def load(ifile_fp, varname, gridfile):
"""Time average ifile and return var, dims"""
path, ifile = os.path.split(ifile_fp)
data = cd.loadvar(ifile_fp, varname, cdostr='timmean')
tmask = cd.loadvar(gridfile, 'tmask')
if data.ndim == 3:
data = data * tmask
else:
data = data * tmask[0, :, :].squeeze()
return np.ma.masked_equal(data, 0)
def save_reanalysis_trends(var, rean, start_date, end_date, datapath):
"""Compute reanalysis trends and save to HDF5
"""
lr = len(rean)
slopes = np.zeros((180, 360, lr))
cdo_str = '-seldate,' + start_date + ',' + end_date + ' -selvar,' + var + ' '
tail = '_' + var + '.mon.mean.nc'
# Loop over the reanalyses
for i, r in enumerate(rean):
ifile = os.path.join(datapath, 'remap_' + r + tail)
cdo.trend(input=(cdo_str + ifile)
, output="int.nc " + r + "_slope.nc")
slopes[:,:,i] = cd.loadvar(r + '_slope.nc', var)
os.system('rm -f int.nc ' + r + '_slope.nc')
os.system('rm -f int.nc ' + r + '_slope.nc')
# Store the DataFrame in HDF5
out_file = os.path.join(datapath, 'reanalysis_trends.h5')
h5f = h5py.File(out_file, 'a')
sy = start_date.split('-')[0]
ey = end_date.split('-')[0]
ds_path = os.path.join(var, sy + '_' + ey)
ds_name = os.path.join(ds_path, 'rean_' + var + '_trend_' + sy + '_' + ey)
h5f.create_dataset(ds_name, data=slopes)
h5f.create_dataset(ds_path + '/reanalysis_names', data=rean)
#h5f[ds_name].dims.create_scale(h5f[ds_path + 'model_names'], 'model_names')
#f[ds_name].dims[2].attach_scale(h5f[ds_path + 'model_names'])
h5f.close()
def save_reanalysis_trends(var, rean, start_date, end_date, datapath):
"""Compute reanalysis trends and save to HDF5
"""
lr = len(rean)
slopes = np.zeros((180, 360, lr))
cdo_str = '-seldate,' + start_date + ',' + end_date + ' -selvar,' + var + ' '
tail = '_' + var + '.mon.mean.nc'
# Loop over the reanalyses
for i, r in enumerate(rean):
ifile = os.path.join(datapath, 'remap_' + r + tail)
cdo.trend(input=(cdo_str + ifile), output="int.nc " + r + "_slope.nc")
slopes[:, :, i] = cd.loadvar(r + '_slope.nc', var)
os.system('rm -f int.nc ' + r + '_slope.nc')
os.system('rm -f int.nc ' + r + '_slope.nc')
# Store the DataFrame in HDF5
out_file = os.path.join(datapath, 'reanalysis_trends.h5')
h5f = h5py.File(out_file, 'a')
sy = start_date.split('-')[0]
ey = end_date.split('-')[0]
ds_path = os.path.join(var, sy + '_' + ey)
ds_name = os.path.join(ds_path, 'rean_' + var + '_trend_' + sy + '_' + ey)
h5f.create_dataset(ds_name, data=slopes)
h5f.create_dataset(ds_path + '/reanalysis_names', data=rean)
#h5f[ds_name].dims.create_scale(h5f[ds_path + 'model_names'], 'model_names')
#f[ds_name].dims[2].attach_scale(h5f[ds_path + 'model_names'])
h5f.close()
def genweights(gridfile):
"""Return weights based on grid area (2D) and
volume (3D).
"""
# Load grid dimensions and tmask
e1t = cd.loadvar(gridfile, 'e1t')
e2t = cd.loadvar(gridfile, 'e2t')
e3t = cd.loadvar(gridfile, 'e3t')
tmask = cd.loadvar(gridfile, 'tmask')
# tile e1 and e2 areas to 3D
e1t3 = np.tile(e1t, [e3t.shape[0], 1, 1])
e2t3 = np.tile(e2t, [e3t.shape[0], 1, 1])
vol = e1t3 * e2t3 * e3t * tmask
vol = np.ma.masked_equal(vol, 0)
weights3d = vol / vol.sum()
area = e1t.squeeze() * e2t.squeeze() * tmask[0, :, :]
area = np.ma.masked_equal(area, 0)
weights2d = area / area.sum()
return weights2d, weights3d
def calc_sam(psl_file, varname, start_date='1800-01-01', end_date='2013-12-31'):
"""
Compute the SAM index as the pressure difference between 40 and 65S
Parameters:
-----------
psl_file : str
The name of the **zonal meaned** SLP netcdf file to compute the SAM
from. Can be a full path.
varname : str
The name of the Sea Level Pressure variable in psl_file.
