticks()[1:-1])
# ax.xaxis.set_minor_locator(minorLocator)
# for tick in ax.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# for tick in ax.xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# plt.setp(ax1.get_xticklabels(), visible=False)
# fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001)
# plt.savefig(image_loc+"PHEN_bartlett_ts_gap{}".format(settings.OUTFMT))
# plt.close()
# #***********************
# # Bartlett GPP & Duke separate
# fig = plt.figure(figsize=(8, 6.5))
# plt.clf()
# ax1 = plt.axes([0.12, 0.52, 0.78, 0.44])
# ax2 = ax1.twinx()
# ax3 = plt.axes([0.12, 0.08, 0.78, 0.44], sharex=ax1)
# gpp, gcc = read_bartlett_gpp(os.path.join(data_loc, "timeseries", "Bartlett GPP Flux and Canopy Greenness.csv"))
# bartlett, duke = read_bartlett_duke(os.path.join(data_loc, "timeseries", "Bartlett 2008-2017 and Duke 2017 Camera Greenness.csv"))
# utils.plot_ts_panel(ax1, [gcc], "-", "phenological", loc="")
# utils.plot_ts_panel(ax2, [gpp], "-", "phenological", loc="")
# utils.plot_ts_panel(ax3, [bartlett, duke], "-", "phenological", loc="upper right", ncol=1)
# ax3.axhline(0.36, c='0.5', ls='--')
# ax3.axvline(185, c='0.5', ls=":")
# # fix the legend
# lines1, labels1 = ax1.get_legend_handles_labels()
# lines2, labels2 = ax2.get_legend_handles_labels()
# ax2.legend(lines1 + lines2, labels1 + labels2, loc="upper right", ncol=1, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5)
# # prettify
# ax2.yaxis.set_label_position("right")
# ax2.yaxis.set_ticks_position('right')
# ax1.set_ylim([0.34, 0.48])
# ax3.set_ylim([0.34, 0.48])
# ax2.set_ylim([-2, 12])
# ax1.set_ylabel("Canopy Greeness", fontsize=settings.FONTSIZE)
# ax2.set_ylabel("Gross Primary Productivity", fontsize=settings.FONTSIZE)
# ax3.set_ylabel("Canopy Greeness", fontsize=settings.FONTSIZE)
# ax1.text(0.02, 0.88, "(c)", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
# ax3.text(0.02, 0.85, "(d)", transform=ax3.transAxes, fontsize=settings.FONTSIZE)
# for tick in ax3.xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# minorLocator = MultipleLocator(100)
# for ax in [ax1, ax3]:
# utils.thicken_panel_border(ax)
# ax.set_yticks(ax.get_yticks()[1:-1])
# ax.xaxis.set_minor_locator(minorLocator)
# for tick in ax.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# utils.thicken_panel_border(ax2)
# ax2.set_yticks(ax2.get_yticks()[1:-1])
# for tick in ax2.yaxis.get_major_ticks():
# tick.label2.set_fontsize(settings.FONTSIZE)
# plt.setp(ax1.get_xticklabels(), visible=False)
# plt.setp(ax2.get_xticklabels(), visible=False)
# fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001)
# ax4 = plt.axes([0.14, 0.65, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, 'bbc7_2017_01_20_140005.jpg'))
# ax4.imshow(img)
# ax4.set_xticks([])
# ax4.set_yticks([])
# ax4.set_title("January")
# ax5 = plt.axes([0.72, 0.65, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, 'bbc7_2017_07_17_130005.jpg'))
# ax5.imshow(img)
# ax5.set_xticks([])
# ax5.set_yticks([])
# ax5.set_title("July")
# ax6 = plt.axes([0.14, 0.25, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, 'bbc7_2017_07_04_124505.jpg'))
# ax6.imshow(img)
# ax6.set_xticks([])
# ax6.set_yticks([])
# ax6.set_title("Bartlett")
# ax7 = plt.axes([0.72, 0.25, 0.15, 0.15])
# img = mpimg.imread(os.path.join(data_loc, 