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compare two cases over the globe assuming they are on lat/lon grid at same resolution
configured for the GMD paper Fixed SST, 25Tg/year per region, 1979-1989
import sys
print(sys.version)
%matplotlib inline
%run -i ~/Python/pjr3def setfig3b1x1 ():
"""
return fig and axes for a single panel figure
"""
plotproj = ccrs.Mollweide()
plotproj._threshold /= 100.
fig, axes = plt.subplots(ncols=1,
gridspec_kw={'width_ratios': [1]},
subplot_kw={'projection': plotproj},
figsize=(6,4.1),
)
fig.set_dpi(300.0)
return fig, axes;
def pltllbox(xri, yri,ax=None):
if ax is None:
ax = plt.gca()
if xri[1] < xri[0]:
xri[1] += 360.
regcx = [xri[0],xri[1],xri[1],xri[0],xri[0]]
regcy = [yri[0],yri[0],yri[1],yri[1],yri[0]]
ax.plot(regcx,regcy,color='red',transform=ccrs.PlateCarree())def xr_getvar_sl(VN, DS1, method='surface', verbose=False):
""" get a field from a netCDF file.
If it is a multi-level field, do something useful to provide single level information
This function assumes that the x-xoordinate increases monotonically
"""
Var1 = xr_getvar(VN,DS1)
#V1 = Var1.mean(dim='time',keep_attrs=True)
dimlist = Var1.dims
#print('dimlist',dimlist)
if 'model_level_number' in dimlist:
level_height = xr_getvar('level_height',DS1)
sigma = xr_getvar('sigma',DS1)
#print('sigma',sigma.values)
surface_altitude = xr_getvar('surface_altitude',DS1)
altitude = level_height + (sigma * surface_altitude)
altitude.attrs['long_name'] = 'altitude above mean sea-level'
#print('altitude',altitude)
if method == 'surface':
print('method:surface')
V1 = Var1.isel(model_level_number=0)
V1.attrs['long_name'] = V1.attrs['long_name'] + ' (surface level)'
elif method == 'maxb850':
i = 0
j = 0
#print('method:max below 850')
dz1 = altitude - surface_altitude # height above surface
#print('altitude',altitude[:,i,j].values)
#print('surface_altitude',surface_altitude[i,j].values,surface_altitude)
#print('dz1(i,j)',dz1[:,i,j].values)
dz2 = (sigma - 1)*surface_altitude + level_height # height above sea level
#print('dz2(i,j)',dz2[:,i,j].values)
pmb = 850.
psmb = 1000.
scaleheight = 8.4e3
altmb = -np.log(pmb/psmb)*scaleheight
#print('altmb is ', altmb)
V1 = Var1.copy()
#V1.values = dz1.values ### for the moment, overwrite field with height above surface height
#print('V1',V1[:,i,j].values)
#V2 = V1.where(dz1 <= altmb+50.)
V2 = V1.where(altitude <= altmb+50.)
V3 = V2.max(dim='model_level_number')
#V3.attrs['long_name'] = 'max value below 850hPa of '+V2.attrs['long_name']
V3.attrs['long_name'] = 'max value below 850hPa of '+V2.name
V1 = V3
#print('V3',V3[i,j].values)
#1./0.
else:
V1 = Var1
return V1Vdict = {'net_ToA_LW_W_m2':'toa_outgoing_longwave_flux'
,'net_ToA_SW_W_m2':'FSNT'
,'net_ToA_SW_Clear_W_m2':'FSNTC'
,'AOD_550nm':'AOD'
,'LWP_kg_m2':'TGCLDLWP'
,'p_surface_Pa':'PS'
,'T_surface_K':'TS'
,'precip_rate_kg_m2_sec':'PRECT'
,'PBL_depth_metres':'PBLH'
,'cloudtop_r_e_microns':'REPJR'
,'cloud_fraction':'CLOUD'
,'Outgoing_SW_Clear_W_m2':'MFSUTC'
}
def xr_llhplot2 (xrVar, cbar='default', plotproj=None, ax=None, cax=None, fig=None,
ylabels=False, clevs=None, cmap=None, title=None, cbartitle=None):
"""xr_llhplot xarray lat lon horizontal plot
"""
#print(' entering xr_llhplot', xrVar)
lon=xrVar['lon'].values
lat=xrVar['lat'].values
xv,yv=np.meshgrid(lon,lat)
data_regridded = xrVar.values
#print('aaa',data_regridded.shape, xv.shape, yv.shape)
df = data_regridded.flatten()
dsub = df[np.isfinite(df)] # ignore NaN
zmax = dsub.max()
zmin = dsub.min()
#print('masked interpolated range',zmin,zmax)
dataproj=ccrs.PlateCarree() # data is always assumed to be lat/lon
if ylabels is None: ylabels = True
if clevs is None:
clevs = findNiceContours(np.array([zmin,zmax]),nlevs=10)
#print('clevs',clevs)
if cmap is None:
#print('aaa, grabbing cmap default')
#cmap = mpl.cm.get_cmap()
cmap = plt.get_cmap()
#print('bbb',cmap.N)
#print('cmap',cmap)
extend = 'both'
norm = mpl.colors.BoundaryNorm(clevs,cmap.N,extend=extend)
#print('norm',norm(clevs))
clat = (lat.min()+lat.max())/2.