Returns:
-------
sam : array
The calculated SAM index
"""
# Extract the pressure at 40 and 65S
(head, tail) = os.path.split(psl_file)
ofile_40s = os.path.join(head, 'p40s_' + tail)
ofile_60s = os.path.join(head, 'p65s_' + tail)
ofile_sam = os.path.join(head, 'SAM_' + tail)
cdo.remapnn('lon=0/lat=-40.0', input=psl_file, output=ofile_40s)
cdo.remapnn('lon=0/lat=-65.0', input=psl_file, output=ofile_60s)
# Compute the SAM index
cdo.sub(input=ofile_40s + ' ' + ofile_60s, output=ofile_sam,
options='-f nc -b 64')
# load the data and make dataframes
sam = cd.loadvar(ofile_sam, varname, start_date=start_date,
end_date=end_date)
# cleanup
for f in [ofile_40s, ofile_60s, ofile_sam]:
os.remove(f)
return sam + 0.0
import numpy as np
import scipy as sp
from mpl_toolkits.basemap import Basemap, addcyclic
import matplotlib.pyplot as plt
import matplotlib as mpl
import brewer2mpl
from discrete_cmap import discrete_cmap
from netCDF4 import Dataset,num2date,date2num
plt.ion()
plt.close('all')
font = {'size' : 10}
plt.rc('font', **font)
# load in the CCMP data
ifile_ccmp = '/raid/ra40/data/ncs/ccmp/ccmp_slope_199801-201112.nc'
slope_ccmp_uwnd = cd.loadvar(ifile_ccmp, 'uwnd')*120.
slope_ccmp_wspd = cd.loadvar(ifile_ccmp, 'wspd')*120.
slope_ccmp_uwnd = np.ma.masked_outside(slope_ccmp_uwnd, -2,2)
slope_ccmp_wspd = np.ma.masked_outside(slope_ccmp_wspd, -2,2)
dims = {'lat' : np.arange(-89.5,90.1,1),
'lon' : np.arange(0,361,1)
}
fig, axa = plt.subplots(2,1, sharex=True, sharey=True, figsize=(7,7),
squeeze=True)
fig.subplots_adjust(right=0.825, top=0.65, hspace=0.1, wspace=0.05)
vmin = -1
def plot_zonmean_trends(datapath):
# Load the CMIP5 data
h5f = h5py.File(datapath + 'cmip5_trends.h5','r')
psl_slope_c5 = h5f['psl/1979_2004/c5_psl_trend_1979_2004'][:]*120
uas_slope_c5_88 = h5f['uas/1988_2011/c5_uas_trend_1988_2011'][:]*120
uflx_slope_c5_88 = h5f['tauu/1988_2011/c5_tauu_trend_1988_2011'][:]*120*100
h5f.close()
# load in the reanlaysis data
rlc = [ 'k' , 'y', 'g' , 'b' , 'c' , 'm' ]
h5f = h5py.File(datapath + 'reanalysis_trends.h5','r')
psl_slope_rean = h5f['slp/1979_2004/rean_slp_trend_1979_2004'][:]*120
uas_slope_rean_88 = h5f['u10m/1988_2011/rean_u10m_trend_1988_2011'][:]*120
uflx_slope_rean_88 = h5f['uflx/1988_2011/rean_uflx_trend_1988_2011'][:]*120*100
rean = h5f['uflx/1988_2011/reanalysis_names'][:]
h5f.close()
# The HadSLPr2 data is in the rean hdf
psl_slope_hadslp = psl_slope_rean[:,:,6]
# load in the CCMP data
ifile_ccmp = datapath + 'slope_remap_CCMP_198701-201112.nc' #despite name, starts 88
slope_ccmp = cd.loadvar(ifile_ccmp, 'uwnd')*120.