'dukehw_2017_07_04_120110.jpg'))
# ax7.imshow(img)
# ax7.set_xticks([])
# ax7.set_yticks([])
# ax7.set_title("Duke")
# plt.savefig(image_loc+"PHEN_Bartlett_GPP_Duke_separate{}".format(settings.OUTFMT))
# plt.close()
# #***********************
# # Bartlett GPP & Duke combined
# # fig = plt.figure(figsize = (8, 5))
# # plt.clf()
# # ax1 = plt.axes([0.12, 0.1, 0.78, 0.8])
# # ax2 = ax1.twinx()
# # gpp, gcc = read_bartlett_gpp(os.path.join(data_loc, "timeseries", "Bartlett GPP Flux and Canopy Greenness.csv"))
# # bartlett, duke = read_bartlett_duke(os.path.join(data_loc, "timeseries", "Bartlett 2008-2017 and Duke 2017 Camera Greenness.csv"))
# # utils.plot_ts_panel(ax1, [gcc, duke], "-", "phenological", loc = "")
# # utils.plot_ts_panel(ax2, [gpp], "-", "phenological", loc = "")
# # # fix the legend
# # lines1, labels1 = ax1.get_legend_handles_labels()
# # lines2, labels2 = ax2.get_legend_handles_labels()
# # # have to remove duplicate label
# # ax2.legend(lines1 + lines2, labels1 + labels2, loc="upper right", ncol = 1, frameon = False, prop = {'size':settings.LEGEND_FONTSIZE}, labelspacing = 0.1, columnspacing = 0.5)
# # # prettify
# # ax2.yaxis.set_label_position("right")
# # ax1.set_ylim([0.34, 0.48])
# # ax2.set_ylim([-2, 12])
# # ax1.set_ylabel("Canopy Greeness", fontsize = settings.FONTSIZE)
# # ax2.set_ylabel("Gross Primary Productivity", fontsize = settings.FONTSIZE)
# # for tick in ax1.xaxis.get_major_ticks():
# # tick.label.set_fontsize(settings.FONTSIZE)
# # minorLocator = MultipleLocator(100)
# # for ax in [ax1, ax3]:
# # utils.thicken_panel_border(ax)
# # ax.set_yticks(ax.get_yticks()[1:])
# # ax.xaxis.set_minor_locator(minorLocator)
# # for tick in ax.yaxis.get_major_ticks():
# # tick.label.set_fontsize(settings.FONTSIZE)
# # utils.thicken_panel_border(ax2)
# # ax2.set_yticks(ax2.get_yticks()[1:-1])
# # for tick in ax2.yaxis.get_major_ticks():
# # tick.label2.set_fontsize(settings.FONTSIZE)
# # fig.subplots_adjust(right = 0.95, top = 0.95, hspace = 0.001)
# # plt.savefig(image_loc+"PHEN_Bartlett_GPP_Duke_combined{}".format(settings.OUTFMT))
# # plt.close()
# #***********************
# # UK Map
# import cartopy.feature as cfeature
# land_50m = cfeature.NaturalEarthFeature('physical', 'land', '50m',
# edgecolor='face',
# facecolor=cfeature.COLORS['land'])
# species, days, lats, lons = read_uk_map_csv(os.path.join(data_loc, "timeseries", "UK_Leafout_4Trees.csv"))
# cmap = plt.cm.YlGn
# bounds = np.arange(80, 160, 10)
# norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
# fig = plt.figure(figsize=(8, 10.5))
# plt.clf()
# ax = plt.axes([0.05, 0.05, 0.9, 0.9], projection=cartopy.crs.LambertConformal(central_longitude=-7.5))
# ax.gridlines() #draw_labels=True)
# ax.add_feature(land_50m, zorder=0, facecolor="0.9", edgecolor="k")
# ax.set_extent([-10, 4, 48, 60], cartopy.crs.PlateCarree())
# scat = ax.scatter(lons, lats, c=days, transform=cartopy.crs.PlateCarree(), cmap=cmap, norm=norm, s=25, edgecolor='0.5', linewidth='0.5', zorder=10)
# utils.thicken_panel_border(ax)
# cb = plt.colorbar(scat, orientation='horizontal', ticks=bounds[1:-1], label="Day of year", drawedges=True, fraction=0.1, pad=0.05, aspect=15, shrink=0.8)
# # prettify
# cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]])
# cb.outline.set_linewidth(2)
# cb.dividers.set_color('k')
# cb.dividers.set_linewidth(2)
# plt.savefig(image_loc + "PHEN_UK_map{}".format(settings.OUTFMT))
# plt.close()
#***********************
