def p_cent_rate(data, days=False):
"""
Inputs:
data - xarray dataset climatology including precipitation as either precipitation or as convection_rain and condensation_rain
days - instructs if data is in daily or pentad means
Returns:
dpcentdt - rate of movement of the precipitation centroid
dpcentdt2 - rate of acceleration of the precipitation centroid
"""
# Get total precip
try:
data['precipitation'] = data.condensation_rain + data.convection_rain
except:
data['precipitation'] = data.precipitation
data['precipitation'] = make_sym(data.precipitation)
# Locate precipitation centroid
precip_centroid(data)
# If units in are days rather than pentads (e.g. for shorter years) convert to pentads
if days:
dpcentdt = gr.ddt(data.p_cent, secperunit=86400.) * 86400.
else:
dpcentdt = gr.ddt(data.p_cent) * 86400.
dpcentdt2 = gr.ddt(dpcentdt, secperunit=86400.) * 86400.
return dpcentdt, dpcentdt2
def rate_at_eq(runs, do_make_sym=True, days=None):
dpdt_eq = []
if days == None:
days = [False] * len(runs)
j = 0
for run in runs:
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
if do_make_sym: # If symmetric data is wanted, average over both hemispheres (NB currently only set up for a climatology 2/05/18)
data['precipitation'] = make_sym(data.precipitation)
# Locate precipitation centroid
precip_centroid(data)
# Get rate of movement of precip centroid
if days[j]:
dpcentdt = gr.ddt(data.p_cent, secperunit=86400.) * 86400.
else:
dpcentdt = gr.ddt(data.p_cent) * 86400.
#dpcentdt_max = dpcentdt_pmask.where(dpcentdt_pmask==dpcentdt_pmask.max('xofyear'),drop=True) # Find the maximum rate
p_cent = np.abs(data.p_cent.where(dpcentdt >= 0.))
dpdt_eq_j = dpcentdt.where(p_cent == p_cent.min('xofyear'), drop=True)
dpdt_eq.append(dpdt_eq_j.values[0])
j = j + 1
return np.asarray(dpdt_eq)
def abs_vort_dt_at_pcent(run, rot_fac=1., lev=150.,lat_bound=45., res=0.01, interp=True):
'''Calculate the (normalised) vorticity tendency at the precipitation centroid'''
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' + run + '.nc')
# Interpolate in time if wanted
if interp:
times_new = np.arange(1., 72.2, 0.2)
precip_new = time_interp(data.precipitation, times_new)
u_new = time_interp(data.ucomp.sel(pfull=lev), times_new)
v_new = time_interp(data.vcomp.sel(pfull=lev), times_new)
else:
precip_new = data.precipitation
u_new = data.ucomp.sel(pfull=lev)
v_new = data.vcomp.sel(pfull=lev)
# Find precipitation centroid
precip_new = make_sym(precip_new)
data = xr.Dataset({'precipitation': precip_new}, coords=precip_new.coords)
precip_centroid(data, lat_bound=lat_bound, res=res)
# Calculate vorticity
v_dx = gr.ddx(v_new) # dvdx
u_dy = gr.ddy(u_new) # dudy
omega = 7.2921150e-5 * rot_fac
f = 2 * omega * np.sin(data.lat *np.pi/180)
vor = (v_dx - u_dy + f)*86400.
div = gr.ddx(u_new) + gr.ddy(v_new)
stretching_mean = (-86400. * vor * div).mean('lon')
vor = make_sym(vor, asym=True)
stretching_mean = make_sym(stretching_mean, asym=True)
# Take time derivative of absolute vorticity
if interp:
dvordt = gr.ddt(vor.mean('lon'))*86400.*5.
else:
dvordt = gr.ddt(vor.mean('lon'))*86400.