slope_ccmp = np.ma.masked_outside(slope_ccmp, -1,1)
uflx_slope_ccmp = cd.loadvar(ifile_ccmp, 'upstr')*120.
uflx_slope_ccmp = np.ma.masked_outside(uflx_slope_ccmp, -15,15)
uflx_slope_ccmp = uflx_slope_ccmp*1.2*1.4e-3*120
# load in the Marshall SAM data
df = pd.read_csv(datapath + 'marshall_sam.csv', index_col=0, parse_dates=True)
df = pt.time_lim(df, pd.datetime(1979,1,1), pd.datetime(2004,12,31))
dft = pt.ols(df, units='decades')
dims = {'lat' : np.arange(-89.5,89.6,1),
'lon' : np.arange(0,360,1)
}
fig, (axt, axm, axb) = plt.subplots(3,1, sharex=True, figsize=(7,7))
fig.subplots_adjust(right=0.5, hspace=0.05)
modlatplot( psl_slope_c5.mean(axis=2), axt)
axt.plot(dims['lat'], psl_slope_hadslp.mean(axis=(1)), 'k--', linewidth=3
, label='HadSLP2r')
axt.plot(dims['lat'], slope_ccmp.mean(axis=1)*np.nan, 'k-.'
, linewidth=3, label='CCMP')
# put on the trends in marshall
axt.plot(-40, dft.slp40.slope*100.0, 'kx', markersize=15, markeredgewidth=3
, zorder=10)
axt.plot(-65, dft.slp65.slope*100.0, 'kx', markersize=15, markeredgewidth=3
, label='Marshall', zorder=10)
modlatplot( uas_slope_c5_88.mean(axis=2), axm)
axm.plot(dims['lat'], slope_ccmp.mean(axis=1), 'k-.', linewidth=3, zorder=9)
modlatplot( uflx_slope_c5_88.mean(axis=2), axb)
axb.plot(dims['lat'], uflx_slope_ccmp.mean(axis=1), 'k-.', linewidth=3,
zorder=9)
for i,r in enumerate(rean):
axt.plot(dims['lat'], psl_slope_rean[:,:,i].mean(axis=1), linestyle='-'
, color=rlc[i], linewidth=2, label=r)
axm.plot(dims['lat'], uas_slope_rean_88[:,:,i].mean(axis=1), linestyle='-'
, color=rlc[i], linewidth=2)
axb.plot(dims['lat'], uflx_slope_rean_88[:,:,i].mean(axis=1), linestyle='-'
, color=rlc[i], linewidth=2)
axt.legend(bbox_to_anchor=(1.6,1), frameon=False, numpoints=1, fontsize=12)
axb.set_xlabel('Latitude')
ylabs = ['Pa decade$^{-1}$',
'm s$^{-1}$ decade$^{-1}$',
r'Pa decade$^{-1}\times 10^{-2}$'
]
for i, ax in enumerate((axt, axm, axb)):
ax.set_xlim([-80, -20])
ax.plot([-90, 0], [0, 0], 'k-')
ax.set_ylabel(ylabs[i])
axt.set_ylim([-135, 90])
axm.set_ylim([-0.325, 0.325])
axb.set_ylim([-1.5, 1.7])
xp=-78.5
axt.text(xp, 70, 'a)')
axm.text(xp, 0.26, 'b)')
axb.text(xp, 1.4, 'c)')
xp=-43
axt.text(xp, 65, '1979-2004')
axm.text(xp, 0.25, '1988-2011')
axb.text(xp, 1.325, '1988-2011')
plt.savefig('../plots/zonmean_trends.pdf'
, bbox_inches = 'tight', dpi=300)
def plot_trend_maps(datapath):
# Load the CMIP5 data
h5f = h5py.File(datapath + 'cmip5_trends.h5', 'r')
uas_slope_c5 = h5f['uas/1988_2011/c5_uas_trend_1988_2011'][:] * 120
#names2 = h5f['uas/1988_2011/model_names'][:]
h5f.close()
# load in the reanalysis data
h5f = h5py.File(datapath + 'reanalysis_trends.h5', 'r')
slopes = h5f['u10m/1988_2011/rean_u10m_trend_1988_2011'][:] * 120
rean = h5f['u10m/1988_2011/reanalysis_names'][:]
h5f.close()
# load in the CCMP data which despite filename, starts 88
ifile_ccmp = datapath + 'slope_remap_CCMP_198701-201112.nc'
slope_ccmp = cd.loadvar(ifile_ccmp, 'uwnd')
slope_ccmp = slope_ccmp * 120.
