def plotter(pdata, colormax=1, colormin=-999, title=''):
lon = ncread('/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/All-Hist/mon/tas/item3236_monthly_mean_a011_2006-01_2016-12.nc', 'longitude0')
lat = ncread('/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/All-Hist/mon/tas/item3236_monthly_mean_a011_2006-01_2016-12.nc', 'latitude0')
#lat = ncread('/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/Plus15-Future_LCO2/day/ua/item15201_daily_mean_a00b_2090-01_2100-12.nc', 'latitude1')
if colormin == -999:
colormin = -colormax
pdata, lon = shiftgrid(180., pdata, lon, start=False)
pdata, lon = addcyclic(pdata, lon)
meshlon, meshlat = np.meshgrid(lon, lat)
m = Basemap(projection='cyl', llcrnrlat=-90, urcrnrlat=90,
llcrnrlon=-180, urcrnrlon=180, resolution='c')
m.drawcoastlines()
m.drawmapboundary()
x, y = m(meshlon, meshlat)
mycmap2 = plt.cm.YlOrRd(np.arange(256))
mycmap1 = plt.cm.Blues_r(np.arange(256))
my_cmap = np.concatenate((mycmap1, mycmap2), axis=0)
my_cmap[230:282, :] = 1
if precip == 'yes':
my_cmap = my_cmap[::-1]
newcmap = mpl.colors.LinearSegmentedColormap.from_list("newjet", my_cmap)
ctrs = np.linspace(colormin, colormax, 17)
plot = m.contourf(x, y, pdata, ctrs,
cmap=newcmap, vmin=np.min(ctrs), vmax=np.max(ctrs),
extend='both')
plt.title(title, y=1)
plt.show()
return plot
def stat_rossC(u):
'''
This treatment makes the regions of reflection (beta<0 and u_m>0)
and regions of absorption (beta>0 and u_m<0) indistinguishable.
These regions of absorption can be identified separately by
plotting the easterlies.
'''
from netcdfread import ncread
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/CAM4-2degree/All-Hist/mon/va/va_Amon_CAM4-2degree_All-Hist_est1_v1-0_ens0000_200601-201512.nc',
'lon')
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/CAM4-2degree/All-Hist/mon/va/va_Amon_CAM4-2degree_All-Hist_est1_v1-0_ens0000_200601-201512.nc',
'lat')
meshlon, meshlat = np.meshgrid(lon, lat)
phi = meshlat * np.pi / 180
cs_phi = np.cos(phi)
dphi = 1.25 * np.pi / 180
omega = 7.29e-5
a = 6.371e6
u_m = u / (a * cs_phi)
u_1opp = np.gradient(u_m * cs_phi**2, dphi, axis=0) / cs_phi
u_2opp = np.gradient(u_1opp, dphi, axis=0) / cs_phi
beta = (2 * omega - u_2opp) * cs_phi**2 / a
ks = np.sqrt(a * beta / u_m)
return ks
def maplot(pdata, colormax=1, colormin=-999, mask='no', title='', precip='no'):
"""
Plots input grid with map of world overlaid
Parameters
----------
plotdata: array
data being plotted
title: str
optional title
"""
from mpl_toolkits.basemap import Basemap, shiftgrid, addcyclic
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from netcdfread import ncread
if colormin == -999:
colormin = -colormax
lon = ncread('/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/All-Hist/mon/tas/item3236_monthly_mean_a011_2006-01_2016-12.nc', 'longitude0')
lat = ncread('/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/All-Hist/mon/tas/item3236_monthly_mean_a011_2006-01_2016-12.nc', 'latitude0')
#lat = ncread('/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/Plus15-Future_LCO2/day/ua/item15201_daily_mean_a00b_2090-01_2100-12.nc', 'latitude1')
plt.figure()
if mask == 'yes':
lsm = ncread('/home/bakerh/Documents/DPhil/CPDN/\
Weather-at-Home_ancilmaker-master/lsm_n96_add.nc', 'lsm')[0, 0, :]
pdata = np.ma.masked_array(pdata, mask=np.logical_not(lsm))
pdata, lon = shiftgrid(180., pdata, lon, start=False)
pdata, lon = addcyclic(pdata, lon)
meshlon, meshlat = np.meshgrid(lon, lat)
m = Basemap(projection='cyl', llcrnrlat=-90, urcrnrlat=90,
llcrnrlon=-180, urcrnrlon=180, resolution='c')
m.drawcoastlines()
m.drawmapboundary()
x, y = m(meshlon, meshlat)
mycmap2 = plt.cm.YlOrRd(np.arange(256))
mycmap1 = plt.cm.Blues_r(np.arange(256))
my_cmap = np.concatenate((mycmap1, mycmap2), axis=0)
my_cmap[230:282, :] = 1
if precip == 'yes':
my_cmap = my_cmap[::-1]
newcmap = mpl.colors.LinearSegmentedColormap.from_list("newjet", my_cmap)
ctrs = np.linspace(colormin, colormax, 17)
plot = m.contourf(x, y, pdata, ctrs,
cmap=newcmap, vmin=np.min(ctrs), vmax=np.max(ctrs),
extend='both')
b = plt.colorbar(plot, orientation='horizontal',
aspect=50, shrink=0.75, spacing='proportional')
b.set_label(label=r'pr (mm day$^{-1}$)')
parallels = m.drawparallels(np.arange(-90., 91., 15.))
meridians = m.drawmeridians(np.arange(-180., 181., 30))
m.drawparallels(parallels, labels=[True, True, True, True])
m.drawmeridians(meridians, labels=[True, True, True, True])
plt.title(title, y=1.08)
plt.show()
def regressnao(eof):
from scipy import stats
lat = np.arange(80, 18.75, -1.25)
lon = np.linspace(-90, 39.375, 70)
both = compare('item16222_monthly_mean')
lst = dbase()
anom = np.zeros((len(both), 145, 192))
NAOinds = np.zeros((2, len(both)))
for i, item in enumerate(both):
anom[i, :] = ncread(
'/network/aopp/hera/mad/bakerh/RPM/SSTpatches/sst_HadOIBl_bc_N96_clim_pert'
+ lst[item] + '_c040926.nc', 'SST'
)[0, :] - ncread(
'/home/bakerh/Documents/DPhil/CPDN/RPM/SSTfiles/sst_HadOIBl_bc_N96_clim_c040926_25months.nc',
'SST_cpl')[0, :]
mslp_control = ncread(
'/network/aopp/hera/mad/bakerh/RPM/cpdn_extract_scripts-master/extracted_data/batch_577/atmos/item16222_monthly_mean/item16222_monthly_mean_'
+ both[i] + '_1999-01_2000-12.nc',
'item16222_monthly_mean')[:, 0, :] / 100
mslp = ncread(
'/network/aopp/hera/mad/bakerh/RPM/cpdn_extract_scripts-master/extracted_data/batch_578/atmos/item16222_monthly_mean/item16222_monthly_mean_'
+ both[i] + '_1999-01_2000-12.nc',
'item16222_monthly_mean')[:, 0, :] / 100
mslp_cw, mslp_cs = nao_region_hadam3p(mslp_control)
mslp_w, mslp_s = nao_region_hadam3p(mslp)
nao_cw = eof_response(np.expand_dims(mslp_cw, axis=0), eof[0], lat,
lon)
nao_cs = eof_response(np.expand_dims(mslp_cs, axis=0), eof[1], lat,
lon)
nao_w = eof_response(np.expand_dims(mslp_w, axis=0), eof[0], lat, lon)
nao_s = eof_response(np.expand_dims(mslp_s, axis=0), eof[1], lat, lon)
NAOinds[0, i] = nao_w - nao_cw
NAOinds[1, i] = nao_s - nao_cs
print('Done: ' + str(i + 1) + ' out of ' + str(len(both)))
anom = anom[:, ::-1, :]
regmap_smoothed = np.zeros((2, 145, 192))
regmap_smoothed_sig = np.zeros((2, 145, 192))
regmap_smoothed_sig[:] = np.NAN
for i in range(99):
for j in range(192):
for a in range(2):
regmap_smoothed[a, i + 23, j] = np.cov(
anom[:, i + 23, j],
NAOinds[a, :])[0, 1] * 3 / (4 * 6371**2 * 1.25 * 1.875 *
(np.pi / 180)**2)
r = np.cov(anom[:, i + 23, j], NAOinds[a, :])[0, 1] / np.sqrt(
np.cov(anom[:, i + 23, j], NAOinds[a, :])[1, 1] *
np.cov(anom[:, i + 23, j], NAOinds[a, :])[0, 0])
t = r * np.sqrt((len(both) - 2) / (1 - r**2))
p = 1 - stats.norm.cdf(np.abs(t))
sig = np.greater_equal(5, p * 100 * 2).astype(int)
if sig == 1:
regmap_smoothed_sig[a, i + 23,
j] = regmap_smoothed[a, i + 23, j]
return regmap_smoothed, regmap_smoothed_sig
def maplotM(pdata, colormax=1, mask='no', title='', precip='no'):
"""
Plots input grid with map of world overlaid
Parameters
----------
plotdata: array
data being plotted
title: str
optional title
"""
from mpl_toolkits.basemap import Basemap, shiftgrid
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from netcdfread import ncread
lat = ncread('/network/aopp/hera/mad/bakerh/HAPPI/MIROC5/All-Hist/day/tas/tas_Aday_MIROC5_All-Hist_est1_v2-0_run001_20060101-20161231.nc','lat')
lon = np.arange(0, 360, 360/256)
plt.figure()
if mask == 'yes':
lsm = ncread('/home/bakerh/Documents/DPhil/Python/lsm_n72.nc', 'lsm')
pdata = np.ma.masked_array(pdata, mask=np.logical_not(lsm))
pdata, lon = shiftgrid(180., pdata, lon, start=False)
meshlon, meshlat = np.meshgrid(lon, lat)
m = Basemap(projection='cyl', llcrnrlat=-90, urcrnrlat=90,
llcrnrlon=-180, urcrnrlon=180, resolution='c')
m.drawcoastlines()
m.drawmapboundary()
x, y = m(meshlon, meshlat)
mycmap2 = plt.cm.YlOrRd(np.arange(256))
mycmap1 = plt.cm.Blues_r(np.arange(256))
my_cmap = np.concatenate((mycmap1, mycmap2), axis=0)
my_cmap[230:282, :] = 1
if precip == 'yes':
my_cmap = my_cmap[::-1]
newcmap = mpl.colors.LinearSegmentedColormap.from_list("newjet", my_cmap)
ctrs = np.linspace(-colormax, colormax, 17)
plot = m.contourf(x, y, pdata, ctrs,
cmap=newcmap, vmin=np.min(ctrs), vmax=np.max(ctrs),
extend='both')
b = plt.colorbar(plot, orientation='horizontal',
aspect=50, shrink=0.75, spacing='proportional')
b.set_label(label=r'-$\mathbf{v}_\chi\cdot\nabla\zeta$ (10$^{-12}$ s$^{-2}$)')
parallels = m.drawparallels(np.arange(-90., 91., 15.))