clon = (lon.min()+lon.max())/2.
if plotproj is None:
plotproj = ccrs.PlateCarree()
plotproj = ccrs.Mollweide()
# if no ax argument, could get current axis, or create it
if ax is None:
#print('grab current axis')
#ax = plt.gca()
ax = plt.axes(projection=plotproj)
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=False,
linewidth=2, color='gray', alpha=0.5)
pl = ax.contourf(xv, yv, data_regridded, levels=clevs, # vmin=zmin, vmax=zmax,
norm=norm, cmap=cmap,
extend=extend, transform=ccrs.PlateCarree())
gl.left_labels=ylabels
gl.right_labels=ylabels
ax.coastlines(linewidth=1,color='blue')
## Find the location of the main plot axes
## has to be done after some plotting is done in projection space
posn = ax.get_position()
# print some registration marks to help in lining up figures
ax2 = fig.add_axes([0,0,0.1,0.1])
ax2.set_position([posn.x0-0.005, posn.y0-0.005, posn.width+0.01, posn.height+0.01])
ax2.patch.set_alpha(0.0)
ax2.scatter([0,0,1,1], [0,1,0,1], c="r", s=100)
ax2.set_axis_off()
ax2.set_xlim([0,1])
ax2.set_ylim([0,1])
if not title is None:
#ax.set_title(title)
ax2.text(0.01,0.9,title)
# Add colorbar to plot
if cbartitle is None:
cbartitle = xrVar.long_name
if cbar == 'default':
if cax is not None:
cax = ax
else:
# create an colorbar axis
cax = fig.add_axes([0,0,0.1,0.1])
## Adjust the positioning and orientation of the colorbar
cax.set_position([posn.x0, posn.y0-0.07, posn.width, 0.05])
cb = plt.colorbar(
pl, orientation='horizontal',ticks=clevs,cax=cax,
label='%s (%s)'%(cbartitle, xrVar.units),
)
cb.ax.tick_params(labelsize=11)
#cb.ax.set_yticklabels(['{:.0f}'.format(x) for x in clevs])#, fontsize=16, weight='bold')
if len(clevs) > 15:
clevs2 = findNiceContours(clevs,nlevs = 10, rmClev=0.,sym=True)
cb.set_ticks(clevs2)
cb.set_ticklabels(clevs2)
#cb.ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))
cb.ax.set_xticklabels(["{:.0f}".format(i) for i in clevs2]) # set ticks of your format
returndef pltfld(DV, titled, fname=None):
cbartitle = DV.long_name
if DV.min().values == DV.max().values:
print('constant field skipping plot ')
else:
dlev_rng = {'CDNUMC':np.array([0.,3.e11])/2.,'FSNT':np.array([-45.,45.]),
'TGCLDLWP':np.array([-50.,50.]),'PRECL':np.array([-1.,1.]),
#'TGCLDLWP':np.array([-80.,80.]),'PRECL':np.array([-1.,1.]),
'PRECT':np.array([-5.,5.]),'SWCF':np.array([-45.,45.]),
'CLDLOW':np.array([-10.,10.]),'CLOUD':np.array([-10.,10.]),
}
if DV.name in dlev_rng:
dlevs = findNiceContours(dlev_rng[DV.name],nlevs = 15,rmClev=0.,sym=True)
else:
dlevs = findNiceContours(np.array([DV.min().values,DV.max().values]),nlevs = 15, rmClev=0.,sym=True)
#dlevs = [-5.,-2.,-1.,-0.5,-0.2,-0.1,0.1,0.2,0.5,1.,2.,5.]