slope_ccmp = np.ma.masked_outside(slope_ccmp, -1, 1)
dims = {'lat': np.arange(-89.5, 90.1, 1), 'lon': np.arange(0, 361, 1)}
fig, axa = plt.subplots(8,
2,
sharex=True,
sharey=True,
figsize=(7, 7),
squeeze=True)
fig.subplots_adjust(right=0.63, hspace=0.1, wspace=0.05)
vmin = -1
vmax = 1
ncols = 11
cmap_anom = brewer2mpl.get_map('RdBu', 'diverging', ncols,
reverse=True).mpl_colormap
cmap_anom = discrete_cmap(ncols, cmap_anom)
m =\
Basemap(llcrnrlon=0,llcrnrlat=-90,urcrnrlon=360,urcrnrlat=0,projection='mill')
lons, lats = np.meshgrid(dims['lon'], dims['lat'])
x, y = m(lons, lats)
xpt, ypt = m(20, -88.5)
cot = m.pcolor(x,
y,
slope_ccmp,
vmin=vmin,
vmax=vmax,
cmap=cmap_anom,
ax=axa[0, 0],
rasterized=True)
axa[0, 0].text(xpt, ypt, 'CCMP')
# put on reanalyses
for i, r in enumerate(rean):
m.pcolor(x,
y,
slopes[:, :, i],
vmin=vmin,
vmax=vmax,
cmap=cmap_anom,
ax=axa[i + 1, 0],
rasterized=True)
anom = slopes[:, :, i] - slope_ccmp
anom = np.ma.masked_outside(anom, -1.0, 1.0)
m.pcolor(x,
y,
anom,
vmin=vmin,
vmax=vmax,
cmap=cmap_anom,
ax=axa[i + 1, 1],
rasterized=True)
rmse = np.sqrt(np.mean(anom[0:89, :]**2))
axa[i + 1, 0].text(xpt, ypt, r.upper())
axa[i + 1, 1].text(xpt, ypt, str(np.round(rmse, 2)))
# put on cmip5
m.pcolor(x,
y,
uas_slope_c5.mean(axis=0),
vmin=vmin,
vmax=vmax,
cmap=cmap_anom,
ax=axa[7, 0],
rasterized=True)
anom = uas_slope_c5.mean(axis=0) - slope_ccmp
anom = np.ma.masked_outside(anom, -1.0, 1.0)
m.pcolor(x,
y,
anom,
vmin=vmin,
vmax=vmax,
cmap=cmap_anom,
ax=axa[7, 1],
rasterized=True)
rmse = np.sqrt(np.mean(anom[0:89, :]**2))
c5_25_precentile = np.percentile(uas_slope_c5, 2.5, axis=0)
c5_975_precentile = np.percentile(uas_slope_c5, 97.5, axis=0)
ds = 4 # downsample for stippling
mask = ((slope_ccmp[::ds, ::ds] > c5_975_precentile[::ds, ::ds]) |
(slope_ccmp[::ds, ::ds] < c5_25_precentile[::ds, ::ds]))
x2 = x[::ds, ::ds]
y2 = y[::ds, ::ds]
m.plot(x2[mask],
y2[mask],
'.k',
alpha=0.75,
markersize=0.2,
ax=axa[7, 1],
zorder=1)
axa[7, 0].text(xpt, ypt, 'CMIP5 mean')
axa[7, 1].text(xpt, ypt, str(np.round(rmse, 2)))
for i, ax in enumerate(axa.flatten()):
ax.autoscale(enable=True, axis='both', tight=True)
m.drawcoastlines(linewidth=1.25, ax=ax)
m.fillcontinents(color='0.8', ax=ax, zorder=2)