# Also normalise this by the value of vorticity
dvordtvor = dvordt/vor.mean('lon')
#dvordtvor = stretching_mean/vor.mean('lon')
# Interpolate vorticity in latitude to match precipitation centroid lats
lats = [data.lat[i] for i in range(len(data.lat)) if data.lat[i] >= -lat_bound and data.lat[i] <= lat_bound]
lats_new = np.arange(-lat_bound, lat_bound+res, res)
stretching_mean = lat_interp(stretching_mean.sel(lat=lats), lats_new)
dvordt = lat_interp(dvordt.sel(lat=lats), lats_new)
dvordtvor = lat_interp(dvordtvor.sel(lat=lats), lats_new)
# Get and return dvordt and dvordtvor at the precipitation centroid, as well as the precipitation centroid itself
st_pcent = [float(stretching_mean[i,:].sel(lat=data.p_cent.values[i]).values) for i in range(len(data.xofyear))]
st_pcent = xr.DataArray(np.asarray(st_pcent), coords=[stretching_mean.xofyear.values], dims=['xofyear'])
dvordt_pcent = [float(dvordt[i,:].sel(lat=data.p_cent.values[i]).values) for i in range(len(data.xofyear))]
dvordt_pcent = xr.DataArray(np.asarray(dvordt_pcent), coords=[dvordt.xofyear.values], dims=['xofyear'])
dvordtvor_pcent = [float(dvordtvor[i,:].sel(lat=data.p_cent.values[i]).values) for i in range(len(data.xofyear))]
dvordtvor_pcent = xr.DataArray(np.asarray(dvordtvor_pcent), coords=[dvordtvor.xofyear.values], dims=['xofyear'])
return dvordt_pcent, dvordtvor_pcent, data.p_cent, st_pcent
def abs_vort_at_pcent_plot(runs, rot_facs, colors, ax=None, lev=150., filename='abs_vort_dtvor_pcent', interp=True, dvordt=False, period_facs=None):
plot_dir = '/scratch/rg419/plots/paper_2_figs/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
rcParams['figure.figsize'] = 6.5, 4
rcParams['font.size'] = 14
if ax==None:
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax=ax1
if period_facs==None:
period_facs = [1.]*len(runs)
for i in range(len(runs)):
dvordt_pcent, dvordtvor_pcent, pcent, st_pcent = abs_vort_dt_at_pcent(runs[i], rot_facs[i], interp=interp)
if interp:
dpcentdt = gr.ddt(pcent)*86400.*5.*period_facs[i]
else:
dpcentdt = gr.ddt(pcent)*86400.*period_facs[i]
times = [dvordt_pcent.xofyear[j] for j in range(len(dvordt_pcent.xofyear)) if dvordt_pcent.xofyear[j] >= 40*period_facs[i] and dvordt_pcent.xofyear[j] <= 55*period_facs[i]]
#times = [dvordt_pcent.xofyear[i] for i in range(len(dvordt_pcent.xofyear)) if dvordt_pcent.xofyear[i] >= 0 and dvordt_pcent.xofyear[i] <= 10000]
dvordtvor_pcent = dvordtvor_pcent.where(((dvordtvor_pcent < 0.02) & (dvordtvor_pcent > -0.04)))
if dvordt:
#ax.plot(dpcentdt.sel(xofyear=times), dvordt_pcent.sel(xofyear=times), color=colors[i], linewidth=2)
ax.plot(pcent.sel(xofyear=times), dvordt_pcent.sel(xofyear=times), color=colors[i], linewidth=2)
ax.set_ylabel('d$\zeta$/dt at precip. centroid')
else:
ax.plot(dpcentdt.sel(xofyear=times), dvordtvor_pcent.sel(xofyear=times), color=colors[i], linewidth=2)
#ax.plot(pcent.sel(xofyear=times), dvordtvor_pcent.sel(xofyear=times), color=colors[i], linewidth=2)
ax.set_ylim([-0.04,0.02])
#ax.set_ylim([-0.1,0.1])
ax.set_ylabel('$1/(f+\zeta)$*d$\zeta$/dt at ITCZ')
ax.set_xlabel('ITCZ migration rate')
ax.set_xlim([0.,0.6])
ax.grid(True,linestyle=':')
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.85, box.height])
legend = ax.legend(rot_facs, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., title='$\Omega$/$\Omega_{E}$', fontsize=10)
legend.get_title().set_fontsize(10)
if ax==None:
plt.subplots_adjust(left=0.18, right=0.8, top=0.95, bottom=0.15)
plt.savefig(plot_dir+filename+'.pdf', format='pdf')
plt.close()
def p_cent_rate(data, days=False):
# Locate precipitation centroid
precip_centroid(data)
# If units in are days rather than pentads (e.g. for shorter years) convert to pentads
if days:
data['xofyear'] = data['xofyear'] / 5.
# Get rate of movement of precip centroid
dpcentdt = gr.ddt(data.p_cent) * 86400.
dpcentdt2 = gr.ddt(dpcentdt, secperunit=86400.) * 86400.
return dpcentdt, dpcentdt2
def p_cent_rate_max(runs, days=None):
# Get the maximum rate of change of the precipitation centroid latitude, and the latitude at which this occurs.
max_rate = []
max_rate_lat = []
if days==None:
days=[False]*len(runs)
j=0
for run in runs:
# Open dataset
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run + '.nc')
# Get total precip
try:
data['precipitation'] = data.condensation_rain + data.convection_rain
except:
data['precipitation'] = data.precipitation
data['precipitation'] = make_sym(data.precipitation)
# Locate precipitation centroid
precip_centroid(data)
# Get rate of movement of precip centroid
if days[j]:
dpcentdt = gr.ddt(data.p_cent, secperunit = 86400.) * 86400.