# MODIS
cubelist = iris.load(os.path.join(data_loc, "MODIS.CMG.{}.SOS.EOS.Anomaly.nc".format(settings.YEAR)))
for c, cube in enumerate(cubelist):
if cube.name() == "EOS":
eos_cube = cubelist[c]
elif cube.name() == "SOS":
sos_cube = cubelist[c]
elif cube.name() == "MAX":
max_cube = cubelist[c]
# deal with NANS
# eos_cube.data = np.ma.masked_where(eos_cube.data != eos_cube.data, eos_cube.data)
sos_cube.data = np.ma.masked_where(sos_cube.data != sos_cube.data, sos_cube.data)
# max_cube.data = np.ma.masked_where(max_cube.data != max_cube.data, max_cube.data)
# set up a 1 x 2 set of axes (2018 only needs one panel)
fig = plt.figure(figsize=(8, 8))
plt.clf()
# set up plot settings
BOUNDS = [[-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100]]#, [-100, -20, -10, -5, -2, 0, 2, 5, 10, 20, 100]]
CMAPS = [settings.COLOURMAP_DICT["phenological_r"]]#, settings.COLOURMAP_DICT["phenological"]]
CUBES = [sos_cube]# eos_cube]
LABELS = ["(a) Start of Season (SOS)"]#, "(b) End of Season (EOS)"]
# boundary circle
theta = np.linspace(0, 2*np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
# spin through axes
for a in range(1):
ax = plt.subplot(1, 1, a+1, projection=cartopy.crs.NorthPolarStereo())
plot_cube = CUBES[a]
if settings.OUTFMT in [".eps", ".pdf"]:
if plot_cube.coord("latitude").points.shape[0] > 90 or plot_cube.coord("longitude").points.shape[0] > 360:
regrid_size = 1.0
print("Regridding cube for {} output to {} degree resolution".format(settings.OUTFMT, regrid_size))
print("Old Shape {}".format(plot_cube.data.shape))
plot_cube = utils.regrid_cube(plot_cube, regrid_size, regrid_size)
print("New Shape {}".format(plot_cube.data.shape))
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k")
ax.coastlines()
ax.set_boundary(circle, transform=ax.transAxes)
ax.set_extent([-180, 180, 45, 90], cartopy.crs.PlateCarree())
ext = ax.get_extent() # save the original extent
cmap = CMAPS[a]
norm = mpl.cm.colors.BoundaryNorm(BOUNDS[a], cmap.N)
mesh = iris.plot.pcolormesh(plot_cube, cmap=cmap, norm=norm, axes=ax)
ax.set_extent(ext, ax.projection) # fix the extent change from colormesh
ax.text(-0.1, 1.0, LABELS[a], fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes)
cb = plt.colorbar(mesh, orientation='horizontal', ticks=BOUNDS[a][1:-1], label="Anomaly (days)", drawedges=True, fraction=0.1, pad=0.05, aspect=15, shrink=0.8)
# prettify
cb.set_ticklabels(["{:g}".format(b) for b in BOUNDS[a][1:-1]])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
ax.set_extent([-180, 180, 45, 90], cartopy.crs.PlateCarree())
fig.subplots_adjust(bottom=0.05, top=0.95, left=0.04, right=0.95, wspace=0.02)
plt.title("")
plt.savefig(image_loc + "PHEN_modis_polar{}".format(settings.OUTFMT))
plt.close()
del eos_cube
del sos_cube
del cubelist
#***********************