#print('xxx',dlevs)
dmap = diverge_map()
plconf = '3-1x1'
#plconf = '1x3'
# good setup for 1 row of 3 columns
# good setup for 3 rows of 1 columns
if plconf == '3-1x1':
fig, axes = setfig3b1x1()
xr_llhplot2(DV, fig=fig, ax=axes,clevs=dlevs,cmap=dmap,title=titled, cbartitle=cbartitle)
pltllbox([-150.,-110.],[0.,30.],ax=axes)
pltllbox([-110.,-70.],[-30.,0.],ax=axes)
pltllbox([-25.,15.],[-30.,0.],ax=axes)
if fname is not None:
print('fname',fname)
plt.savefig(fname,dpi=300,transparent=True)
plt.show()
pref_fn = 'UKESM'
def make_ind1 (REG_ID, Varname, filetype=None):
case_start1 = '~/NetCDF_Files/UKESM1_data/'+REG_ID+'_20450101_20490101_mean_'
case_start1 = '~/NetCDF_Files/UKESM1_data_v2/Coupled_50Tg/'+REG_ID+'_coupled_50Tgy_20410101_20500101_mean_'
case_end1 = ".nc"
fstring1 ='%s%s%s'
pref1=REG_ID+'_UKESM1_50Tgpy_Cpld'
if filetype == 'Fixed_SST':
# fixed SST simulations
case_start1 = '~/NetCDF_Files/UKESM1_data/'+REG_ID+'_AtmosOnly_19840101_19880101_mean_'
pref1=REG_ID+'_50Tgpy_FixedSST'
case_start1 = '~/NetCDF_Files/UKESM1_data_v2/AtmosOnly_25Tg_1979-1989/'+REG_ID+'_AtmosOnly_25Tgy_19790101_19890101_mean_'
pref1=REG_ID+'_25Tgpy_FixedSST'
#case_start1 = '~/NetCDF_Files/UKESM1_data_v2/AtmosOnly_50Tg_1979-1989/'+REG_ID+'_AtmosOnly_50Tgy_19790101_19890101_mean_'
#pref1=REG_ID+'_50Tgpy_FixedSST'
case_end1 = ".nc"
fstring1 ='%s%s%s'
pref1=REG_ID+'_UKESM1_25Tgpy_Cpld'
ind1 = fstring1 % (case_start1,Varname,case_end1)
return ind1
def make_ind2(REG_ID, Varname, filetype='Fixed_SST'):
case_start2 = '~/NetCDF_Files/UKESM1_data/CTL_20450101_20490101_mean_'
case_start2 = '~/NetCDF_Files/UKESM1_data_v2/Coupled_Control/CTL_coupled_20410101_20500101_mean_'
case_end2 = ".nc"
pref2='UKESM1_control'
fstring2 ='%s%s%s'
if filetype == 'Fixed_SST':
case_start2 = '~/NetCDF_Files/UKESM1_data/CTL_AtmosOnly_19840101_19880101_mean_'
case_start2 = '~/NetCDF_Files/UKESM1_data_v2/AtmosOnly_Control_1979-1989/'+'CTL_AtmosOnly_19790101_19890101_mean_'
case_end2 = ".nc"
pref2='Control'
fstring2 ='%s%s%s'
ind2 = fstring2 % (case_start2,Varname,case_end2)
return ind2
Varname='LWP_kg_m2'
REG_ID = 'R1_NEP'
filetype = 'Fixed_SST'
#filetype = 'Coupled'
ind1 = make_ind1(REG_ID,Varname,filetype)
print('example string used for file 1 open',ind1)
ind2 = make_ind2(REG_ID,Varname,filetype)
difftitle='Syn R1+R2+R3(each@25Tgpyr)-Ctl'
difftitle=''
print('example string used for file 2 open',ind2)
# accumulate differences over 3 areas
#Varlist = np.array(['p_surface_Pa'])
#Varlist = np.array(['Outgoing_SW_Clear_W_m2','p_surface_Pa','T_surface_K','precip_rate_kg_m2_sec','PBL_depth_metres','cloudtop_r_e_microns','AOD_550nm','LWP_kg_m2','net_ToA_LW_W_m2','net_ToA_SW_W_m2'])
Varlist = np.array(['Outgoing_SW_Clear_W_m2','precip_rate_kg_m2_sec','PBL_depth_metres','cloudtop_r_e_microns','AOD_550nm','LWP_kg_m2','net_ToA_LW_W_m2','net_ToA_SW_W_m2',
'net_ToA_SW_Clear_W_m2','cloud_fraction'])
Varlist = np.array(['net_ToA_SW_Clear_W_m2','net_ToA_SW_W_m2'])
#Varlist = np.array(['net_ToA_SW_Clear_W_m2','net_ToA_SW_W_m2'])
#Varlist = np.array(['AOD_550nm','LWP_kg_m2','net_ToA_LW_W_m2','net_ToA_SW_W_m2'])
#Varlist = np.array(['T_surface_K'])
Varlist = np.array(['LWP_kg_m2','net_ToA_SW_Clear_W_m2','net_ToA_SW_W_m2','cloud_fraction'])
Varlist = np.array(['cloud_fraction'])
FSNT1 = None
FSNT2 = None
FSNTC1 = None
FSNTC2 = None
# specify regions (assume lon always specified as west, then east limit)
xreg = np.array([[-150.,-110.],[-110,-70],[-25.,15.],[170.,-120.],[-170.,-90.]])%360.
yreg = np.array([[0.,30.], [-30.,0.], [-30.,0.], [30.,50.], [-50.,-30.] ])
namereg = ['NEP','SEP','SEA','NP','SP']
#xreg = [[0.,360.]]