else:
dpcentdt = gr.ddt(data.p_cent) * 86400.
dpcentdt_ppos = dpcentdt.where(data.p_cent>=0.) # Find precip centroid rate where precip centroid is in the northern hemisphere
dpcentdt_max = dpcentdt_ppos.where(dpcentdt_ppos==dpcentdt_ppos.max(),drop=True) # Find the maximum of the above
if len(dpcentdt_max) > 1:
dpcentdt_max = dpcentdt_max[0]
pcent_dtmax = data.p_cent.sel(xofyear=dpcentdt_max.xofyear) # Find the location of the preciptiation when the rate is maximum
print(dpcentdt_max.values, pcent_dtmax.values) # Print both
max_rate.append(dpcentdt_max)
max_rate_lat.append(pcent_dtmax)
j=j+1
max_rate = np.asarray(max_rate)
max_rate_lat = np.asarray(max_rate_lat)
return max_rate, max_rate_lat
def get_pcent_ellipse(data, check=True):
# Input: climatology of Isca run precipitation
# Output: parameters for an ellipse describing the relation between the precipitation centroid and its rate of movement.
precip_centroid(data)
dpcentdt = gr.ddt(data.p_cent) * 86400.
lsqe = el.LSqEllipse()
lsqe.fit([data.p_cent.values, dpcentdt.values])
center, width, height, phi = lsqe.parameters()
if check:
plt.close('all')
fig = plt.figure(figsize=(6,6))
ax = fig.add_subplot(111)
ax.plot(data.p_cent, dpcentdt, 'ro', label='test data', zorder=1)
ellipse = Ellipse(xy=center, width=2*width, height=2*height, angle=np.rad2deg(phi),
edgecolor='b', fc='None', lw=2, label='Fit', zorder = 2)
ax.add_patch(ellipse)
plt.legend()
plt.show()
return center, width, height, phi
def fig_9(run, ax, pentad=40, lev=850.):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
heating = (data.dt_tg_condensation + data.dt_tg_convection +
data.dt_tg_diffusion + data.tdt_rad).mean('lon') * 86400.
rho = data.pfull * 100. / mc.rdgas / data.temp
dtdy = gr.ddy(data.temp.mean('lon'), vector=False)
dtdp = gr.ddp(data.temp.mean('lon'))
dtdt = gr.ddt(data.temp.mean('lon')) * 86400.
vt_eddy = data.vcomp_temp.mean(
'lon') - data.vcomp.mean('lon') * data.temp.mean('lon')
wt_eddy = data.omega_temp.mean(
'lon') - data.omega.mean('lon') * data.temp.mean('lon')
vdtdy_mean = -1. * data.vcomp.mean('lon') * dtdy * 86400.
wdtdp_mean = -1. * data.omega.mean('lon') * dtdp * 86400.
expansion_term = (data.omega / rho / mc.cp_air).mean('lon') * 86400.
div_vt_eddy = -1. * gr.ddy(vt_eddy, vector=True) * 86400.
div_wt_eddy = -1. * gr.ddp(wt_eddy) * 86400.
lats = [
data.lat[i] for i in range(len(data.lat))
if data.lat[i] >= -40. and data.lat[i] <= 40.
]
heating.sel(pfull=lev, xofyear=pentad, lat=lats).plot(ax=ax, color='k')
dtdt.sel(pfull=lev, xofyear=pentad, lat=lats).plot(ax=ax,
color='k',
linestyle=':')
#vdtdy_mean.sel(pfull=lev, xofyear=pentad, lat=lats).plot(ax=ax, color='C1')
#(wdtdp_mean + heating).sel(pfull=lev, xofyear=pentad, lat=lats).plot(ax=ax, color='C2')
expansion_term.sel(pfull=lev, xofyear=pentad, lat=lats).plot(ax=ax,
color='C1')
(vdtdy_mean + wdtdp_mean + expansion_term).sel(pfull=lev,
xofyear=pentad,
lat=lats).plot(
ax=ax,
color='k',
linestyle='--')
#vdtdy_mean.sel(pfull=lev, xofyear=pentad, lat=lats).plot(ax=ax, color='C2', linestyle='--')
#wdtdp_mean.sel(pfull=lev, xofyear=pentad, lat=lats).plot(ax=ax, color='C3', linestyle='--')
#(vdtdy_mean + wdtdp_mean + expansion_term).sel(pfull=lev, xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle='--')
#(-dthetadt + vdthetady_mean + wdthetadp_mean + heating_theta + div_vt_eddy + div_wt_eddy).sel(pfull=lev, xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle=':')
(div_vt_eddy + div_wt_eddy).sel(pfull=lev, xofyear=pentad,
lat=lats).plot(ax=ax,
color='k',
linestyle='-.')