# MODIS LAI
# max_cube.data = np.ma.masked_where(max_cube.data <= -9000, max_cube.data)
# if settings.OUTFMT in [".eps", ".pdf"]:
# if max_cube.coord("latitude").points.shape[0] > 180 or max_cube.coord("longitude").points.shape[0] > 360:
# regrid_size = 1.0
# print("Regridding cube for {} output to {} degree resolution".format(settings.OUTFMT, regrid_size))
# print("Old Shape {}".format(max_cube.data.shape))
# max_cube = utils.regrid_cube(max_cube, regrid_size, regrid_size)
# print("New Shape {}".format(max_cube.data.shape))
# fig = plt.figure(figsize=(8, 8))
# plt.clf()
# ax = plt.axes([0.05, 0.05, 0.9, 0.9], projection=cartopy.crs.NorthPolarStereo())
# ax.gridlines() #draw_labels=True)
# ax.add_feature(cartopy.feature.LAND, zorder=0, facecolor="0.9", edgecolor="k")
# ax.coastlines()
# ax.set_boundary(circle, transform=ax.transAxes)
# ax.set_extent([-180, 180, 45, 90], cartopy.crs.PlateCarree())
# ext = ax.get_extent() # save the original extent
# cmap = settings.COLOURMAP_DICT["phenological_r"]
# bounds = [-100, -5, -3, -2, -1, 0, 1, 2, 3, 5, 100]
# norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
# mesh = iris.plot.pcolormesh(max_cube, cmap=cmap, norm=norm, axes=ax)
# ax.set_extent(ext, ax.projection) # fix the extent change from colormesh
# ax.text(-0.1, 1.0, "Max Leaf Area Index", fontsize=settings.FONTSIZE * 0.8, transform=ax.transAxes)
# cb = plt.colorbar(mesh, orientation='horizontal', ticks=bounds[1:-1], label="Anomaly (m"+r'$^2'+"/m"+r'$^2'+")", drawedges=True, fraction=0.1, pad=0.05, aspect=15, shrink=0.8)
# # prettify
# cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]])
# cb.outline.set_linewidth(2)
# cb.dividers.set_color('k')
# cb.dividers.set_linewidth(2)
# ax.set_extent([-180, 180, 45, 90], cartopy.crs.PlateCarree())
# plt.savefig(image_loc + "PHEN_modis_lai{}".format(settings.OUTFMT))
# plt.close()
#***********************
# MODIS timeseries - 2017
# sos, eos, lai = read_modis_ts(os.path.join(data_loc, "MODIS.CMG.{}.SOS.EOS.MAX.AnomalyTS.csv".format(settings.YEAR)))
# plt.clf()
# fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 4), sharex=True)
# # plot same on both
# ax1.plot(sos.times, sos.data, c="g", ls="-", lw=2, label="Start of Season", marker="o")
# ax1.plot(eos.times, eos.data, c="brown", ls="-", lw=2, label="End of Season", marker="o")
# ax1.fill_between(eos.times, eos.data, sos.data, color="lightgreen")
# ax2.plot(sos.times, sos.data, c="g", ls="-", lw=2, marker="o")
# ax2.plot(eos.times, eos.data, c="brown", ls="-", lw=2, marker="o")
# ax2.fill_between(eos.times, eos.data, sos.data, color="lightgreen")
# # invert so green down is on the bottom
# ax1.invert_yaxis()
# ax2.invert_yaxis()
# # hide the spines between ax and ax2
# ax1.spines['bottom'].set_visible(False)
# ax2.spines['top'].set_visible(False)
# ax1.xaxis.tick_top()
# ax1.tick_params(labeltop='off') # don't put tick labels at the top
# ax2.xaxis.tick_bottom()
# # plot the legend
# ax1.legend(loc="upper right", ncol=1, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, labelspacing=0.1, columnspacing=0.5)
# # prettify
# ax1.set_xlim([1999, 2018])
# ax2.set_ylabel("Day of Year", fontsize=settings.FONTSIZE)
# # zoom in
# ax1.set_ylim([160, 110])
# ax2.set_ylim([300, 250])
# # set label
# ax1.text(-0.1, 0.92, "(c)", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
# minorLocator = MultipleLocator(100)
# for ax in [ax1, ax2]:
# utils.thicken_panel_border(ax)
# ax.set_yticks(ax.get_yticks()[1:-1])
# ax.xaxis.set_minor_locator(minorLocator)
# for tick in ax.yaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# for tick in ax.xaxis.get_major_ticks():
# tick.label.set_fontsize(settings.FONTSIZE)
# plt.setp(ax1.get_xticklabels(), visible=False)
# fig.subplots_adjust(right=0.95, top=0.95, hspace=0.001)
# plt.savefig(image_loc+"PHEN_modis_ts{}".format(settings.OUTFMT))
# plt.close()
#***********************