meridians = m.drawmeridians(np.arange(-180., 181., 30))
m.drawparallels(parallels, labels=[True, True, True, True])
m.drawmeridians(meridians, labels=[True, True, True, True])
plt.title(title, y=1.08)
plt.show()
def sphr2cart(grid):
from scipy import interpolate
import numpy as np
from netcdfread import ncread
r = 6.371e6
x_tot = 2*np.pi * r * np.cos(60*np.pi/180)
lon = ncread('/network/aopp/hera/mad/bakerh/HAPPI/batch_518/atmos/item3236_monthly_mean/item3236_monthly_mean_a011_2006-01_2016-12.nc', 'longitude0')
lat = ncread('/network/aopp/hera/mad/bakerh/HAPPI/batch_518/atmos/item3236_monthly_mean/item3236_monthly_mean_a011_2006-01_2016-12.nc', 'latitude0')
lon_r = np.radians(lon)
lat_r = np.radians(lat)
meshlon, meshlat = np.meshgrid(lon_r, lat_r)
meshx = r * meshlon / 2
meshy = r * meshlat
f = interpolate.interp2d(lon_r, lat_r, grid)
x = meshx[0, :]
y = meshy[:, 0]
grid_cart = f(x*lon_r.max()/x.max(), y*lat_r.max()/y.max())
return x, y, grid_cart
def sens():
import numpy as np
from netcdfread import ncread
import glob
a = sorted(
glob.glob('/network/aopp/hera/mad/bakerh/BTmodel_COR/main/sens/*'))
v = np.zeros((36, 26, 64, 128))
for i in range(36):
v[i] = ncread(a[i], 'V')
return v
def jet_clim_indices():
import glob
from netcdfread import ncread
a = glob.glob(
'/network/aopp/hera/mad/bakerh/RPM/cpdn_extract_scripts-master/extracted_data/batch_577/atmos/item15201_daily_mean/*'
)
lat = ncread(
'/network/aopp/hera/mad/bakerh/RPM/cpdn_extract_scripts-master/extracted_data/batch_577/atmos/item15201_daily_mean/item15201_daily_mean_b01f_1999-01_2000-12.nc',
'latitude0')
lon = ncread(
'/network/aopp/hera/mad/bakerh/RPM/cpdn_extract_scripts-master/extracted_data/batch_577/atmos/item15201_daily_mean/item15201_daily_mean_b01f_1999-01_2000-12.nc',
'longitude0')
ji = np.zeros((2, 2, len(a)))
for i in range(len(a)):
ucontrol = ncread(a[i], 'item15201_daily_mean')[:, 0, :]
u_djf = ucontrol[300:450]
u_jja = ucontrol[480:630]
ji[0, 0, i], ji[0, 1, i] = jetind(u_djf, lat, lon)
ji[1, 0, i], ji[1, 1, i] = jetind(u_jja, lat, lon)
print(str(i))
return ji
def lsc(tbar, r1, r2):
from netcdfread import ncread
import numpy as np
lsm = ncread(
'/home/bakerh/Documents/DPhil/CPDN/Weather-at-Home_ancilmaker-master/lsm_n96_add.nc',
'lsm')[0, 0, :]
lsm1 = np.ones((145, 192))
for i in range(145):
for j in range(192):
if lsm[i, j] == 0:
lsm1[i, j] = 0
t_nat = np.mean(tbar['All-Nat'], axis=0)
t_sst = np.mean(tbar['GHG-Nat'], axis=0)
t_ghg = np.mean(tbar['SST-Nat'], axis=0)
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/All-Hist/mon/tas/item3236_monthly_mean_a011_2006-01_2016-12.nc',
'longitude0')
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/All-Hist/mon/tas/item3236_monthly_mean_a011_2006-01_2016-12.nc',
'latitude0')
meshlat = np.zeros([np.ma.size(lon), np.ma.size(lat)])
meshlat[:, :] = lat
meshlatweight = np.transpose(np.cos(meshlat * np.pi / 180))
t_nat_w = t_nat * meshlatweight
t_sst_w = t_sst * meshlatweight
t_ghg_w = t_ghg * meshlatweight
def lscalc(t_w, lsm1, meshlatweight, r1=56, r2=89):
lsm1 = lsm1[r1:r2, :]
t_w = t_w[r1:r2, :]
meshlatweight = meshlatweight[r1:r2, :]
t_l = np.mean(t_w[lsm1 > 0.5]) / np.mean(meshlatweight[lsm1 > 0.5])
t_o = np.mean(t_w[lsm1 < 0.5]) / np.mean(meshlatweight[lsm1 < 0.5])
return t_l, t_o
t_nat_l, t_nat_o = lscalc(t_nat_w, lsm1, meshlatweight, r1, r2)
t_sst_l, t_sst_o = lscalc(t_sst_w, lsm1, meshlatweight, r1, r2)
t_ghg_l, t_ghg_o = lscalc(t_ghg_w, lsm1, meshlatweight, r1, r2)
return t_nat_l, t_nat_o, t_sst_l, t_sst_o, t_ghg_l, t_ghg_o
def maplot_c(pdata, pdata1, title='', precip='no'):
"""
Plots input grid with map of world overlaid
Parameters
----------
plotdata: array
data being plotted
title: str
optional title
"""
from mpl_toolkits.basemap import Basemap, shiftgrid
import numpy as np
import matplotlib.pyplot as plt
from netcdfread import ncread
lon = ncread('/network/aopp/hera/mad/bakerh/HAPPI/batch_518/atmos/item3236_monthly_mean/item3236_monthly_mean_a011_2006-01_2016-12.nc', 'longitude0')
lat = ncread('/network/aopp/hera/mad/bakerh/HAPPI/batch_518/atmos/item3236_monthly_mean/item3236_monthly_mean_a011_2006-01_2016-12.nc', 'latitude0')
plt.figure()
pdata, lon1 = shiftgrid(180., pdata, lon, start=False)
pdata1, lon = shiftgrid(180., pdata1, lon, start=False)
meshlon, meshlat = np.meshgrid(lon, lat)
m = Basemap(projection='cyl', llcrnrlat=-90, urcrnrlat=90,
llcrnrlon=-180, urcrnrlon=180, resolution='c')
m.drawcoastlines()
m.drawmapboundary()
x, y = m(meshlon, meshlat)
plot = m.contour(x, y, pdata, [4, 5, 6], colors='k')
plt.clabel(plot, inline=1, fontsize=10)
plot1 = m.contour(x, y, pdata1, [4, 5, 6], colors='r', linestyles='--')
plt.clabel(plot, inline=1, fontsize=10)
parallels = m.drawparallels(np.arange(-90., 91., 15.))
meridians = m.drawmeridians(np.arange(-180., 181., 30))
m.drawparallels(parallels, labels=[True, True, True, True])
m.drawmeridians(meridians, labels=[True, True, True, True])
plt.title(title, y=1.08)
plt.show()
def mean_4xco2(var='va'):
from scipy import interpolate
from netcdfread import ncread
import glob
import numpy as np
from btmodel import maplot_ll
lat_n96 = ncread(
'/home/bakerh/Documents/DPhil/CPDN/Weather-at-Home_ancilmaker-master/lsm_n96_add.nc',
'latitude')
lon_n96 = ncread(
'/home/bakerh/Documents/DPhil/CPDN/Weather-at-Home_ancilmaker-master/lsm_n96_add.nc',
'longitude')
a = sorted(
glob.glob('/network/aopp/hera/mad/bakerh/HAPPI/CMIP5/AMIP/' + var +
'/*'))
b = sorted(
glob.glob('/network/aopp/hera/mad/bakerh/HAPPI/CMIP5/AMIP4xCO2/' +
var + '/*'))
sindices = np.zeros((30 * 3))
for i in range(30):
sindices[3 * i:3 * (i + 1)] = [5 + 12 * i, 6 + 12 * i, 7 + 12 * i]
sindices = sindices.astype(int)
v_means = np.zeros((9, 145, 192))
for i in range(9):
st = 0
if i == 3:
st = 4
if i == 1:
st = 348
if var == 'pr':
v_c = ncread(a[i], var)[st + sindices]
v_f = ncread(b[i], var)[st + sindices]
else:
v_c = ncread(a[i], var)[st + sindices, 9]
v_f = ncread(b[i], var)[st + sindices, 9]
dv = np.mean(v_f - v_c, axis=0)
lat = ncread(a[i], 'lat')
lon = ncread(b[i], 'lon')
maplot_ll(dv, lat, lon, 3)
f = interpolate.interp2d(lon, lat[::-1], dv)
v_res = f(lon_n96, lat_n96)
v_means[i] = v_res
return v_means
def stat_rossM(u):
'''
This treatment makes the regions of reflection (beta<0 and u_m>0)
and regions of absorption (beta>0 and u_m<0) indistinguishable.
These regions of absorption can be identified separately by
plotting the easterlies.
'''
from netcdfread import ncread
lat = ncread('/network/aopp/hera/mad/bakerh/HAPPI/MIROC5/All-Hist/day/tas/tas_Aday_MIROC5_All-Hist_est1_v2-0_run001_20060101-20161231.nc','lat')
lon = np.arange(0, 360, 360/256)
meshlon, meshlat = np.meshgrid(lon, lat)
phi = meshlat*np.pi/180
cs_phi = np.cos(phi)
dphi = 1.25*np.pi/180
omega = 7.29e-5
a = 6.371e6
u_m = u / (a*cs_phi)
u_1opp = np.gradient(u_m*cs_phi**2, dphi, axis=0) / cs_phi
u_2opp = np.gradient(u_1opp, dphi, axis=0) / cs_phi
beta = (2*omega - u_2opp)*cs_phi**2/a
ks = np.sqrt(a*beta/u_m)
return ks
def trop():
from netCDF4 import Dataset
from netcdfread import ncread
import glob
def lapserate(t, z17):
"""
Produces plot of lapse rate of T data
"""
import numpy as np
k = 287.058 / 0.718
L = np.zeros((17, 145))
for i in range(16):
L[i, :] = k * 9.81 * ((t[i+1, :] - t[i, :]) * (z17[i+1] + z17[i])) / (287.058*(t[i+1, :] + t[i, :]) * (z17[i+1] - z17[i]))
return L
sindices = np.zeros((11*3))
windices = np.zeros((11*3))
for i in range(11):
sindices[3*i:3*(i+1)] = [5+12*i, 6+12*i, 7+12*i]
for i in range(11):
windices[3*i:3*(i+1)] = [12*i, 1+12*i, 11+12*i]
sindices = sindices.astype(int)
windices = windices.astype(int)
z = ncread('/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/All-Hist/mon/ta/item16203_monthly_mean_a011_2006-01_2016-12.nc', 'z9')
a = glob.glob('/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/' +
'All-Hist/mon/ta/*')
tbar = np.zeros((np.ma.size(z), 145))
for e, d in enumerate(a):
nc_fid = Dataset(d, 'r')
tbar += np.mean(nc_fid.variables['item16203_monthly_mean'][sindices, :],
axis=(0, 3))
print('Done ' + str(e+1))
tbar /= np.ma.size(a)
L = lapserate(tbar, z)
return tbar, L
def circ_patternM(d_v, v_anom, f_anom, r=[0, 128, 0, 256]):
from statsmodels.api import OLS
lat = ncread('/network/aopp/hera/mad/bakerh/HAPPI/MIROC5/present/tas/tas_Aday_MIROC5_All-Hist_est1_v2-0_run001_20060101-20161231.nc','lat')
lon = np.arange(0, 360, 360/256)
meshlat = np.zeros((256, 128))
meshlat[:, :] = lat
meshlat = np.transpose(meshlat)
mlw = (np.cos(meshlat * np.pi/180))
v_coef = np.zeros((np.ma.size(v_anom, axis=0)))
v_anom_w = v_anom * mlw
dv = d_v * mlw
dv_sub = np.ndarray.flatten(dv[r[0]:r[1], r[2]:r[3]])
v_anom_sub = np.transpose(np.reshape(v_anom_w[:, r[0]:r[1], r[2]:r[3]],
(np.ma.size(v_anom, axis=0),
(r[1]-r[0])*(r[3]-r[2]))))
model = OLS(dv_sub, v_anom_sub).fit()
v_coef = model._results.params
field_circ = np.zeros((np.ma.size(v_anom, axis=0), 128, 256))
for a in range(np.ma.size(v_anom, axis=0)):
field_circ[a, :] = v_coef[a] * f_anom[a, :]
field_circ = np.sum(field_circ, axis=0)
return field_circ
def rws_fprime(ua200, va200):
from windspharm.standard import VectorWind
from netcdfread import ncread, ncsave
from scipy import interpolate
lon42 = ncread(
'/network/aopp/hera/mad/bakerh/BTmodel_COR/main/inputs/forcing_ghg.nc',
'lon')
lat42 = ncread(
'/network/aopp/hera/mad/bakerh/BTmodel_COR/main/inputs/forcing_ghg.nc',
'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/All-Hist/mon/tas/item3236_monthly_mean_a011_2006-01_2016-12.nc',
'longitude0')
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/Plus15-Future_LCO2/day/ua/item15201_daily_mean_a00b_2090-01_2100-12.nc',
'latitude1')
u = ua200['All-Nat'] #[1]
v = va200['All-Nat'] #[1]
u_sst = ua200['GHG-Nat'] #[1]
v_sst = va200['GHG-Nat'] #[1]
u_ghg = ua200['SST-Nat'] #[1]
v_ghg = va200['SST-Nat'] #[1]
v = w5rem(v)
u = w5rem(u)
uwnd = np.zeros((64, 128, len(u)))
vwnd = np.zeros((64, 128, len(v)))
uwnd_sst = np.zeros((64, 128, len(u_sst)))