#yreg = [[-90.,91.]]
reglist = np.array(['R1_NEP','R2_SEP','R3_SEA'])
filetype = None
filetype = 'Fixed_SST'
#filetype = 'Coupled'
for Varname in Varlist:
print()
print('-------------------------------'+Varname)
nreg = 0 # is it the start of the region summation
for REG_ID in reglist:
#ind1 = fstring1 % (case_start1,Varname,case_end1)
ind1 = make_ind1(REG_ID,Varname,filetype)
print('ind1 opening',ind1)
DS1 = xr.open_mfdataset(ind1)
#print('xxx',DS1.time_bnds.values)
DS1 = fix_UKMO_ds(ind1, DS1)
#print('DS1.lon',DS1.lon.values)
#DS1 = center_time(DS1)
VN = Vdict[Varname]
print('VN is ',VN)
V1 = xr_getvar_sl(VN,DS1,method='maxb850')
#print('V1',V1)
ind2 = make_ind2(REG_ID,Varname,filetype)
print('opening ind2',ind2)
#DS2 = xr.open_mfdataset(ind2)
DS2 = xr.open_mfdataset(ind2)
DS2 = fix_UKMO_ds(ind2, DS2)
V2 = xr_getvar_sl(VN,DS2,method='maxb850')
DV = V1-V2
print('DV range', DV.min().values, DV.max().values)
weights = None
if 'area' in DS1:
area = DS1['area']
elif 'area' in DS2:
area = DS2['area']
else:
print('calculating areas')
lat = V1['lat'].values
lon = V1['lon'].values
area = make_fvarea(lon,lat)
weights = V1.copy()
weights.data =area
weights.attrs['units']='steradians'
print(Varname, V1.attrs['long_name'],'Range V1 and V2 ',V1.min().values, V1.max().values, V2.min().values, V2.max().values)
V1A = V1.weighted(weights).mean()
sV1A = ' (%5.2f)' % V1A
V2A = V2.weighted(weights).mean()
sV2A = ' (%5.2f)' % V2A
DVA = V1A-V2A
sDVA = ' (%5.2f)' % DVA
print('nreg',nreg,'area avgs (V1A, V2A, DVA) %5.2f' % (V1A.values), '%5.2f' % (V2A.values),' Delta %5.2f' % (DVA.values))
if nreg == 0:
V1S = V1
V2S = V2
DVS = DV
nreg = 1
else:
V1S = V1S + V1
V2S = V2S +V2
DVS = DVS + DV
nreg = nreg+1
print(' all regions summed')
#V1S = V1S/nreg
#V2S = V2S/nreg
DVA = DVS.weighted(weights).mean()
sDVA = ' ({:5.2f}{:s})'.format(DVA.values, DVA.units)
print('yyy', sDVA)
print('summed area Delta %5.2f' % (DVA.values))
fname = pref_fn+'_'+Varname+'_'+DV.name+'-D.pdf'
pltfld(DVS, difftitle+sDVA,fname)
if VN == 'FSNT':
print('xxx')
FSNT1=V1S
FSNT2=V2S
elif VN == 'FSNTC':
print('yyy')
FSNTC1=V1S
FSNTC2=V2S
print('field processing complete')
if ((FSNT1 is None) or (FSNTC1 is None)):
print ('fields for SWCRE not requested')
else:
SWCRE1 = FSNT1-FSNTC1
SWCRE1.attrs['long_name'] = 'TOA shortwave CRE'
SWCRE1 = SWCRE1.rename('SWCRE')
SWCRE2 = FSNT2-FSNTC2
SWCRE2.attrs['long_name'] = 'TOA shortwave CRE'
SWCRE2 = SWCRE2.rename('SWCRE')
DSWCRE = SWCRE1-SWCRE2
DSWCREA = DSWCRE.weighted(weights).mean()
#sDSWCREA = ' (%5.2f %s)' % DSWCREA, DSWCREA.units
sDSWCREA = ' ({:5.2f}{:s})'.format(DSWCREA.values, DSWCREA.units)
print('xxx',sDSWCREA)
fname = pref_fn+'_'+Varname+'_'+'SWCRE'+'-D.pdf'
pltfld(DSWCRE, difftitle+sDSWCREA,fname)sDSWCREA = ' ({:5.2f}{:s})'.format(DSWCREA.values, DSWCREA.units)
print('xxx',sDSWCREA)