# MODIS timeseries - 2018
sos_na, sos_ea, sprt_na, sprt_ea = read_modis_ts(os.path.join(data_loc, "MODIS.CMG.{}.SOS.EOS.SPRT.FALT.TS.csv".format(settings.YEAR)))
dummy, sos_na = utils.calculate_climatology_and_anomalies_1d(sos_na, 2000, 2010)
dummy, sos_ea = utils.calculate_climatology_and_anomalies_1d(sos_ea, 2000, 2010)
fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6.5), sharex=True)
# North America
# use un-anomalised spring T to get legend without the data
utils.plot_ts_panel(ax1, [sos_na, sprt_na], "-", "phenological", loc=LEGEND_LOC)
# Eurasia
utils.plot_ts_panel(ax2, [sos_ea, sprt_ea], "-", "phenological", loc=LEGEND_LOC)
# make twin axes for Spring T
dummy, sprt_na = utils.calculate_climatology_and_anomalies_1d(sprt_na, 2000, 2010)
dummy, sprt_ea = utils.calculate_climatology_and_anomalies_1d(sprt_ea, 2000, 2010)
ax3 = ax1.twinx()
utils.plot_ts_panel(ax3, [sprt_na], "-", "phenological", loc="")
ax4 = ax2.twinx()
utils.plot_ts_panel(ax4, [sprt_ea], "-", "phenological", loc="")
# prettify
ax1.set_ylim([-8, 8])
ax2.set_ylim([-8, 8])
ax3.set_ylim([2, -2])
ax4.set_ylim([2, -2])
# labels
ax1.text(0.02, 0.88, "(a) North America", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
ax2.text(0.02, 0.88, "(b) Eurasia", transform=ax2.transAxes, fontsize=settings.FONTSIZE)
ax1.text(0.47, 0.88, "2018 SOS Anomaly = 1.86 days", transform=ax1.transAxes, fontsize=settings.FONTSIZE*0.8)
ax1.text(0.47, 0.78, "2018 Spring T anomaly = -0.75 "+r'$^{\circ}$'+"C", transform=ax1.transAxes, fontsize=settings.FONTSIZE*0.8)
ax2.text(0.47, 0.88, "2018 SOS Anomaly = 2.01 days", transform=ax2.transAxes, fontsize=settings.FONTSIZE*0.8)
ax2.text(0.47, 0.78, "2018 Spring T anomaly = 0.13 "+r'$^{\circ}$'+"C", transform=ax2.transAxes, fontsize=settings.FONTSIZE*0.8)
fig.text(0.01, 0.5, "SOS Anomaly (days)", va='center', rotation='vertical', fontsize = settings.FONTSIZE)
fig.text(0.95, 0.5, "Temperature Anomaly ("+r'$^{\circ}$'+"C)", va='center', rotation='vertical', fontsize = settings.FONTSIZE)
# ticks and labels
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
minorLocator = MultipleLocator(1)
for ax in [ax1, ax2]:
utils.thicken_panel_border(ax)
ax.set_yticks(ax.get_yticks()[1:-1])
ax.xaxis.set_minor_locator(minorLocator)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for ax in [ax3, ax4]:
ax.yaxis.tick_right()
utils.thicken_panel_border(ax)
ax.set_yticks(ax.get_yticks()[1:-1])
ax.xaxis.set_minor_locator(minorLocator)
for tick in ax.yaxis.get_major_ticks():
tick.label2.set_fontsize(settings.FONTSIZE)
# final settings
ax1.set_xlim([1999, 2020])
plt.setp([a.get_xticklabels() for a in [ax1]], visible=False)
fig.subplots_adjust(left=0.1, right=0.85, top=0.95, bottom=0.05, hspace=0.001)
plt.savefig(image_loc+"PHEN_modis_ts{}".format(settings.OUTFMT))
plt.close()
return # run_all_plots
def run_all_plots():
#************************************************************************
# Precipitation Timeseries
if False:
ghcn, gpcc, gpcp, ghcn2, erai, merra2 = read_land(
DATALOC + "Land_insitu_timeseries-1979.dat")
fig = plt.figure(figsize=(8, 5))
ax1 = plt.axes([0.1, 0.1, 0.85, 0.85])