vwnd_sst = np.zeros((64, 128, len(u_sst)))
uwnd_ghg = np.zeros((64, 128, len(u_ghg)))
vwnd_ghg = np.zeros((64, 128, len(u_ghg)))
u = np.transpose(u, (1, 2, 0))
v = np.transpose(v, (1, 2, 0))
u_sst = np.transpose(u_sst, (1, 2, 0))
v_sst = np.transpose(v_sst, (1, 2, 0))
u_ghg = np.transpose(u_ghg, (1, 2, 0))
v_ghg = np.transpose(v_ghg, (1, 2, 0))
for i in range(np.ma.size(uwnd, axis=2)):
h = interpolate.interp2d(lon, lat[::-1], u[:, :, i])
uwnd[:, :, i] = h(lon42, lat42[::-1])
g = interpolate.interp2d(lon, lat[::-1], v[:, :, i])
vwnd[:, :, i] = g(lon42, lat42[::-1])
for i in range(np.ma.size(uwnd_sst, axis=2)):
h = interpolate.interp2d(lon, lat[::-1], u_sst[:, :, i])
uwnd_sst[:, :, i] = h(lon42, lat42[::-1])
g = interpolate.interp2d(lon, lat[::-1], v_sst[:, :, i])
vwnd_sst[:, :, i] = g(lon42, lat42[::-1])
for i in range(np.ma.size(uwnd_ghg, axis=2)):
h = interpolate.interp2d(lon, lat[::-1], u_ghg[:, :, i])
uwnd_ghg[:, :, i] = h(lon42, lat42[::-1])
g = interpolate.interp2d(lon, lat[::-1], v_ghg[:, :, i])
vwnd_ghg[:, :, i] = g(lon42, lat42[::-1])
w = VectorWind(uwnd, vwnd)
w_sst = VectorWind(uwnd_sst, vwnd_sst)
w_ghg = VectorWind(uwnd_ghg, vwnd_ghg)
eta = w.absolutevorticity()
eta_sst = w_sst.absolutevorticity()
eta_ghg = w_ghg.absolutevorticity()
div = w.divergence()
div_sst = w_sst.divergence()
div_ghg = w_ghg.divergence()
uchi, vchi = w.irrotationalcomponent()
uchi_sst, vchi_sst = w_sst.irrotationalcomponent()
uchi_ghg, vchi_ghg = w_ghg.irrotationalcomponent()
etax, etay = w.gradient(eta)
# etax_sst, etay_sst = w_sst.gradient(eta_sst)
# etax_ghg, etay_ghg = w_ghg.gradient(eta_ghg)
eta = np.transpose(eta, (2, 0, 1))
eta_sst = np.transpose(eta_sst, (2, 0, 1))
eta_ghg = np.transpose(eta_ghg, (2, 0, 1))
div = np.transpose(div, (2, 0, 1))
div_sst = np.transpose(div_sst, (2, 0, 1))
div_ghg = np.transpose(div_ghg, (2, 0, 1))
etax = np.transpose(etax, (2, 0, 1))
etay = np.transpose(etay, (2, 0, 1))
uchi = np.transpose(uchi, (2, 0, 1))
uchi_sst = np.transpose(uchi_sst, (2, 0, 1))
uchi_ghg = np.transpose(uchi_ghg, (2, 0, 1))
vchi = np.transpose(vchi, (2, 0, 1))
vchi_sst = np.transpose(vchi_sst, (2, 0, 1))
vchi_ghg = np.transpose(vchi_ghg, (2, 0, 1))
#f_ghg = -eta.mean(axis=0)*(div_ghg-div.mean(axis=0))-(uchi_ghg-uchi.mean(axis=0))*etax.mean(axis=0)-(vchi_ghg-vchi.mean(axis=0))*etay.mean(axis=0)
#f_sst = -eta.mean(axis=0)*(div_sst-div.mean(axis=0))-(uchi_sst-uchi.mean(axis=0))*etax.mean(axis=0)-(vchi_sst-vchi.mean(axis=0))*etay.mean(axis=0)
f_ghg = -eta * (div_ghg - div) - ((uchi_ghg - uchi) * etax +
(vchi_ghg - vchi) * etay)
f_sst = -eta * (div_sst - div) - ((uchi_sst - uchi) * etax +
(vchi_sst - vchi) * etay)
meshlon, meshlat = np.meshgrid(lon42, lat42)
'''
ncsave('/home/bakerh/Downloads/vort200_control', lat42, lon42, eta.mean(axis=0)-2*np.sin(meshlat*np.pi/180)*7.2921e-5, 'vorticity')
ncsave('/home/bakerh/Downloads/vort200_sst', lat42, lon42, eta_sst.mean(axis=0)-2*np.sin(meshlat*np.pi/180)*7.2921e-5, 'vorticity')
ncsave('/home/bakerh/Downloads/vort200_ghg', lat42, lon42, eta_ghg.mean(axis=0)-2*np.sin(meshlat*np.pi/180)*7.2921e-5, 'vorticity')
ncsave('/home/bakerh/Downloads/forcing_ghg', lat42, lon42, f_ghg.mean(axis=0), 'forcing')
ncsave('/home/bakerh/Downloads/forcing_sst', lat42, lon42, f_sst.mean(axis=0), 'forcing')
'''
month = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
ncsave('/home/bakerh/Downloads/vort200_control', month, lat42, lon42,
eta - 2 * np.sin(meshlat * np.pi / 180) * 7.2921e-5, 'vorticity')
ncsave('/home/bakerh/Downloads/vort200_sst', month, lat42, lon42,
eta_sst - 2 * np.sin(meshlat * np.pi / 180) * 7.2921e-5,
'vorticity')
ncsave('/home/bakerh/Downloads/vort200_ghg', month, lat42, lon42,
eta_ghg - 2 * np.sin(meshlat * np.pi / 180) * 7.2921e-5,
'vorticity')
ncsave('/home/bakerh/Downloads/forcing_ghg', month, lat42, lon42, f_ghg,
'forcing')
ncsave('/home/bakerh/Downloads/forcing_sst', month, lat42, lon42, f_sst,
'forcing')
def main(var, global_tas):
from scipy import interpolate
from netcdfread import ncread
import glob
sindices = np.zeros((95 * 3))
for i in range(95):
sindices[3 * i:3 * (i + 1)] = [5 + 12 * i, 6 + 12 * i, 7 + 12 * i]
sindices = sindices.astype(int)
lat_n96 = ncread(
'/home/bakerh/Documents/DPhil/CPDN/Weather-at-Home_ancilmaker-master/lsm_n96_add.nc',
'latitude')
lon_n96 = ncread(
'/home/bakerh/Documents/DPhil/CPDN/Weather-at-Home_ancilmaker-master/lsm_n96_add.nc',
'longitude')
concs = {}
concs['inmcm4'] = 1084.44
concs['MIROC-ESM'] = 533.57
concs['CanESM2'] = 532.45
concs['MPI-ESM-LR'] = 555.37
concs['GFDL-ESM2M'] = 778.35
concs['HadGEM2-CC'] = 759.01
concs['bcc-csm1-1'] = 675.27
concs['IPSL-CM5A-MR'] = 656.68
concs['GFDL-ESM2G'] = 770.60
concs['IPSL-CM5B-LR'] = 666.95
concs['MIROC-ESM-CHEM'] = 537.12
concs['IPSL-CM5A-LR'] = 604.38
concs['HadGEM2-ES'] = 740.65
yrs = {}
# yrs['CESM1-BGC'] = 2019.6038556835267
yrs['CanESM2'] = 2015.6501388153354
yrs['GFDL-ESM2G'] = 2040.2726255644302
yrs['GFDL-ESM2M'] = 2038.4461621682028
yrs['HadGEM2-CC'] = 2030.5237032804
yrs['HadGEM2-ES'] = 2028.1144760733512
yrs['IPSL-CM5A-LR'] = 2013.7490131087695
yrs['IPSL-CM5A-MR'] = 2018.568003728501
yrs['IPSL-CM5B-LR'] = 2025.1511344273508
yrs['MIROC-ESM'] = 2021.3187712711444
yrs['MIROC-ESM-CHEM'] = 2018.89262095655
yrs['MPI-ESM-LR'] = 2020.0421238126532
# yrs['NorESM1-ME'] = 2034.3684277126238
yrs['bcc-csm1-1'] = 2022.8345355891056
yrs['inmcm4'] = 2045.194355545509
yrs2 = {}
# yrs2['CESM1-BGC'] = 2034.7262797142157,
yrs2['CanESM2'] = 2028.6584559023813,
yrs2['GFDL-ESM2G'] = 2056.5526316368,
yrs2['GFDL-ESM2M'] = 2053.6514333101513,
yrs2['HadGEM2-CC'] = 2043.093047747961,
yrs2['HadGEM2-ES'] = 2039.480727397444,
yrs2['IPSL-CM5A-LR'] = 2029.344424079118,
yrs2['IPSL-CM5A-MR'] = 2032.351689422151,
yrs2['IPSL-CM5B-LR'] = 2039.2780954952768,
yrs2['MIROC-ESM'] = 2031.144339409215,
yrs2['MIROC-ESM-CHEM'] = 2030.7223697396423,
yrs2['MPI-ESM-LR'] = 2039.3393134034848,
# yrs2['NorESM1-ME'] = 2047.8103280489413,
yrs2['bcc-csm1-1'] = 2038.2589979997997,
yrs2['inmcm4'] = 2058.6460082149406
tcr = {}
# tcr['CESM1-BGC'] = 1.7
tcr['CanESM2'] = 2.4
tcr['GFDL-ESM2G'] = 1.1
tcr['GFDL-ESM2M'] = 1.3
tcr['HadGEM2-CC'] = np.nan
tcr['HadGEM2-ES'] = 2.5
tcr['IPSL-CM5A-LR'] = 2
tcr['IPSL-CM5A-MR'] = 2
tcr['IPSL-CM5B-LR'] = 1.5
tcr['MIROC-ESM'] = 2.2
tcr['MIROC-ESM-CHEM'] = np.nan
tcr['MPI-ESM-LR'] = 2.0
# tcr['NorESM1-ME'] = 1.6
tcr['bcc-csm1-1'] = 1.7
tcr['inmcm4'] = 1.3
tcre = {}
# tcre['CESM1-BGC'] = 1.7
tcre['CanESM2'] = 2.375
tcre['GFDL-ESM2G'] = 0.825
tcre['GFDL-ESM2M'] = 1.075
tcre['HadGEM2-CC'] = np.nan
tcre['HadGEM2-ES'] = 2.1
tcre['IPSL-CM5A-LR'] = 1.6
tcre['IPSL-CM5A-MR'] = 1.575
tcre['IPSL-CM5B-LR'] = 1.2
tcre['MIROC-ESM'] = 2.15
tcre['MIROC-ESM-CHEM'] = np.nan
tcre['MPI-ESM-LR'] = 1.6
# tcre['NorESM1-ME'] = 1.6
tcre['bcc-csm1-1'] = 1.4
tcre['inmcm4'] = 1
tx90p_n96 = {}
tx90p_n96_2 = {}
items = []
tx90p_MID = []
tx90p_TROP = []
ts_tx90p_MID = []
ts_tx90p_TROP = []
emissions = []
years = []
tcrs = []
tcres = []
global_tass = []
a = glob.glob('/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/*')
#a1 = a[0]
#a[0] = a[1]
#a[1] = a1
for i in a:
items.append(i[78 + 2 * len(var) - 10:-30])
for i in items:
print(i)
if i == 'HadGEM2-CC':
tx90p = np.array(
ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/'
+ var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200501-210012.nc', var + 'ETCCDI')[12:, :])
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200501-210012.nc', 'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200501-210012.nc', 'lon')
elif i == 'HadGEM2-ES':
tx90p = np.array(
ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/'
+ var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200501-229912.nc',
var + 'ETCCDI')[12:1152, :])
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200501-229912.nc', 'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200501-229912.nc', 'lon')
elif i == 'bcc-csm1-1':
tx90p = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', var + 'ETCCDI')[:1140, :]
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', 'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', 'lon')
elif i == 'IPSL-CM5A-LR':
tx90p = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', var + 'ETCCDI')[:1140, :]
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', 'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', 'lon')
elif i == 'MPI-ESM-LR':
tx90p = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', var + 'ETCCDI')[:1140, :]
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', 'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', 'lon')
else:
tx90p = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-210012.nc', var + 'ETCCDI')[:1140, :]
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-210012.nc', 'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-210012.nc', 'lon')
tx90p = tx90p[sindices]
tx90p2 = np.copy(tx90p)
ts_tx90p = np.copy(tx90p)
# idx = (np.abs(conversion_0[:, 2]-concs[i])).argmin()
# yr = conversion_0[idx, 0]
yr = int(np.round(yrs[i]))
yr2 = int(np.round(yrs2[i]))
tx90p = np.mean(tx90p[3 * (yr - 2010):3 * (yr - 2000), :], axis=0)
tx90p2 = np.mean(tx90p2[3 * (yr2 - 2010):3 * (yr2 - 2000), :], axis=0)
f = interpolate.interp2d(lon, lat[::-1], tx90p)
f2 = interpolate.interp2d(lon, lat[::-1], tx90p2)
ts_tx90p = np.reshape(ts_tx90p, (95, 3, len(lat), len(lon)))
ts_tx90p = np.mean(ts_tx90p, axis=1)
ts_tx90p_mid = np.zeros((95))
ts_tx90p_trop = np.zeros((95))
'''
for j in range(95):
g = interpolate.interp2d(lon, lat[::-1], ts_tx90p[j])
ts_tx90p_mid[j] = latweightmean(g(lon_n96, lat_n96), msk='MID')
ts_tx90p_trop[j] = latweightmean(g(lon_n96, lat_n96), msk='TROP')
ts_tx90p_MID.append(ts_tx90p_mid)
ts_tx90p_TROP.append(ts_tx90p_trop)
'''
tx90p_n96[i] = f(lon_n96, lat_n96)
tx90p_n96_2[i] = f2(lon_n96, lat_n96)
emissions.append(concs[i])
tcres.append(tcre[i])
tcrs.append(tcr[i])
years.append(yrs[i])
global_tass.append(global_tas[i + '_r1i1p1'])