if i % 2 == 0:
m.drawparallels(np.arange(-80, 81, 20),
labels=[1, 0, 0, 0],
linewidth=0,
ax=ax)
m.drawmeridians(np.arange(0, 360, 90),
labels=[0, 0, 0, 1],
linewidth=0,
yoffset=0.5e6,
ax=axa[7, 0])
m.drawmeridians(np.arange(0, 360, 90),
labels=[0, 0, 0, 1],
linewidth=0,
yoffset=0.5e6,
ax=axa[7, 1])
box = axa[0, 0].get_position()
tl = fig.add_axes(
[box.x0 * 1.1 + box.width * 1., box.y0, 0.02, box.height])
bounds = np.linspace(vmin, vmax, ncols)
plt.colorbar(cot,
cax=tl,
label='m s$^{-1}$\ndecade$^{-1}$',
spacing='proportional',
boundaries=bounds)
fig.delaxes(axa[0, 1])
axa[0, 0].set_title('u10m trends 1988-2011')
plt.savefig('../plots/uas_trend_maps_1988-2011.pdf',
bbox_inches='tight',
dpi=300)
import numpy as np
import scipy as sp
from mpl_toolkits.basemap import Basemap, addcyclic
import matplotlib.pyplot as plt
import matplotlib as mpl
import brewer2mpl
from discrete_cmap import discrete_cmap
from netCDF4 import Dataset, num2date, date2num
plt.ion()
plt.close('all')
font = {'size': 10}
plt.rc('font', **font)
# load in the CCMP data
ifile_ccmp = '/raid/ra40/data/ncs/ccmp/ccmp_slope_199801-201112.nc'
slope_ccmp_uwnd = cd.loadvar(ifile_ccmp, 'uwnd') * 120.
slope_ccmp_wspd = cd.loadvar(ifile_ccmp, 'wspd') * 120.
slope_ccmp_uwnd = np.ma.masked_outside(slope_ccmp_uwnd, -2, 2)
slope_ccmp_wspd = np.ma.masked_outside(slope_ccmp_wspd, -2, 2)
dims = {'lat': np.arange(-89.5, 90.1, 1), 'lon': np.arange(0, 361, 1)}
fig, axa = plt.subplots(2,
1,
sharex=True,
sharey=True,
figsize=(7, 7),
squeeze=True)
fig.subplots_adjust(right=0.825, top=0.65, hspace=0.1, wspace=0.05)
def plot_trend_maps(datapath):
# Load the CMIP5 data
h5f = h5py.File(datapath + 'cmip5_trends.h5','r')
uas_slope_c5 = h5f['uas/1988_2011/c5_uas_trend_1988_2011'][:]*120
#names2 = h5f['uas/1988_2011/model_names'][:]
h5f.close()
# load in the reanalysis data
h5f = h5py.File(datapath + 'reanalysis_trends.h5','r')
slopes = h5f['u10m/1988_2011/rean_u10m_trend_1988_2011'][:]*120
rean = h5f['u10m/1988_2011/reanalysis_names'][:]
h5f.close()
# load in the CCMP data which despite filename, starts 88
ifile_ccmp = datapath + 'slope_remap_CCMP_198701-201112.nc'
slope_ccmp = cd.loadvar(ifile_ccmp, 'uwnd')
slope_ccmp = slope_ccmp*120.