# Land
utils.plot_ts_panel(ax1, [ghcn, gpcc, gpcp, ghcn2, erai, merra2],
"-",
"hydrological",
loc=LEGEND_LOC,
bbox=BBOX)
ax1.set_ylim([-60, 100])
ax1.yaxis.set_ticks([-50, -25, 0, 25, 50, 75, 100])
ax1.set_ylabel("Anomaly (mm)", fontsize=settings.FONTSIZE)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# ax1.text(0.02, 0.9, "(a) Land in Situ", transform=ax1.transAxes, fontsize=settings.LABEL_FONTSIZE)
plt.savefig(settings.IMAGELOC + "PCP_ts{}".format(settings.OUTFMT))
plt.close()
# old 3-panel plot for 2015 report
if False:
fig = plt.figure(figsize=(8, 8))
ax1 = plt.axes([0.11, 0.68, 0.78, 0.3])
ax2 = plt.axes([0.11, 0.38, 0.78, 0.3], sharex=ax1)
ax3 = plt.axes([0.11, 0.08, 0.78, 0.3], sharex=ax1)
# Land
utils.plot_ts_panel(ax1, [ghcn, gpcc, gpcp, ghcn2, erai, merra2],
"-",
"hydrological",
loc=LEGEND_LOC,
bbox=BBOX)
# Ocean
ocean_year = read_domain(
DATALOC + "gpcp_v23_globalocean_ann_1979_2018", "GPCP")
o_clim, o_anoms = utils.calculate_climatology_and_anomalies_1d(
ocean_year, 1981, 2010)
ax2.plot(o_anoms.times,
o_anoms.data,
c="b",
ls="-",
label=o_anoms.name,
lw=2)
# Nino and others
land_year = read_domain(DATALOC + "gpcp_v23_globalland_ann_1979_2018",
"GPCP")
l_clim, l_anoms = utils.calculate_climatology_and_anomalies_1d(
land_year, 1981, 2010)
combined_year = read_domain(
DATALOC + "gpcp_v23_globallandocean_ann_1979_2018", "GPCP")
c_clim, c_anoms = utils.calculate_climatology_and_anomalies_1d(
combined_year, 1981, 2010)
nino = read_domain(DATALOC + "nino34_1979_2018_ann_anomaly",
"Nino 3.4")
ax3.plot(o_anoms.times,
o_anoms.data,
c="b",
ls="-",
label="{} Ocean".format(o_anoms.name),
lw=2,
zorder=10)
ax3.plot(l_anoms.times,
l_anoms.data,
c="lime",
ls="-",
label="{} Land".format(l_anoms.name),
lw=2,
zorder=10)
ax3.plot(c_anoms.times,
c_anoms.data,
c="r",
ls="-",
label="{} Land + Ocean".format(c_anoms.name),
lw=2,
zorder=10)
ax3.plot([1960, 1961], [0, 0], c="k", label="Nino 3.4")
ax4 = ax3.twinx()
ax4.plot(nino.times, nino.data, c="k", zorder=1)
ax4.fill_between(nino.times,
nino.data,
0,
color="0.5",
label=nino.name,
zorder=1)
ax4.set_ylim([-1.9, 1.9])
ax3.patch.set_visible(False)
ax3.set_zorder(ax4.get_zorder() + 1)
#*******************
# prettify
minorLocator = MultipleLocator(1)
ax1.set_ylim([-60, 100])
ax1.yaxis.set_ticks([-50, 0, 50, 100])
for ax in [ax2, ax3]:
ax.set_ylim([-50, 50])
ax.axhline(0, c='0.5', ls='--')
ax.legend(loc=LEGEND_LOC, ncol=2, frameon=False, prop={'size':settings.LEGEND_FONTSIZE}, \
labelspacing=0.1, columnspacing=0.5, bbox_to_anchor=BBOX)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_ticks_position('left')
utils.thicken_panel_border(ax)
ax2.set_ylabel("Anomaly (mm yr" + r'$^{-1}$' + ")",
fontsize=settings.FONTSIZE)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax2.get_xticklabels(), visible=False)
ax4.set_xlim([1979, int(settings.YEAR) + 1])
ax4.yaxis.set_label_position("right")
ax3.yaxis.set_tick_params(right=False)
utils.thicken_panel_border(ax4)
for ax in [ax1, ax2, ax3]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax3.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax4.yaxis.get_major_ticks():
tick.label2.set_fontsize(settings.FONTSIZE)
# sort labelling
ax1.text(0.02,
0.9,
"(a) Land in Situ",
transform=ax1.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02,
0.9,
"(b) Ocean",