tx90p_MID.append(latweightmean(f(lon_n96, lat_n96), msk='MID'))
tx90p_TROP.append(latweightmean(f(lon_n96, lat_n96), msk='TROP'))
if var == 'tx90p':
d = 0.9
else:
d = 1
t_l = (tx90p_n96['CanESM2'] + tx90p_n96['MIROC-ESM-CHEM'] +
tx90p_n96['IPSL-CM5A-LR']) * d / 3
t_h = (tx90p_n96['GFDL-ESM2G'] + tx90p_n96['inmcm4'] +
tx90p_n96['HadGEM2-CC']) * d / 3
t2 = (tx90p_n96_2['CanESM2'] + tx90p_n96_2['MIROC-ESM-CHEM'] +
tx90p_n96_2['IPSL-CM5A-LR'] + tx90p_n96_2['GFDL-ESM2G'] +
tx90p_n96_2['inmcm4'] + tx90p_n96_2['HadGEM2-CC']) * d / 6
t15 = (tx90p_n96['CanESM2'] + tx90p_n96['MIROC-ESM-CHEM'] +
tx90p_n96['IPSL-CM5A-LR'] + tx90p_n96['GFDL-ESM2G'] +
tx90p_n96['inmcm4'] + tx90p_n96['HadGEM2-CC']) * d / 6
'''
for i in range(len(years)):
ts_tx90p_MID[i] *= d
ts_tx90p_TROP[i] *= d
tx90p_MID[i] *= d
tx90p_TROP[i] *= d
'''
return t_l, t_h, t15, t2, tcrs, tcres, emissions, years, global_tass #, ts_tx90p_MID, ts_tx90p_TROP
def cmiptas():
from scipy import interpolate
from netcdfread import ncread
import glob
import numpy as np
def lscalc(t_w, lsm1, meshlatweight, r1=56, r2=89):
lsm1 = lsm1[r1:r2, :]
t_w = t_w[r1:r2, :]
meshlatweight = meshlatweight[r1:r2, :]
t_l = np.mean(t_w[lsm1 > 0.5]) / np.mean(meshlatweight[lsm1 > 0.5])
t_o = np.mean(t_w[lsm1 < 0.5]) / np.mean(meshlatweight[lsm1 < 0.5])
return t_l, t_o
lsm = ncread(
'/home/bakerh/Documents/DPhil/CPDN/Weather-at-Home_ancilmaker-master/lsm_n96_add.nc',
'lsm')[0, 0, :]
lsm1 = np.ones((145, 192))
for i in range(145):
for j in range(192):
if lsm[i, j] == 0:
lsm1[i, j] = 0
lat_n96 = ncread(
'/home/bakerh/Documents/DPhil/CPDN/Weather-at-Home_ancilmaker-master/lsm_n96_add.nc',
'latitude')
lon_n96 = ncread(
'/home/bakerh/Documents/DPhil/CPDN/Weather-at-Home_ancilmaker-master/lsm_n96_add.nc',
'longitude')
meshlat = np.zeros([np.ma.size(lon_n96), np.ma.size(lat_n96)])
meshlat[:, :] = lat_n96
meshlatweight = np.transpose(np.cos(meshlat * np.pi / 180))
models = [
'ACCESS1-0_', 'ACCESS1-3_', 'bcc-csm1-1_', 'bcc-csm1-1-m_', 'BNU-ESM_',
'CanESM2_', 'CCSM4_', 'CNRM-CM5_', 'CSIRO-Mk3-6-0_', 'EC-EARTH_',
'FGOALS-g2_', 'FGOALS-s2_', 'GFDL-CM3_', 'GFDL-ESM2G_', 'GFDL-ESM2M_',
'GISS-E2-H_', 'GISS-E2-R_', 'HadGEM2-ES', 'inmcm4_', 'IPSL-CM5A-LR_',
'IPSL-CM5A-MR_', 'IPSL-CM5B-LR_', 'MPI-ESM-LR_', 'MPI-ESM-MR_',
'MPI-ESM-P_', 'NorESM1-M_'
]
dT = np.zeros(26)
aT = np.zeros(26)
aTt = np.zeros(26)
for j, model in enumerate(models):
a = glob.glob(
'/network/aopp/hera/mad/bakerh/HAPPI/CMIP5/abrupt4xco2/tas_Amon_' +
model + '*')
b = glob.glob(
'/network/aopp/hera/mad/bakerh/HAPPI/CMIP5/piControl/tas_Amon_' +
model + '*')
data4 = ncread(a[0], 'tas')
datac = ncread(b[0], 'tas')
if len(a) > 1:
for i in range(len(a) - 1):
data4 = np.concatenate((data4, ncread(a[i + 1], 'tas')),
axis=0)
if len(b) > 1:
for i in range(len(b) - 1):
datac = np.concatenate((datac, ncread(b[i + 1], 'tas')),
axis=0)
data4 = np.squeeze(np.mean(data4[1200:1800], axis=0))
datac = np.squeeze(np.mean(datac[1200:], axis=0))
lat = ncread(a[0], 'lat')
lon = ncread(a[0], 'lon')
f = interpolate.interp2d(lon, lat[::-1], data4)
g = interpolate.interp2d(lon, lat[::-1], datac)
data4_n96_w = f(lon_n96, lat_n96) * meshlatweight
datac_n96_w = g(lon_n96, lat_n96) * meshlatweight
t4 = np.mean(data4_n96_w) / np.mean(meshlatweight)
tc = np.mean(datac_n96_w) / np.mean(meshlatweight)
dt = t4 - tc
t4_l, t4_o = lscalc(data4_n96_w, lsm1, meshlatweight, 0, 145)
tc_l, tc_o = lscalc(datac_n96_w, lsm1, meshlatweight, 0, 145)
t4t_l, t4t_o = lscalc(data4_n96_w, lsm1, meshlatweight, 56, 89)
tct_l, tct_o = lscalc(datac_n96_w, lsm1, meshlatweight, 56, 89)
ta = (t4_l - tc_l) - (t4_o - tc_o)
tat = (t4t_l - tct_l) - (t4t_o - tct_o)
dT[j] = dt
aT[j] = ta
aTt[j] = tat
print(model)
return dT, aT, aTt
def plotall(data_l,
data_h,
colorlimit,
mask='yes',
precip='no',
pltlbl='RCP8.5 TX90p'):
from netcdfread import ncread
import matplotlib as mpl
from matplotlib import pyplot as plt
from mpl_toolkits.basemap import Basemap, shiftgrid, addcyclic
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/batch_518/atmos/item3236_monthly_mean/item3236_monthly_mean_a011_2006-01_2016-12.nc',
'longitude0')
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/batch_518/atmos/item3236_monthly_mean/item3236_monthly_mean_a011_2006-01_2016-12.nc',
'latitude0')
summer = data_h - data_l
fig = plt.figure(facecolor='w', edgecolor='k', linewidth=2)
if mask == 'yes':
lsm = ncread(
'/home/bakerh/Documents/DPhil/CPDN/\
Weather-at-Home_ancilmaker-master/lsm_n96_add.nc', 'lsm')[0, 0, :]
summer = np.ma.masked_array(summer, mask=np.logical_not(lsm))
summer, lon3 = shiftgrid(180., summer, lon, start=False)
summer, lon3 = addcyclic(summer, lon3)
meshlon, meshlat = np.meshgrid(lon3, lat)
ctrs = np.linspace(-colorlimit, colorlimit, 17)
mycmap2 = plt.cm.YlOrRd(np.arange(256))
mycmap1 = plt.cm.Blues_r(np.arange(256))
my_cmap = np.concatenate((mycmap1, mycmap2), axis=0)
if precip == 'yes':
my_cmap = my_cmap[::-1]
my_cmap[230:282, :] = 1
newcmap = mpl.colors.LinearSegmentedColormap.from_list("nj", my_cmap)
cmp = newcmap
ax1 = fig.add_subplot(1, 1, 1)
m = Basemap(projection='moll', lon_0=0, resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawmapboundary(linewidth=2)
x, y = m(meshlon, meshlat)
plot = m.contourf(x,
y,
summer,
ctrs,
cmap=cmp,
vmin=np.min(ctrs),
vmax=np.max(ctrs),
extend='both')
hfont = {'fontname': 'Arial'}
c = fig.colorbar(plot,
ax=ax1,
orientation='horizontal',
spacing='proportional',
aspect=50)
c.set_label(label='Difference (days season$\mathregular{^{-1}}$)', size=20)
#cl = ax1.getp(c, 'xmajorticklabels')
c.ax.tick_params(labelsize=20)
pad = 15 # in points
ax1.annotate(pltlbl,
xy=(0, 0.5),
xytext=(-ax1.yaxis.labelpad - pad, 0),
fontsize=32,
fontname='Arial',
xycoords=ax1.yaxis.label,
textcoords='offset points',
ha='right',
va='center',
rotation=90)
plt.show()
plt.subplots_adjust(hspace=0,
wspace=0.05,
top=.97,
bottom=0.15,
left=.05,
right=.95)
def latweightmean(data, msk='no'):
from netcdfread import ncread
lsm = ncread(
'/home/bakerh/Documents/DPhil/CPDN/Weather-at-Home_ancilmaker-master/lsm_n96_add.nc',
'lsm')[0, 0, :]
lsm1 = np.ones((145, 192))
for i in range(145):
for j in range(192):
if lsm[i, j] == 0:
lsm1[i, j] = 0
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/batch_518/atmos/item3236_monthly_mean/item3236_monthly_mean_a011_2006-01_2016-12.nc',
'longitude0')
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/batch_518/atmos/item3236_monthly_mean/item3236_monthly_mean_a011_2006-01_2016-12.nc',
'latitude0')
meshlat = np.zeros([np.ma.size(lon), np.ma.size(lat)])
meshlat[:, :] = lat
meshlatweight = np.transpose(np.cos(meshlat * np.pi / 180))
weighted = data * meshlatweight
if msk == 'yes':
mask = np.zeros((145, 192))
mask[:] = lsm1
meaned = np.mean(weighted[mask > 0.5]) / np.mean(
meshlatweight[lsm1 > 0.5])
elif msk == 'NH':
mask = np.zeros((73, 192))
lsm1 = lsm1[:73, :]
mask[:] = lsm1
weighted = weighted[:73, :]
meshlatweight = meshlatweight[:73, :]
meaned = np.mean(weighted[mask > 0.5]) / np.mean(
meshlatweight[lsm1 > 0.5])
elif msk == 'TROP':
mask = np.zeros((49, 192))
lsm1 = lsm1[48:97, :]
mask[:] = lsm1
weighted = weighted[48:97, :]
meshlatweight = meshlatweight[48:97, :]
meaned = np.mean(weighted[mask > 0.5]) / np.mean(
meshlatweight[lsm1 > 0.5])
elif msk == 'MID':
mask = np.zeros((48, 192))
lsm1 = lsm1[:48, :]
mask[:] = lsm1
weighted = weighted[:48, :]
meshlatweight = meshlatweight[:48, :]
meaned = np.mean(weighted[mask > 0.5]) / np.mean(
meshlatweight[lsm1 > 0.5])
elif msk == 'no':
meaned = np.mean(weighted) / np.mean(meshlatweight)
elif np.ma.size(msk) == 5:
mask = np.zeros((msk[2] - msk[1], 192 + msk[4] - msk[3]))
lsm1 = np.concatenate(
(lsm1[msk[1]:msk[2], msk[3]:], lsm1[msk[1]:msk[2], :msk[4]]),
axis=1)
mask[:] = lsm1
weighted = np.concatenate(
(weighted[msk[1]:msk[2],
msk[3]:], weighted[:, msk[1]:msk[2], :msk[4]]),
axis=2)
meshlatweight = np.concatenate(
(meshlatweight[msk[1]:msk[2],
msk[3]:], meshlatweight[msk[1]:msk[2], :msk[4]]),
axis=1)
meaned = np.mean(weighted[mask > 0.5]) / np.mean(
meshlatweight[lsm1 > 0.5])
else:
mask = np.zeros((msk[1] - msk[0], msk[3] - msk[2]))
lsm1 = lsm1[msk[0]:msk[1], msk[2]:msk[3]]
mask[:] = lsm1
weighted = weighted[msk[0]:msk[1], msk[2]:msk[3]]
meshlatweight = meshlatweight[msk[0]:msk[1], msk[2]:msk[3]]
meaned = np.mean(weighted[mask > 0.5]) / np.mean(
meshlatweight[lsm1 > 0.5])
return meaned
def animate_rw(
data,
colorlimit=0.5,
ts=6,
title='',
save='no',
location='/home/bakerh/Documents/DPhil/Figures/Python/BTmodel/animations/new.mp4'
):
"""
Plots animation of input grid of quantity at sigma and lat coords
Parameters
----------
data: array
data being plotted
sigma: array
sigma levels of data
lat: array
latitudes of data
title: str
optional title
"""
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from netcdfread import ncread
from mpl_toolkits.basemap import Basemap, shiftgrid, addcyclic
lon = ncread(
'/network/aopp/hera/mad/bakerh/BTmodel_COR/main/inputs/forcing_ghg.nc',
'lon')
lat = ncread(
'/network/aopp/hera/mad/bakerh/BTmodel_COR/main/inputs/forcing_ghg.nc',
'lat')
summer = np.copy(data[::ts])
fig = plt.figure(facecolor='w', edgecolor='k', linewidth=2)
summer, lon = shiftgrid(180., summer, lon, start=False)
summer, lon = addcyclic(summer, lon)
meshlon, meshlat = np.meshgrid(lon, lat)
ctrs = np.linspace(-colorlimit, colorlimit, 17)
mycmap2 = plt.cm.YlOrRd(np.arange(256))
mycmap1 = plt.cm.Blues_r(np.arange(256))
my_cmap = np.concatenate((mycmap1, mycmap2), axis=0)
my_cmap[230:282, :] = 1
newcmap = mpl.colors.LinearSegmentedColormap.from_list("nj", my_cmap)
cmp = newcmap
def updatefig(i):
fig.clear()
ax1 = fig.add_subplot(1, 1, 1)
m = Basemap(projection='cyl',
llcrnrlat=-90,
urcrnrlat=90,
llcrnrlon=-180,
urcrnrlon=180,
resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawmapboundary(linewidth=2)
x, y = m(meshlon, meshlat)
plot = m.contourf(x,
y,
summer[i] - summer[0],
ctrs,
cmap=cmp,
vmin=np.min(ctrs),
vmax=np.max(ctrs),
extend='both')