slope_ccmp = np.ma.masked_outside(slope_ccmp, -1,1)
dims = {'lat' : np.arange(-89.5,90.1,1),
'lon' : np.arange(0,361,1)
}
fig, axa = plt.subplots(8,2, sharex=True, sharey=True, figsize=(7,7),
squeeze=True)
fig.subplots_adjust(right=0.63, hspace=0.1, wspace=0.05)
vmin = -1
vmax = 1
ncols = 11
cmap_anom = brewer2mpl.get_map('RdBu', 'diverging', ncols,
reverse=True).mpl_colormap
cmap_anom = discrete_cmap(ncols, cmap_anom)
m =\
Basemap(llcrnrlon=0,llcrnrlat=-90,urcrnrlon=360,urcrnrlat=0,projection='mill')
lons, lats = np.meshgrid(dims['lon'], dims['lat'])
x, y = m(lons, lats)
xpt, ypt = m(20,-88.5)
cot = m.pcolor(x, y, slope_ccmp,vmin=vmin, vmax=vmax, cmap=cmap_anom,
ax=axa[0,0], rasterized=True)
axa[0,0].text(xpt, ypt, 'CCMP')
# put on reanalyses
for i, r in enumerate(rean):
m.pcolor(x, y, slopes[:,:,i],vmin=vmin, vmax=vmax, cmap=cmap_anom,
ax=axa[i+1, 0], rasterized=True)
anom = slopes[:,:,i] - slope_ccmp
anom = np.ma.masked_outside(anom,-1.0, 1.0)
m.pcolor(x, y, anom,vmin=vmin, vmax=vmax,
cmap=cmap_anom, ax=axa[i+1, 1], rasterized=True)
rmse = np.sqrt( np.mean(anom[0:89,:]**2) )
axa[i+1,0].text(xpt, ypt, r.upper())
axa[i+1,1].text(xpt, ypt, str(np.round(rmse,2)))
# put on cmip5
m.pcolor(x, y, uas_slope_c5.mean(axis=0),vmin=vmin, vmax=vmax, cmap=cmap_anom,
ax=axa[7, 0], rasterized=True)
anom = uas_slope_c5.mean(axis=0) - slope_ccmp
anom = np.ma.masked_outside(anom,-1.0, 1.0)
m.pcolor(x, y, anom,vmin=vmin, vmax=vmax,
cmap=cmap_anom, ax=axa[7, 1], rasterized=True)
rmse = np.sqrt( np.mean(anom[0:89,:]**2) )
c5_25_precentile = np.percentile(uas_slope_c5,2.5, axis=0)
c5_975_precentile = np.percentile(uas_slope_c5,97.5, axis=0)
ds = 4 # downsample for stippling
mask = ( (slope_ccmp[::ds,::ds]>c5_975_precentile[::ds,::ds]) |
(slope_ccmp[::ds,::ds]<c5_25_precentile[::ds,::ds]))
x2 = x[::ds,::ds]
y2 = y[::ds,::ds]
m.plot(x2[mask], y2[mask], '.k', alpha=0.75,
markersize=0.2, ax=axa[7,1], zorder=1)
axa[7,0].text(xpt, ypt, 'CMIP5 mean')
axa[7,1].text(xpt, ypt, str(np.round(rmse,2)))
for i, ax in enumerate(axa.flatten()):
ax.autoscale(enable=True, axis='both', tight=True)
m.drawcoastlines(linewidth=1.25, ax=ax)
m.fillcontinents(color='0.8',ax=ax, zorder=2)
if i%2 ==0:
m.drawparallels(np.arange(-80,81,20),labels=[1,0,0,0], linewidth=0,
ax=ax)
m.drawmeridians(np.arange(0,360,90),labels=[0,0,0,1],
linewidth=0,yoffset=0.5e6, ax=axa[7,0])
m.drawmeridians(np.arange(0,360,90),labels=[0,0,0,1],
linewidth=0,yoffset=0.5e6, ax=axa[7,1])
box = axa[0,0].get_position()
tl = fig.add_axes([box.x0*1.1 + box.width * 1., box.y0, 0.02, box.height])
bounds = np.linspace(vmin, vmax, ncols)
plt.colorbar(cot, cax=tl, label='m s$^{-1}$\ndecade$^{-1}$',
spacing='proportional', boundaries=bounds)
fig.delaxes(axa[0,1])
axa[0,0].set_title('u10m trends 1988-2011')
plt.savefig('../plots/uas_trend_maps_1988-2011.pdf',
bbox_inches='tight', dpi=300)
def plot_zonmean_trends(datapath):
# Load the CMIP5 data
h5f = h5py.File(datapath + 'cmip5_trends.h5', 'r')
psl_slope_c5 = h5f['psl/1979_2004/c5_psl_trend_1979_2004'][:] * 120
uas_slope_c5_88 = h5f['uas/1988_2011/c5_uas_trend_1988_2011'][:] * 120
uflx_slope_c5_88 = h5f[
'tauu/1988_2011/c5_tauu_trend_1988_2011'][:] * 120 * 100
h5f.close()
# load in the reanlaysis data
rlc = ['k', 'y', 'g', 'b', 'c', 'm']
h5f = h5py.File(datapath + 'reanalysis_trends.h5', 'r')
psl_slope_rean = h5f['slp/1979_2004/rean_slp_trend_1979_2004'][:] * 120
uas_slope_rean_88 = h5f['u10m/1988_2011/rean_u10m_trend_1988_2011'][:] * 120
uflx_slope_rean_88 = h5f[
'uflx/1988_2011/rean_uflx_trend_1988_2011'][:] * 120 * 100
rean = h5f['uflx/1988_2011/reanalysis_names'][:]
h5f.close()
# The HadSLPr2 data is in the rean hdf
psl_slope_hadslp = psl_slope_rean[:, :, 6]
# load in the CCMP data
ifile_ccmp = datapath + 'slope_remap_CCMP_198701-201112.nc' #despite name, starts 88
slope_ccmp = cd.loadvar(ifile_ccmp, 'uwnd') * 120.