transform=ax2.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax3.text(0.02,
0.9,
"(c) Globe",
transform=ax3.transAxes,
fontsize=settings.LABEL_FONTSIZE)
plt.savefig(settings.IMAGELOC +
"PCP_ts_3panel{}".format(settings.OUTFMT))
plt.close()
# 3-panel plot for 2019 report
if True:
fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 8), sharex=True)
# Land
ghcn, gpcc, gpcp = read_land(DATALOC +
"Land_insitu_timeseries-1979.dat")
utils.plot_ts_panel(ax1, [ghcn, gpcc, gpcp],
"-",
"hydrological",
loc=LEGEND_LOC,
bbox=BBOX)
# Ocean
gpcp = read_ocean(DATALOC + "Ocean_insitu_timeseries-1979.dat")
utils.plot_ts_panel(ax2, [gpcp],
"-",
"hydrological",
loc="",
bbox=BBOX)
# Globe
gpcp = read_ocean(DATALOC + "Global_insitu_timeseries-1979.dat")
utils.plot_ts_panel(ax3, [gpcp],
"-",
"hydrological",
loc="",
bbox=BBOX)
#*******************
# prettify
fig.subplots_adjust(right=0.98, bottom=0.08, top=0.98, hspace=0.001)
minorLocator = MultipleLocator(1)
ax1.set_ylim([-60, 100])
ax1.yaxis.set_ticks([-50, 0, 50, 100])
ax2.set_ylim([-29, 43])
ax3.set_ylim([-29, 43])
for ax in [ax1, ax2, ax3]:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax3.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
ax2.set_ylabel("Anomaly (mm yr" + r'$^{-1}$' + ")",
fontsize=settings.FONTSIZE)
# sort labelling
ax1.text(0.02,
0.9,
"(a) Land in Situ",
transform=ax1.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax2.text(0.02,
0.9,
"(b) Ocean",
transform=ax2.transAxes,
fontsize=settings.LABEL_FONTSIZE)
ax3.text(0.02,
0.9,
"(c) Globe",
transform=ax3.transAxes,
fontsize=settings.LABEL_FONTSIZE)
plt.savefig(settings.IMAGELOC +
"PCP_ts_3panel{}".format(settings.OUTFMT))
plt.close()
#************************************************************************
# GPCP map
if True:
cube = read_map(
DATALOC +
"GPCP_{}anomaly_base_1981-2000.txt".format(settings.YEAR))
bounds = [-2000, -400, -300, -200, -100, 0, 100, 200, 300, 400, 2000]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "PCP_{}_anoms_gpcp".format(settings.YEAR),
cube, settings.COLOURMAP_DICT["hydrological"], bounds,
"Anomalies from 1981-2000 (mm)")
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_PCP_{}_anoms_gpcp".format(settings.YEAR),
cube,
settings.COLOURMAP_DICT["hydrological"],
bounds,
"Anomalies from 1981-2000 (mm)",
figtext="(k) Precipitation")
#************************************************************************
# GPCP map - Ocean only
# # Read Oceans
# print "FIX YEAR"
# cube = iris.load(DATALOC + "ocean_precipitation_sotc2015.nc", "anomaly_map")[0]
# # add in the coordinates which haven't been stored sensibly
# for c, coord in enumerate(["latitude", "longitude"]):
# coord_cube = iris.load(DATALOC + "ocean_precipitation_sotc2015.nc", coord)[0]
# if c == 0:
# iris_coord = iris.coords.DimCoord(coord_cube.data[:,0], standard_name=coord, units='degrees')
# elif c == 1:
# iris_coord = iris.coords.DimCoord(coord_cube.data[0], standard_name=coord, units='degrees')
# cube.add_dim_coord(iris_coord,c)
# cube.coord(coord).guess_bounds()
# bounds=[-2000, -400, -300, -200, -100, 0, 100, 200, 300, 400, 2000]
# utils.plot_smooth_map_iris(settings.IMAGELOC + "PCP_{}_anoms_gpcp_ocean".format(settings.YEAR), cube, settings.COLOURMAP_DICT["hydrological"], bounds, "Anomalies from 1981-2000 (mm)")
return # run_all_plots