# parallels = m.drawparallels(np.arange(-90., 91., 15.))
# meridians = m.drawmeridians(np.arange(-180., 181., 30.))
# m.drawparallels(parallels, labels=[True, True, True, True])
# m.drawmeridians(meridians, labels=[True, True, True, True])
b = fig.colorbar(plot,
aspect=50,
shrink=0.75,
spacing='proportional',
orientation='horizontal',
extend='max',
pad=0.05)
b.set_label(label=r'V200 (1x10$^{-6}$ ms$^{-1}$)')
j = i / (24 / ts)
ax1.set_title(' t=' + str('%02.3f' % j), loc='left', y=.9)
plt.subplots_adjust(top=.99, bottom=0.01, left=.05, right=.95)
plt.suptitle(title, y=.85)
plt.draw()
anim = animation.FuncAnimation(fig, updatefig, len(summer))
if save == 'yes':
anim.save(location, codec='mpeg4', bitrate=8000, dpi=300)
return anim
def comp(model, var='tx90p'):
from scipy import interpolate
from netcdfread import ncread
sindices = np.zeros((95 * 3))
for i in range(95):
sindices[3 * i:3 * (i + 1)] = [5 + 12 * i, 6 + 12 * i, 7 + 12 * i]
sindices = sindices.astype(int)
lat_n96 = ncread(
'/home/bakerh/Documents/DPhil/CPDN/Weather-at-Home_ancilmaker-master/lsm_n96_add.nc',
'latitude')
lon_n96 = ncread(
'/home/bakerh/Documents/DPhil/CPDN/Weather-at-Home_ancilmaker-master/lsm_n96_add.nc',
'longitude')
yrs = {}
# yrs['CESM1-BGC'] = 2019.6038556835267
yrs['CanESM2'] = 2015.6501388153354
yrs['GFDL-ESM2G'] = 2040.2726255644302
yrs['GFDL-ESM2M'] = 2038.4461621682028
yrs['HadGEM2-CC'] = 2030.5237032804
yrs['HadGEM2-ES'] = 2028.1144760733512
yrs['IPSL-CM5A-LR'] = 2013.7490131087695
yrs['IPSL-CM5A-MR'] = 2018.568003728501
yrs['IPSL-CM5B-LR'] = 2025.1511344273508
yrs['MIROC-ESM'] = 2021.3187712711444
yrs['MIROC-ESM-CHEM'] = 2018.89262095655
yrs['MPI-ESM-LR'] = 2020.0421238126532
# yrs['NorESM1-ME'] = 2034.3684277126238
yrs['bcc-csm1-1'] = 2022.8345355891056
yrs['inmcm4'] = 2045.194355545509
yrs26 = {}
yrs26['CanESM2'] = 2015.9168461250079
yrs26['HadGEM2-ES'] = 2031.2511570532158
yrs26['IPSL-CM5A-LR'] = 2013.6504936554309
yrs26['IPSL-CM5A-MR'] = 2019.6423318791442
yrs26['MIROC-ESM'] = 2022.8139042931136
yrs26['MIROC-ESM-CHEM'] = 2016.8634829024122
yrs26['MPI-ESM-LR'] = 2025.4963052174598
# yrs['NorESM1-ME'] = 2044.9730257768917
yrs26['bcc-csm1-1'] = 2025.1860768601402
i = model
if i == 'HadGEM2-CC':
tx90p = np.array(
ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200501-210012.nc', var + 'ETCCDI')[12:, :])
tx90p26 = np.array(
ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp26/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp26_r1i1p1_200501-210012.nc', var + 'ETCCDI')[12:, :])
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200501-210012.nc', 'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200501-210012.nc', 'lon')
elif i == 'HadGEM2-ES':
tx90p = np.array(
ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200501-229912.nc', var + 'ETCCDI')[12:1152, :])
tx90p26 = np.array(
ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp26/' +
var + '/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp26_r1i1p1_200501-229912.nc', var + 'ETCCDI')[12:1152, :])
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200501-229912.nc', 'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200501-229912.nc', 'lon')
elif i == 'bcc-csm1-1':
tx90p = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', var + 'ETCCDI')[:1140, :]
tx90p26 = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp26/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp26_r1i1p1_200601-230012.nc', var + 'ETCCDI')[:1140, :]
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', 'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', 'lon')
elif i == 'IPSL-CM5A-LR':
tx90p = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', var + 'ETCCDI')[:1140, :]
tx90p26 = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp26/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp26_r1i1p1_200601-230012.nc', var + 'ETCCDI')[:1140, :]
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', 'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', 'lon')
elif i == 'MPI-ESM-LR':
tx90p = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', var + 'ETCCDI')[:1140, :]
tx90p26 = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp26/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp26_r1i1p1_200601-230012.nc', var + 'ETCCDI')[:1140, :]
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', 'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-230012.nc', 'lon')
else:
tx90p = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-210012.nc', var + 'ETCCDI')[:1140, :]
tx90p26 = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp26/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp26_r1i1p1_200601-210012.nc', var + 'ETCCDI')[:1140, :]
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-210012.nc', 'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/climdex/CMIP5/rcp85/' + var +
'/' + var + 'ETCCDI_mon_' + str(i) +
'_rcp85_r1i1p1_200601-210012.nc', 'lon')
tx90p = tx90p[sindices]
tx90p26 = tx90p26[sindices]
# idx = (np.abs(conversion_0[:, 2]-concs[i])).argmin()
# yr = conversion_0[idx, 0]
yr = np.round(yrs[i])
yr26 = np.round(yrs26[i])
tx90p = np.mean(tx90p[3 * (yr - 2010):3 * (yr - 2000), :], axis=0)
tx90p26 = np.mean(tx90p26[3 * (yr26 - 2010):3 * (yr26 - 2000), :], axis=0)
f = interpolate.interp2d(lon, lat[::-1], tx90p)
f26 = interpolate.interp2d(lon, lat[::-1], tx90p26)
tx90p_n96 = f(lon_n96, lat_n96) * .9
tx90p26_n96 = f26(lon_n96, lat_n96) * .9
plotall(tx90p26_n96,
tx90p_n96,
20,
pltlbl=model + ' RCP8.5 - RCP2.6 TX90p')
def maplotr(pdata, model, colormax=1, colormin=-999, title='', precip='no'):
"""
Plots input grid with map of world overlaid
Parameters
----------
plotdata: array
data being plotted
title: str
optional title
"""
from mpl_toolkits.basemap import Basemap, shiftgrid, addcyclic
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from netcdfread import ncread
if colormin == -999:
colormin = -colormax
if model == 'had':
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/All-Hist/mon/tas/item3236_monthly_mean_a011_2006-01_2016-12.nc',
'longitude0')
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/All-Hist/mon/tas/item3236_monthly_mean_a011_2006-01_2016-12.nc',
'latitude0')
if model == 'mir':
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/MIROC5/All-Hist/day/tas/tas_Aday_MIROC5_All-Hist_est1_v2-0_run001_20060101-20161231.nc',
'lat')
lon = np.arange(0, 360, 360 / 256)
if model == 'cam':
lat = np.linspace(-90, 90, 96)
lon = np.linspace(0, 357.5, 144)
plt.figure()
pdata, lon = shiftgrid(180., pdata, lon, start=False)
pdata, lon = addcyclic(pdata, lon)
meshlon, meshlat = np.meshgrid(lon, lat)
m = Basemap(projection='cyl',
llcrnrlat=-90,
urcrnrlat=90,
llcrnrlon=-180,
urcrnrlon=180,
resolution='c')
m.drawcoastlines()
m.drawmapboundary()
x, y = m(meshlon, meshlat)
mycmap2 = plt.cm.YlOrRd(np.arange(256))
mycmap1 = plt.cm.Blues_r(np.arange(256))
my_cmap = np.concatenate((mycmap1, mycmap2), axis=0)
my_cmap[230:282, :] = 1
if precip == 'yes':
my_cmap = my_cmap[::-1]
newcmap = mpl.colors.LinearSegmentedColormap.from_list("newjet", my_cmap)
ctrs = np.linspace(colormin, colormax, 17)
plot = m.contourf(x,
y,
pdata,
ctrs,
cmap=newcmap,
vmin=np.min(ctrs),
vmax=np.max(ctrs),
extend='both')
b = plt.colorbar(plot,
orientation='horizontal',
aspect=50,
shrink=0.75,
spacing='proportional')
b.set_label(label=r'pr (mm day$^{-1}$)')
parallels = m.drawparallels(np.arange(-90., 91., 15.))
meridians = m.drawmeridians(np.arange(-180., 181., 30))
m.drawparallels(parallels, labels=[True, True, True, True])
m.drawmeridians(meridians, labels=[True, True, True, True])
plt.title(title, y=1.08)
plt.show()
def mapplot60(plotdata_sig, plotdata, mask='yes', title=''):
"""
Plots input grid with map of world overlaid
Parameters
----------
plotdata: array
data being plotted
title: str
optional title
"""
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from netcdfread import ncread
lat = np.linspace(90, -90, 145)
lon = np.arange(0, 360, 1.875)
long = np.concatenate((lon, [360]))
fig, axs = plt.subplots(2, 2, facecolor='w',
edgecolor='k', linewidth=2)
# fig.suptitle('North Atlantic Jet Sensitivity', fontsize=20, y=0.96)
for a in range(2):
for b in range(2):
if b == 0:
mx = 1.2
else:
mx = .8
plotdata1 = np.concatenate((plotdata_sig[a, 1-b, :],
np.expand_dims(plotdata_sig[a, 1-b, :, 0],
axis=1)), axis=1) * 1e5
plotdata3 = np.concatenate((plotdata[a, 1-b, :],
np.expand_dims(plotdata[a, 1-b, :, 0],
axis=1)), axis=1) * 1e5
if mask == 'yes':
lsm = ncread('/home/bakerh/Documents/DPhil/CPDN/\
Weather-at-Home_ancilmaker-master/lsm_n96_add.nc', 'lsm')[0, 0, :]
lsm = np.concatenate((lsm, np.expand_dims(lsm[:, 0], axis=1)),
axis=1)
plotdata1 = np.ma.masked_array(plotdata1, lsm)
plotdata3 = np.ma.masked_array(plotdata3, lsm)
meshlon, meshlat = np.meshgrid(long, lat)
ax1 = axs[a, b]
m = Basemap(projection='cyl', llcrnrlat=-60, urcrnrlat=60,
llcrnrlon=0, urcrnrlon=360, resolution='c', ax=ax1)
m.drawcoastlines()
m.drawmapboundary(linewidth=2)
x, y = m(meshlon, meshlat)
mycmap2 = plt.cm.YlOrRd(np.arange(256))
mycmap1 = plt.cm.Blues_r(np.arange(256))
my_cmap = np.concatenate((mycmap1, mycmap2), axis=0)
#my_cmap[239:274, :] = 1
newcmap = mpl.colors.LinearSegmentedColormap.from_list("newjet",
my_cmap)
caxismin, caxismax, ctrs = -mx, mx, np.linspace(-mx, mx, 17)
plot = m.contourf(x, y, plotdata1, ctrs,
cmap=newcmap, vmin=caxismin, vmax=caxismax,
extend='both')
m.contour(x, y, plotdata3, ctrs, colors='k')
ax1.set_ylim(-60, 60)
m.drawparallels(np.arange(-60, 90, 30),
labels=[True, False, False, True], linewidth=0)
m.drawmeridians(np.arange(0, 390, 30),
labels=[True, False, False, True], linewidth=0)
ax1.set_yticks(np.arange(-60., 90., 30.))
ax1.set_xticks(np.arange(0., 390., 30.))