slope_ccmp = np.ma.masked_outside(slope_ccmp, -1, 1)
uflx_slope_ccmp = cd.loadvar(ifile_ccmp, 'upstr') * 120.
uflx_slope_ccmp = np.ma.masked_outside(uflx_slope_ccmp, -15, 15)
uflx_slope_ccmp = uflx_slope_ccmp * 1.2 * 1.4e-3 * 120
# load in the Marshall SAM data
df = pd.read_csv(datapath + 'marshall_sam.csv',
index_col=0,
parse_dates=True)
df = pt.time_lim(df, pd.datetime(1979, 1, 1), pd.datetime(2004, 12, 31))
dft = pt.ols(df, units='decades')
dims = {'lat': np.arange(-89.5, 89.6, 1), 'lon': np.arange(0, 360, 1)}
fig, (axt, axm, axb) = plt.subplots(3, 1, sharex=True, figsize=(7, 7))
fig.subplots_adjust(right=0.5, hspace=0.05)
modlatplot(psl_slope_c5.mean(axis=2), axt)
axt.plot(dims['lat'],
psl_slope_hadslp.mean(axis=(1)),
'k--',
linewidth=3,
label='HadSLP2r')
axt.plot(dims['lat'],
slope_ccmp.mean(axis=1) * np.nan,
'k-.',
linewidth=3,
label='CCMP')
# put on the trends in marshall
axt.plot(-40,
dft.slp40.slope * 100.0,
'kx',
markersize=15,
markeredgewidth=3,
zorder=10)
axt.plot(-65,
dft.slp65.slope * 100.0,
'kx',
markersize=15,
markeredgewidth=3,
label='Marshall',
zorder=10)
modlatplot(uas_slope_c5_88.mean(axis=2), axm)
axm.plot(dims['lat'],
slope_ccmp.mean(axis=1),
'k-.',
linewidth=3,
zorder=9)
modlatplot(uflx_slope_c5_88.mean(axis=2), axb)
axb.plot(dims['lat'],
uflx_slope_ccmp.mean(axis=1),
'k-.',
linewidth=3,
zorder=9)
for i, r in enumerate(rean):
axt.plot(dims['lat'],
psl_slope_rean[:, :, i].mean(axis=1),
linestyle='-',
color=rlc[i],
linewidth=2,
label=r)
axm.plot(dims['lat'],
uas_slope_rean_88[:, :, i].mean(axis=1),
linestyle='-',
color=rlc[i],
linewidth=2)
axb.plot(dims['lat'],
uflx_slope_rean_88[:, :, i].mean(axis=1),
linestyle='-',
color=rlc[i],
linewidth=2)
axt.legend(bbox_to_anchor=(1.6, 1),
frameon=False,
numpoints=1,
fontsize=12)
axb.set_xlabel('Latitude')
ylabs = [
'Pa decade$^{-1}$', 'm s$^{-1}$ decade$^{-1}$',
r'Pa decade$^{-1}\times 10^{-2}$'
]
for i, ax in enumerate((axt, axm, axb)):
ax.set_xlim([-80, -20])
ax.plot([-90, 0], [0, 0], 'k-')
ax.set_ylabel(ylabs[i])
axt.set_ylim([-135, 90])
axm.set_ylim([-0.325, 0.325])
axb.set_ylim([-1.5, 1.7])
xp = -78.5
axt.text(xp, 70, 'a)')
axm.text(xp, 0.26, 'b)')
axb.text(xp, 1.4, 'c)')
xp = -43
axt.text(xp, 65, '1979-2004')
axm.text(xp, 0.25, '1988-2011')
axb.text(xp, 1.325, '1988-2011')
plt.savefig('../plots/zonmean_trends.pdf', bbox_inches='tight', dpi=300)