ax1.tick_params(labelleft='off', labelbottom='off', which='major',
direction='out', length=5, width=2)
ax1.set_yticks(np.arange(-60., 70., 10.), minor=True)
ax1.set_xticks(np.arange(0., 370., 10.), minor=True)
ax1.tick_params(labelleft='off', labelbottom='off', which='minor',
direction='out', length=4, width=1)
if a == 0 and b == 0:
ax1.set_ylabel('DJF', fontsize=16, labelpad=25)
ax1.set_title('Latitude', fontsize=16, y=1.08)
if a == 1 and b == 0:
c = fig.colorbar(plot, ax=ax1, orientation='horizontal',
spacing='proportional', aspect=50)
ax1.set_ylabel('JJA', fontsize=16, labelpad=25)
c.set_label(label='Poleward jet latitude shift ($^\circ$ [$\mathregular{1x10^5km^2}$K]$\mathregular{^{-1}}$)', size=16)
if a == 0 and b == 1:
ax1.set_title('Speed', fontsize=16, y=1.08)
if a == 1 and b == 1:
c = fig.colorbar(plot, ax=ax1, orientation='horizontal',
spacing='proportional', aspect=50)
c.set_label(label='Jet speed increase (ms$\mathregular{^{-1}}$ [$\mathregular{1x10^5km^2}$K]$\mathregular{^{-1}}$)', size=16)
plt.subplots_adjust(hspace=0, wspace=0.1, top=.95, bottom=0.05, left=.05,
right=.95)
plt.show()
def cmipw5(v_had):
import glob
from netcdfread import ncread
from scipy import interpolate
lat_n96 = ncread(
'/home/bakerh/Documents/DPhil/CPDN/Weather-at-Home_ancilmaker-master/lsm_n96_add.nc',
'latitude')
lon_n96 = ncread(
'/home/bakerh/Documents/DPhil/CPDN/Weather-at-Home_ancilmaker-master/lsm_n96_add.nc',
'longitude')
models = [
'ACCESS1-0_', 'ACCESS1-3_', 'bcc-csm1-1_', 'bcc-csm1-1-m_', 'BNU-ESM_',
'CanESM2_', 'CCSM4_', 'CESM1-BGC_', 'CESM1-CAM5_', 'CMCC-CESM_',
'CMCC-CM_', 'CMCC-CMS_', 'CNRM-CM5_', 'CSIRO-Mk3-6-0_', 'EC-EARTH_',
'FGOALS-g2_', 'FIO-ESM_', 'GFDL-CM3_', 'GFDL-ESM2G_', 'GFDL-ESM2M_',
'GISS-E2-H-CC_', 'GISS-E2-H_', 'GISS-E2-R-CC_', 'GISS-E2-R_',
'HadGEM2-AO_', 'HadGEM2-CC_', 'HadGEM2-ES_', 'inmcm4_',
'IPSL-CM5A-LR_', 'IPSL-CM5A-MR_', 'IPSL-CM5B-LR_', 'MPI-ESM-LR_',
'MPI-ESM-MR_', 'NorESM1-ME_', 'NorESM1-M_'
]
indices = {}
for k, model in enumerate(models):
a = glob.glob(
'/network/aopp/hera/mad/bakerh/HAPPI/CMIP5/historical/va/va_Amon_'
+ model + '*')
b = glob.glob(
'/network/aopp/hera/mad/bakerh/HAPPI/CMIP5/rcp85/va/va_Amon_' +
model + '*')
m = 6
e = 156
time = np.arange(1850, 2101)
if model == 'bcc-csm1-1_':
time = np.arange(1850, 2301)
elif model == 'IPSL-CM5A-LR_':
time = np.arange(1850, 2301)
elif model == 'CCSM4_':
time = np.arange(1850, 2301)
elif model == 'CNRM-CM5_':
time = np.arange(1850, 2301)
elif model == 'CSIRO-Mk3-6-0_':
time = np.arange(1850, 2301)
elif model == 'GISS-E2-R_':
time = np.arange(1850, 2301)
elif model == 'GISS-E2-H_':
time = np.arange(1850, 2301)
elif model == 'MPI-ESM-LR_':
time = np.arange(1850, 2301)
elif model == 'GFDL-CM3_':
e = 146
time = np.arange(1860, 2101)
elif model == 'GFDL-ESM2G_':
e = 145
time = np.arange(1861, 2101)
elif model == 'GFDL-ESM2M_':
e = 145
time = np.arange(1861, 2101)
elif model == 'HadGEM2-CC_':
m = 5
e = 146
time = np.arange(1860, 2100)
elif model == 'HadGEM2-AO_':
e = 146
time = np.arange(1860, 2100)
elif model == 'HadGEM2-ES_':
m = 5
e = 146
time = np.arange(1860, 2300)
elif model == 'FGOALS-g2_':
m = 6
e = 106
time = np.arange(1900, 2102)
va = ncread(a[0], 'va')[m::12, 9]
va1 = ncread(b[0], 'va')[m::12, 9]
if len(a) > 1:
for i in range(len(a) - 1):
va = np.concatenate((va, ncread(a[i + 1], 'va')[m::12, 9]),
axis=0)
if len(b) > 1:
for i in range(len(b) - 1):
va1 = np.concatenate((va1, ncread(b[i + 1], 'va')[m::12, 9]),
axis=0)
lat = ncread(a[0], 'lat')
lon = ncread(a[0], 'lon')
va = va[:e]
va_j2 = np.concatenate((va, va1), axis=0)
va_j2_96 = np.zeros((len(va_j2), 145, 192))
for j in range(len(va_j2)):
f = interpolate.interp2d(lon, lat[::-1], va_j2[j])
va_j2_96[j] = f(lon_n96, lat_n96)
meshlat = np.zeros([np.ma.size(lon_n96), np.ma.size(lat_n96)])
meshlat[:, :] = lat_n96
meshlatweight = np.cos(meshlat.transpose() * np.pi / 180)
index_j2 = np.zeros(len(va_j2))
for i in range(len(va_j2)):
index_j2[i] = np.sum(
v_had[12:49] * va_j2_96[i, 12:49] *
meshlatweight[12:49]) / (np.sqrt(
np.sum(v_had[12:49] * v_had[12:49] *
meshlatweight[12:49])) * np.sqrt(
np.sum(va_j2_96[i, 12:49] * va_j2_96[i, 12:49] *
meshlatweight[12:49])))
indices[model] = time, index_j2
print(model)
return indices
def rws_fprime_co2(ua200, va200):
from windspharm.standard import VectorWind
from netcdfread import ncread, ncsave
from scipy import interpolate
lon42 = ncread(
'/network/aopp/hera/mad/bakerh/BTmodel_COR/main/inputs/forcing_ghg.nc',
'lon')
lat42 = ncread(
'/network/aopp/hera/mad/bakerh/BTmodel_COR/main/inputs/forcing_ghg.nc',
'lat')
# HadAM3P
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/All-Hist/mon/tas/item3236_monthly_mean_a011_2006-01_2016-12.nc',
'longitude0')
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/Plus15-Future_LCO2/day/ua/item15201_daily_mean_a00b_2090-01_2100-12.nc',
'latitude1')
# MIROC5
# lat = ncread('/network/aopp/hera/mad/bakerh/HAPPI/MIROC5/All-Hist/day/tas/tas_Aday_MIROC5_All-Hist_est1_v2-0_run001_20060101-20161231.nc','lat')
# lon = np.arange(0, 360, 360/256)
# CAM4
#lat = np.linspace(-90, 90, 96)
#lon = np.linspace(0, 357.5, 144)
u = ua200['Plus15-Future_LCO2']
v = va200['Plus15-Future_LCO2']
u_ghg = ua200['Plus15-Future_HCO2']
v_ghg = va200['Plus15-Future_HCO2']
uwnd = np.zeros((64, 128, len(u)))
vwnd = np.zeros((64, 128, len(v)))
uwnd_ghg = np.zeros((64, 128, len(u_ghg)))
vwnd_ghg = np.zeros((64, 128, len(u_ghg)))
u = np.transpose(u, (1, 2, 0))
v = np.transpose(v, (1, 2, 0))
u_ghg = np.transpose(u_ghg, (1, 2, 0))
v_ghg = np.transpose(v_ghg, (1, 2, 0))
for i in range(np.ma.size(uwnd, axis=2)):
h = interpolate.interp2d(lon, lat[::-1], u[:, :, i])
uwnd[:, :, i] = h(lon42, lat42[::-1])
g = interpolate.interp2d(lon, lat[::-1], v[:, :, i])
vwnd[:, :, i] = g(lon42, lat42[::-1])
for i in range(np.ma.size(uwnd_ghg, axis=2)):
h = interpolate.interp2d(lon, lat[::-1], u_ghg[:, :, i])
uwnd_ghg[:, :, i] = h(lon42, lat42[::-1])
g = interpolate.interp2d(lon, lat[::-1], v_ghg[:, :, i])
vwnd_ghg[:, :, i] = g(lon42, lat42[::-1])
w = VectorWind(uwnd, vwnd)
w_ghg = VectorWind(uwnd_ghg, vwnd_ghg)
eta = w.absolutevorticity()
eta_ghg = w_ghg.absolutevorticity()
div = w.divergence()
div_ghg = w_ghg.divergence()
uchi, vchi = w.irrotationalcomponent()
uchi_ghg, vchi_ghg = w_ghg.irrotationalcomponent()
etax, etay = w.gradient(eta)
# etax_ghg, etay_ghg = w_ghg.gradient(eta_ghg)
eta = np.transpose(eta, (2, 0, 1))
eta_ghg = np.transpose(eta_ghg, (2, 0, 1))
div = np.transpose(div, (2, 0, 1))
div_ghg = np.transpose(div_ghg, (2, 0, 1))
etax = np.transpose(etax, (2, 0, 1))
etay = np.transpose(etay, (2, 0, 1))
uchi = np.transpose(uchi, (2, 0, 1))
uchi_ghg = np.transpose(uchi_ghg, (2, 0, 1))
vchi = np.transpose(vchi, (2, 0, 1))
vchi_ghg = np.transpose(vchi_ghg, (2, 0, 1))
f_ghg = -eta * (div_ghg - div) - ((uchi_ghg - uchi) * etax +
(vchi_ghg - vchi) * etay)
meshlon, meshlat = np.meshgrid(lon42, lat42)
'''
ncsave('/home/bakerh/Downloads/vort200_control', lat42, lon42, eta.mean(axis=0)-2*np.sin(meshlat*np.pi/180)*7.2921e-5, 'vorticity')
ncsave('/home/bakerh/Downloads/forcing_ghg', lat42, lon42, f_ghg.mean(axis=0), 'forcing')
'''
month = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
ncsave('/home/bakerh/Downloads/vort200_control', month, lat42, lon42,
eta - 2 * np.sin(meshlat * np.pi / 180) * 7.2921e-5, 'vorticity')
ncsave('/home/bakerh/Downloads/forcing_ghg', month, lat42, lon42, f_ghg,
'forcing')
def ming_forcing(ua, va, psl):
from windspharm.standard import VectorWind
from netcdfread import ncread, ncsave
from scipy import interpolate
lon42 = ncread(
'/network/aopp/hera/mad/bakerh/BTmodel_COR/main/inputs/forcing_ghg.nc',
'lon')
lat42 = ncread(
'/network/aopp/hera/mad/bakerh/BTmodel_COR/main/inputs/forcing_ghg.nc',
'lat')
lon = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/All-Hist/mon/tas/item3236_monthly_mean_a011_2006-01_2016-12.nc',
'longitude0')
lat = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/Plus15-Future_LCO2/day/ua/item15201_daily_mean_a00b_2090-01_2100-12.nc',
'latitude1')
lat145 = ncread(
'/network/aopp/hera/mad/bakerh/HAPPI/HadAM3P-N96/All-Hist/mon/tas/item3236_monthly_mean_a011_2006-01_2016-12.nc',
'latitude0')
u = ua['All-Nat'][1]
v = va['All-Nat'][1]
u_sst = ua['GHG-Nat'][1]
v_sst = va['GHG-Nat'][1]
u_ghg = ua['SST-Nat'][1]
v_ghg = va['SST-Nat'][1]
p = psl['All-Nat'][1]
p_sst = psl['GHG-Nat'][1]
p_ghg = psl['SST-Nat'][1]
uwnd = np.zeros((64, 128, len(u)))
vwnd = np.zeros((64, 128, len(v)))
uwnd_sst = np.zeros((64, 128, len(u_sst)))
vwnd_sst = np.zeros((64, 128, len(u_sst)))
uwnd_ghg = np.zeros((64, 128, len(u_ghg)))
vwnd_ghg = np.zeros((64, 128, len(u_ghg)))
ps = np.zeros((64, 128, len(p)))
ps_sst = np.zeros((64, 128, len(p_sst)))
ps_ghg = np.zeros((64, 128, len(p_ghg)))
u = np.transpose(u, (1, 2, 0))
v = np.transpose(v, (1, 2, 0))
u_sst = np.transpose(u_sst, (1, 2, 0))
v_sst = np.transpose(v_sst, (1, 2, 0))
u_ghg = np.transpose(u_ghg, (1, 2, 0))
v_ghg = np.transpose(v_ghg, (1, 2, 0))
p = np.transpose(p, (1, 2, 0))
p_sst = np.transpose(p_sst, (1, 2, 0))
p_ghg = np.transpose(p_ghg, (1, 2, 0))
for i in range(np.ma.size(uwnd, axis=2)):
h = interpolate.interp2d(lon, lat[::-1], u[:, :, i])
uwnd[:, :, i] = h(lon42, lat42[::-1])
g = interpolate.interp2d(lon, lat[::-1], v[:, :, i])
vwnd[:, :, i] = g(lon42, lat42[::-1])
j = interpolate.interp2d(lon, lat145[::-1], p[:, :, i])
ps[:, :, i] = j(lon42, lat42[::-1])
for i in range(np.ma.size(uwnd_sst, axis=2)):
h = interpolate.interp2d(lon, lat[::-1], u_sst[:, :, i])
uwnd_sst[:, :, i] = h(lon42, lat42[::-1])
g = interpolate.interp2d(lon, lat[::-1], v_sst[:, :, i])
vwnd_sst[:, :, i] = g(lon42, lat42[::-1])
j = interpolate.interp2d(lon, lat145[::-1], p_sst[:, :, i])
ps_sst[:, :, i] = j(lon42, lat42[::-1])
for i in range(np.ma.size(uwnd_ghg, axis=2)):
h = interpolate.interp2d(lon, lat[::-1], u_ghg[:, :, i])
uwnd_ghg[:, :, i] = h(lon42, lat42[::-1])
g = interpolate.interp2d(lon, lat[::-1], v_ghg[:, :, i])
vwnd_ghg[:, :, i] = g(lon42, lat42[::-1])
j = interpolate.interp2d(lon, lat145[::-1], p_ghg[:, :, i])
ps_ghg[:, :, i] = j(lon42, lat42[::-1])
w = VectorWind(uwnd, vwnd)
w_sst = VectorWind(uwnd_sst, vwnd_sst)
w_ghg = VectorWind(uwnd_ghg, vwnd_ghg)
psx, psy = w.gradient(ps)
psx_sst, psy_sst = w_sst.gradient(ps_sst)
psx_ghg, psy_ghg = w_ghg.gradient(ps_ghg)
omega = np.mean(uwnd * psx + vwnd * psy, axis=2)
omega_sst = uwnd_sst * psx_sst + vwnd_sst * psy_sst
omega_ghg = uwnd_ghg * psx_ghg + vwnd_ghg * psy_ghg
omega_sst = np.transpose(omega_sst, (2, 0, 1))
omega_ghg = np.transpose(omega_ghg, (2, 0, 1))
oforc_sst = np.mean(omega_sst - omega, axis=0)
oforc_ghg = np.mean(omega_ghg - omega, axis=0)
return omega, oforc_sst, oforc_ghg
def main(dataset='ncep2'):
def nao_region_ncep2(field):
sindices = np.zeros((3 * (np.shape(field)[0] // 12 - 1)))
windices = np.zeros((3 * (np.shape(field)[0] // 12 - 1)))
for i in range((np.shape(field)[0] // 12 - 1)):
sindices[3 * i:3 *
(i + 1)] = [17 + 12 * i, 18 + 12 * i, 19 + 12 * i]
windices[3 * i:3 *
(i + 1)] = [11 + 12 * i, 12 + 12 * i, 13 + 12 * i]
sindices = sindices.astype(int)
windices = windices.astype(int)
field1 = field[:, 4:29, 108:]
field2 = field[:, 4:29, :17]
field_r = np.concatenate((field1, field2), axis=2)
field_w = field_r[windices]
field_s = field_r[sindices]
field_w = np.add.reduceat(field_w, range(0, len(field_w), 3)) / 3
field_s = np.add.reduceat(field_s, range(0, len(field_s), 3)) / 3
lat_r = np.arange(80, 17.5, -2.5)
lon_r = np.arange(-90, 42.5, 2.5)
return field_w, field_s, lat_r, lon_r
def nao_region_ncar20c(field):
sindices = np.zeros((3 * (np.shape(field)[0] // 12 - 1)))
windices = np.zeros((3 * (np.shape(field)[0] // 12 - 1)))
for i in range((np.shape(field)[0] // 12 - 1)):
sindices[3 * i:3 *
(i + 1)] = [17 + 12 * i, 18 + 12 * i, 19 + 12 * i]
windices[3 * i:3 *
(i + 1)] = [11 + 12 * i, 12 + 12 * i, 13 + 12 * i]
sindices = sindices.astype(int)
windices = windices.astype(int)
field1 = field[:, 5:36, 135:]
field2 = field[:, 5:36, :21]
field_r = np.concatenate((field1, field2), axis=2)
field_w = field_r[windices]
field_s = field_r[sindices]
field_w = np.add.reduceat(field_w, range(0, len(field_w), 3)) / 3
field_s = np.add.reduceat(field_s, range(0, len(field_s), 3)) / 3
lat_r = np.arange(80, 18, -2)
lon_r = np.arange(-90, 42, 2)
return field_w, field_s, lat_r, lon_r
def nao_region_era20c(field):
sindices = np.zeros((3 * (np.shape(field)[0] // 12 - 1)))
windices = np.zeros((3 * (np.shape(field)[0] // 12 - 1)))
for i in range((np.shape(field)[0] // 12 - 1)):
sindices[3 * i:3 *
(i + 1)] = [17 + 12 * i, 18 + 12 * i, 19 + 12 * i]
windices[3 * i:3 *
(i + 1)] = [11 + 12 * i, 12 + 12 * i, 13 + 12 * i]
sindices = sindices.astype(int)
windices = windices.astype(int)
field1 = field[:, 10:71, 270:]
field2 = field[:, 10:71, :41]
field_r = np.concatenate((field1, field2), axis=2)
field_w = field_r[windices]
field_s = field_r[sindices]
field_w = np.add.reduceat(field_w, range(0, len(field_w), 3)) / 3
field_s = np.add.reduceat(field_s, range(0, len(field_s), 3)) / 3
lat_r = np.arange(80, 19, -1)
lon_r = np.arange(-90, 41, 1)
return field_w, field_s, lat_r, lon_r
from scipy import interpolate
lat_n96 = np.arange(80, 18.75, -1.25)
lon_n96 = np.linspace(-90, 39.375, 70)
if dataset == 'ncep2':
# NCEP2 MSLP 1979-2017 inclusive
mslp = ncread(
'/network/aopp/hera/mad/bakerh/Reanalyses/NCEP-DOE/mslp.mon.mean.nc',
'mslp')[:468] / 100
mslp_w, mslp_s, lat_r, lon_r = nao_region_ncep2(mslp)
mslp_w96 = np.zeros((np.shape(mslp_w)[0], 49, 70))
mslp_s96 = np.zeros((np.shape(mslp_w)[0], 49, 70))
for i in range(np.shape(mslp_w)[0]):
f = interpolate.interp2d(lon_r, lat_r[::-1], mslp_w[i])
mslp_w96[i] = f(lon_n96, lat_n96)
f = interpolate.interp2d(lon_r, lat_r[::-1], mslp_s[i])
mslp_s96[i] = f(lon_n96, lat_n96)
eofs = np.zeros((2, 49, 70))
var = np.zeros((2, 3))
nao = np.zeros((2, len(mslp_w96)))
eofs[0], var[0] = eof_clim(mslp_w96, lat_n96, lon_n96, 1)
nao[0] = nao_indivyear(eofs[0], mslp_w96, lat_n96, lon_n96)
eofs[1], var[1] = eof_clim(mslp_s96, lat_n96, lon_n96, 1)
nao[1] = nao_indivyear(eofs[1], mslp_s96, lat_n96, lon_n96)
if dataset == 'ncar20c':
# NCAR-20thC MSLP 1871-2012 inclusive
mslp = ncread(
'/network/aopp/hera/mad/bakerh/Reanalyses/NCAR-20thC/mslp/prmsl.mon.mean.nc',
'prmsl') / 100
mslp_w, mslp_s, lat_r, lon_r = nao_region_ncar20c(mslp)
mslp_w96 = np.zeros((np.shape(mslp_w)[0], 49, 70))
mslp_s96 = np.zeros((np.shape(mslp_w)[0], 49, 70))
for i in range(np.shape(mslp_w)[0]):
f = interpolate.interp2d(lon_r, lat_r[::-1], mslp_w[i])
mslp_w96[i] = f(lon_n96, lat_n96)
f = interpolate.interp2d(lon_r, lat_r[::-1], mslp_s[i])
mslp_s96[i] = f(lon_n96, lat_n96)
eofs = np.zeros((2, 49, 70))
var = np.zeros((2, 3))
nao = np.zeros((2, len(mslp_w96)))
eofs[0], var[0] = eof_clim(mslp_w96, lat_n96, lon_n96, 1)
nao[0] = nao_indivyear(eofs[0], mslp_w96, lat_n96, lon_n96)
eofs[1], var[1] = eof_clim(mslp_s96, lat_n96, lon_n96, 1)
nao[1] = nao_indivyear(eofs[1], mslp_s96, lat_n96, lon_n96)
if dataset == 'era20c':
# ERA20C MSLP 1900-2010 inclusive
mslp = ncread(
'/network/aopp/hera/mad/bakerh/Reanalyses/ERA20C/ERA20C_mslp_daily_1900_2010.nc',
'msl') / 100
mslp_w, mslp_s, lat_r, lon_r = nao_region_era20c(mslp)
mslp_w96 = np.zeros((np.shape(mslp_w)[0], 49, 70))
mslp_s96 = np.zeros((np.shape(mslp_w)[0], 49, 70))
for i in range(np.shape(mslp_w)[0]):
f = interpolate.interp2d(lon_r, lat_r[::-1], mslp_w[i])
mslp_w96[i] = f(lon_n96, lat_n96)
f = interpolate.interp2d(lon_r, lat_r[::-1], mslp_s[i])
mslp_s96[i] = f(lon_n96, lat_n96)
eofs = np.zeros((2, 49, 70))
var = np.zeros((2, 3))
nao = np.zeros((2, len(mslp_w96)))
eofs[0], var[0] = eof_clim(mslp_w96, lat_n96, lon_n96, 1)
nao[0] = nao_indivyear(eofs[0], mslp_w96, lat_n96, lon_n96)
eofs[1], var[1] = eof_clim(mslp_s96, lat_n96, lon_n96, 1)
nao[1] = nao_indivyear(eofs[1], mslp_s96, lat_n96, lon_n96)
if dataset == 'hadam3p':
import glob
a = glob.glob(
'/network/aopp/hera/mad/bakerh/RPM/cpdn_extract_scripts-master/extracted_data/batch_577/atmos/item16222_monthly_mean/*'
)
mslp_w96 = np.zeros((len(a), 49, 70))
mslp_s96 = np.zeros((len(a), 49, 70))
for i, item in enumerate(a):
mslp = ncread(item, 'item16222_monthly_mean')[:, 0, :] / 100
mslp_w96[i], mslp_s96[i] = nao_region_hadam3p(mslp)
print(str(i))
eofs = np.zeros((2, 49, 70))
var = np.zeros((2, 3))
nao = np.zeros((2, len(mslp_w96)))
eofs[0], var[0] = eof_clim(mslp_w96, lat_n96, lon_n96, 1)
nao[0] = nao_indivyear(eofs[0], mslp_w96, lat_n96, lon_n96)
eofs[1], var[1] = eof_clim(mslp_s96, lat_n96, lon_n96, 1)
nao[1] = nao_indivyear(eofs[1], mslp_s96, lat_n96, lon_n96)
return eofs, var, nao
def response_1(v_model, model='ghg', month='J', r=[0, 90, 0, 360]):
def eofweight(unweighted, lat, lon):
'''
Outputs weighted data for projections (i.e. sqrt cos lat)
Parameters
----------
unweighted: array
unweighted data
lat: array
latitudes
lon: array
lon values
Outputs
-------
weighted: array
weighted data
'''
meshlat = np.zeros([np.ma.size(lon), np.ma.size(lat)])
meshlat[:, :] = lat
meshlatweight = np.sqrt(np.cos(meshlat.transpose() * np.pi / 180))
weighted = unweighted * meshlatweight
return weighted
def eof_response(response,
eofn,
lati,
long,
region=[-90, 90, 0, 360],
weigh='yes'):
'''
Projects response onto eofn
Parameters
----------
response: array
data to project on to eofn
eofn: array
nth eof
lat: array
latitude of data
lon: array
longitude of data
Returns
-------
projection: array
response projected on eofn
'''
lat = lati[(region[0] <= lati) & (lati <= region[1])]
lon = long[(region[2] <= long) & (long <= region[3])]
eof_region = eofn.copy()[(region[0] <= lati) & (lati <= region[1]), :]
eof_region = eof_region[:, (region[2] <= long) & (long <= region[3])]
r_region = response.copy()[:, (region[0] <= lati) &
(lati <= region[1]), :]
r_region = r_region[:, :, (region[2] <= long) & (long <= region[3])]
responsew = eofweight(r_region, lat, lon)
eofn_w = eofweight(eof_region.copy(), lat, lon)
projection = np.zeros(len(response))
if weigh == 'yes':
for i in range(len(response)):
projection[i] = (np.sum(responsew[i] * eofn_w) /
(np.sum(eofn_w * eofn_w)))
else:
for i in range(len(response)):
projection[i] = (np.sum(responsew[i] * eofn_w) /
((np.sum(responsew[i] * responsew[i]))))
return projection
import numpy as np
from netcdfread import ncread
import glob
lon = ncread(
'/network/aopp/hera/mad/bakerh/BTmodel_COR/main/inputs/forcing_ghg.nc',
'lon')
lati = ncread(
'/network/aopp/hera/mad/bakerh/BTmodel_COR/main/inputs/forcing_ghg.nc',
'lat')
lat = np.copy(lati[::-1])
a = glob.glob(
'/network/aopp/hera/mad/bakerh/BTmodel_COR/main/outputs/single/' +
model + '/' + month + '/*')
v_f = np.zeros((64, 21, 64, 128))
for i in range(64):
v_f[i] = ncread(a[i], 'V')
v_f = (np.mean(v_f[:, 16:], axis=1) - v_f[:, 0]) * 1e6
w = eof_response(v_f, v_model, lati, lon, region=r, weigh='yes')
f_per = eof_response(v_f, v_model, lati, lon, region=r, weigh='no')
weights = np.zeros((64, 128))
forcing_p = np.zeros((64, 128))
for LONraster in range(1, 65):
w1 = w[LONraster - 1]
f1 = f_per[LONraster - 1]
if LONraster == 1:
for ii in range(int(LONraster * 2 - 2),
int(LONraster * 2 - 2 + 3)):
for jj in range(34 - 2, 34 + 3):
weights[jj, ii] += w1 * (
(np.cos(0.5 * np.pi *
(lon[int(ii)] - lon[int(LONraster * 2 - 2)]) /
(lon[3] - lon[1])))**2) * (
(np.cos(0.5 * np.pi * (lat[jj] - lat[34]) /
(lat[31] - lat[33])))**2)
forcing_p[jj, ii] += f1 * (
(np.cos(0.5 * np.pi *
(lon[int(ii)] - lon[int(LONraster * 2 - 2)]) /
(lon[3] - lon[1])))**2) * (
(np.cos(0.5 * np.pi * (lat[jj] - lat[34]) /
(lat[31] - lat[33])))**2)
for ii in range(126, 128):
for jj in range(34 - 2, 34 + 3):
weights[jj, ii] += w1 * (
(np.cos(0.5 * np.pi * (lon[int(ii)] - 360) /
(lon[3] - lon[1])))**2) * (
(np.cos(0.5 * np.pi * (lat[jj] - lat[34]) /
(lat[31] - lat[33])))**2)
forcing_p[jj, ii] += f1 * (
(np.cos(0.5 * np.pi * (lon[int(ii)] - 360) /
(lon[3] - lon[1])))**2) * (
(np.cos(0.5 * np.pi * (lat[jj] - lat[34]) /
(lat[31] - lat[33])))**2)
elif LONraster == 64:
for ii in range(int(LONraster * 2 - 2 - 2),
int(LONraster * 2 - 2 + 2)):
for jj in range(34 - 2, 34 + 3):
weights[jj, ii] += w1 * (
(np.cos(0.5 * np.pi *
(lon[int(ii)] - lon[int(LONraster * 2 - 2)]) /
(lon[3] - lon[1])))**2) * (
(np.cos(0.5 * np.pi * (lat[jj] - lat[34]) /
(lat[31] - lat[33])))**2)
forcing_p[jj, ii] += f1 * (
(np.cos(0.5 * np.pi *
(lon[int(ii)] - lon[int(LONraster * 2 - 2)]) /
(lon[3] - lon[1])))**2) * (
(np.cos(0.5 * np.pi * (lat[jj] - lat[34]) /
(lat[31] - lat[33])))**2)
else:
for ii in range(int(LONraster * 2 - 2 - 2),
int(LONraster * 2 - 2 + 3)):
for jj in range(34 - 2, 34 + 3):
weights[jj, ii] += w1 * (
(np.cos(0.5 * np.pi *
(lon[int(ii)] - lon[int(LONraster * 2 - 2)]) /
(lon[3] - lon[1])))**2) * (
(np.cos(0.5 * np.pi * (lat[jj] - lat[34]) /
(lat[31] - lat[33])))**2)
forcing_p[jj, ii] += f1 * (
(np.cos(0.5 * np.pi *
(lon[int(ii)] - lon[int(LONraster * 2 - 2)]) /
(lon[3] - lon[1])))**2) * (
(np.cos(0.5 * np.pi * (lat[jj] - lat[34]) /
(lat[31] - lat[33])))**2)
weights = weights[::-1, :]
forcing_p = forcing_p[::-1, :] * 2.5e-11
v_perfect = np.sum(v_f.transpose(1, 2, 0) * f_per, axis=2)
return weights, forcing_p, v_perfect, w, f_per
def reconstruct_nao(gto, season='djf', rmask=np.ones((145, 192))):
from scipy import interpolate
from mpl_toolkits.basemap import shiftgrid
lat_n96 = np.linspace(90, -90, 145)
lon_n96 = np.linspace(0, 358.125, 192)
lat_sst = np.linspace(89.5, -89.5, 180)
lon_sst = np.linspace(-179.5, 179.5, 360)
lon_sst_shift = np.linspace(.5, 359.5, 360)
sindices = np.zeros((147 * 3))
windices = np.zeros((147 * 3))
if season == 'djf':
months = 3
sindices = np.zeros((147 * 3))
windices = np.zeros((147 * 3))
for i in range(147):
sindices[3 * i:3 *
(i + 1)] = [17 + 12 * i, 18 + 12 * i, 19 + 12 * i]
windices[3 * i:3 *
(i + 1)] = [11 + 12 * i, 12 + 12 * i, 13 + 12 * i]
if season == 'ndj':
months = 3
sindices = np.zeros((147 * 3))
windices = np.zeros((147 * 3))
for i in range(147):
sindices[3 * i:3 *
(i + 1)] = [16 + 12 * i, 17 + 12 * i, 18 + 12 * i]
windices[3 * i:3 *
(i + 1)] = [10 + 12 * i, 11 + 12 * i, 12 + 12 * i]
if season == 'son':
months = 3
sindices = np.zeros((147 * 3))
windices = np.zeros((147 * 3))
for i in range(147):
sindices[3 * i:3 *
(i + 1)] = [14 + 12 * i, 15 + 12 * i, 16 + 12 * i]
windices[3 * i:3 * (i + 1)] = [8 + 12 * i, 9 + 12 * i, 10 + 12 * i]
if season == 'nd':
months = 2
sindices = np.zeros((147 * 2))
windices = np.zeros((147 * 2))
for i in range(147):
sindices[2 * i:2 * (i + 1)] = [16 + 12 * i, 17 + 12 * i]
windices[2 * i:2 * (i + 1)] = [10 + 12 * i, 11 + 12 * i]
if season == 'dj':
months = 2
sindices = np.zeros((147 * 2))
windices = np.zeros((147 * 2))
for i in range(147):
sindices[2 * i:2 * (i + 1)] = [17 + 12 * i, 18 + 12 * i]
windices[2 * i:2 * (i + 1)] = [11 + 12 * i, 12 + 12 * i]
if season == 'n':
months = 1
sindices = np.zeros((147))
windices = np.zeros((147))
for i in range(147):
sindices[i] = 16 + 12 * i
windices[i] = 10 + 12 * i
if season == 'd':
months = 1
sindices = np.zeros((147))
windices = np.zeros((147))
for i in range(147):
sindices[i] = 17 + 12 * i
windices[i] = 11 + 12 * i
if season == 'on':
months = 2
sindices = np.zeros((147 * 2))
windices = np.zeros((147 * 2))
for i in range(147):
sindices[2 * i:2 * (i + 1)] = [15 + 12 * i, 16 + 12 * i]
windices[2 * i:2 * (i + 1)] = [9 + 12 * i, 11 + 12 * i]
sindices = sindices.astype(int)
windices = windices.astype(int)
gto_interp = np.zeros((2, 180, 360))
sst = ncread(
'/network/aopp/hera/mad/bakerh/Reanalyses/HadISST/HadISST_sst.nc',
'sst')
sst_0 = np.where(sst < -999, 0, sst)
for i in range(2):
f = interpolate.interp2d(lon_n96, lat_n96[::-1], gto[i, :])
gto_interp[i, :] = f(lon_sst_shift, lat_sst)
gto_interp[i], lon1 = shiftgrid(181.,
gto_interp[i],
lon_sst_shift,
start=False)
meshlat = np.zeros([180, 360])
meshlat[:, :] = np.expand_dims(lat_sst, axis=1)
meshlatweight = np.cos(
meshlat * np.pi / 180) * 6371**2 * 1.25 * 1.875 * (np.pi / 180)**2
nao_recon_monthly = np.zeros((2, months * 147))
nao_recon = np.zeros((2, 147))
sst_w = sst_0[windices]
sst_s = sst_0[sindices]
sst_w = sst_w * meshlatweight
sst_s = sst_s * meshlatweight
'''
OLD Rmask code
lsm = ncread('/home/bakerh/Documents/DPhil/CPDN/\
Weather-at-Home_ancilmaker-master/lsm_n96_add.nc', 'lsm')[0, 0, :]
lsm1 = np.ones((145, 192))
for i in range(145):
for j in range(192):
if lsm[i, j] == 0:
lsm1[i, j] = 0
for y in range(36):
nao_anom[0, y] = np.nansum(wdataSST[y]*gto[0]*(1-lsm1)*rmask)/20 # 2beta
nao_anom[1, y] = np.nansum(sdataSST[y]*gto[1]*(1-lsm1)*rmask)/20
'''
for y in range(147 * months):
nao_recon_monthly[0, y] = np.nansum(
sst_w[y] * gto_interp[0]) / 20 # 2beta
nao_recon_monthly[1, y] = np.nansum(sst_s[y] * gto_interp[1]) / 20
if months == 1:
for y in range(147):
nao_recon[0, y] = nao_recon_monthly[0, months * y]
nao_recon[1, y] = nao_recon_monthly[1, months * y]
else:
for y in range(147):
nao_recon[0, y] = np.mean(nao_recon_monthly[0, months * y:months *
(y + 1)])
nao_recon[1, y] = np.mean(nao_recon_monthly[1, months * y:months *
(y + 1)])
return nao_recon
def maplotter(pdata, colormax=1, colormin=-999, title='', output='yes'):
"""
Plots input grid with map of world overlaid
Parameters
----------
plotdata: array
data being plotted
title: str
optional title
"""
from mpl_toolkits.basemap import Basemap, shiftgrid, addcyclic
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from netcdfread import ncread
if colormin == -999:
colormin = -colormax
lon = ncread(
'/network/aopp/hera/mad/bakerh/BTmodel_COR/main/inputs/forcing_ghg.nc',
'lon')
lat = ncread(
'/network/aopp/hera/mad/bakerh/BTmodel_COR/main/inputs/forcing_ghg.nc',
'lat')
pdata, lon = shiftgrid(180., pdata, lon, start=False)
pdata, lon = addcyclic(pdata, lon)
plt.figure()
meshlon, meshlat = np.meshgrid(lon, lat)
m = Basemap(projection='cyl',
llcrnrlat=-90,
urcrnrlat=90,
llcrnrlon=-180,
urcrnrlon=180,
resolution='c')
m.drawcoastlines()
m.drawmapboundary()
x, y = m(meshlon, meshlat)
mycmap2 = plt.cm.YlOrRd(np.arange(256))
mycmap1 = plt.cm.Blues_r(np.arange(256))
my_cmap = np.concatenate((mycmap1, mycmap2), axis=0)
my_cmap[230:282, :] = 1
newcmap = mpl.colors.LinearSegmentedColormap.from_list("newjet", my_cmap)
ctrs = np.linspace(colormin, colormax, 17)
plot = m.contourf(x,
y,
pdata,
ctrs,
cmap=newcmap,
vmin=np.min(ctrs),
vmax=np.max(ctrs),
extend='both')
b = plt.colorbar(plot,
orientation='horizontal',
aspect=50,
shrink=0.75,
spacing='proportional')
b.set_label(label=r'V200 (1x10$^{-6}$ ms$^{-1}$)')
#b.set_label(label=r'Forcing (1x10$^{-11}$ s$^{-2}$)')
#b.set_label(label=r'Weights')
parallels = m.drawparallels(np.arange(-90., 91., 15.))
meridians = m.drawmeridians(np.arange(-180., 181., 30))
m.drawparallels(parallels, labels=[True, True, True, True])
m.drawmeridians(meridians, labels=[True, True, True, True])
plt.title(title, y=1.08)
plt.show()