ax.set_title('')
ax.set_xlabel('')
#ax.set_ylim(-0.25,0.25)
ax.set_ylim(-5., 5.)
ax.set_xlim(-30., 30.)
ax.grid(True, linestyle=':')
ax.set_ylabel('Heating rate, K/day')
def bootstrap_method(data, days=False):
max_rate = []
max_rate_lat = []
max_lat = []
eq_rate = []
for boot in range(1000):
print(boot)
sample = bootstrap_sample(data.year_no.size)
precip_sample = data.sel(year_no=sample).mean('year_no')
precip_centroid(precip_sample)
# Next step is to unpick pcent_rate_max function so that you can just hand it the data for the sample mean
# Get the numbers for 1000 or so samples, find 5 and 95 percentile
if days:
dpcentdt = gr.ddt(precip_sample.p_cent, secperunit=86400.) * 86400.
else:
dpcentdt = gr.ddt(precip_sample.p_cent) * 86400.
dpcentdt_ppos = dpcentdt.where(
precip_sample.p_cent >= 0.
) # Find precip centroid rate where precip centroid is in the northern hemisphere
dpcentdt_max = dpcentdt_ppos.where(
dpcentdt_ppos == dpcentdt_ppos.max('xofyear'), drop=True)
dpcentdt_max = dpcentdt_max[0]
pcent_max = precip_sample.p_cent.max('xofyear')
pcent_dtmax = precip_sample.p_cent.sel(
xofyear=dpcentdt_max.xofyear
) # Find the location of the preciptiation when the rate is maximum
p_cent_abs = np.abs(precip_sample.p_cent.where(dpcentdt >= 0.))
dpdt_eq = (dpcentdt.where(p_cent_abs == p_cent_abs.min('xofyear'),
drop=True)).values[0]
#dpdt_eq = dpcentdt.where(precip_sample.p_cent == precip_sample.p_cent.min('xofyear'), drop=True).max()
max_rate.append(float(dpcentdt_max.values))
max_rate_lat.append(float(pcent_dtmax.values))
max_lat.append(float(pcent_max.values))
eq_rate.append(float(dpdt_eq))
return max_rate, max_rate_lat, max_lat, eq_rate
def get_budget_terms(energy, uenergy, venergy, wenergy, diab):
u_e_eddy = uenergy - energy * data.ucomp
v_e_eddy = venergy - energy * data.vcomp
w_e_eddy = wenergy - energy * data.omega
eddy_conv = -1.*(gr.ddx(u_e_eddy) + gr.ddy(v_e_eddy) + gr.ddp(w_e_eddy))
horiz_adv = -1.*(data.ucomp * gr.ddx(energy) + data.vcomp * gr.ddy(energy, vector=False))
denergydt = gr.ddt(energy)*20.
total = (diab + horiz_adv + vert_adv + eddy_conv)*10.
return diab, horiz_adv, vert_adv, eddy_conv, denergydt, total
def fig_8(run, ax, pentad=45, rotfac=1., dayfac=1.):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' + run + '.nc')
heating = data.dt_tg_condensation + data.dt_tg_convection + data.dt_tg_diffusion + data.tdt_rad
convTtotheta=(1000./data.pfull)**mc.kappa
theta = data.temp*convTtotheta
heating_theta = heating*convTtotheta
dthetady = gr.ddy(theta.mean('lon'), vector=False)
dthetadp = gr.ddp(theta.mean('lon'))
dthetadt = gr.ddt(theta.mean('lon'))
vdthetady_mean = data.vcomp.mean('lon') * dthetady
wdthetadp_mean = data.omega.mean('lon') * dthetadp
adv_heating = -1. *(vdthetady_mean + wdthetadp_mean)
sinphi = np.sin(data.lat * np.pi/180.)
cosphi = np.cos(data.lat * np.pi/180.)
w_850 = data.omega.sel(pfull=850.).mean('lon')
w_max, w_max_lat = get_latmax(-1.*w_850)
theta_850 = theta.sel(pfull=850.).mean('lon')
theta_max, theta_max_lat = get_latmax(theta_850)
sinphimax = np.sin(theta_max_lat * np.pi/180.)
theta_m = theta_max - 300.*(mc.omega * rotfac)**2.*mc.a**2./(2.*mc.grav*14000.) * (sinphi**2.-sinphimax**2.)**2./cosphi**2.
def adj_theta(theta_in, adj_by, dayfac=dayfac):
if 'lon' in adj_by.coords:
return theta_in + adj_by.sel(pfull=850.).mean('lon') * 86400. * dayfac
else:
return theta_in + adj_by.sel(pfull=850.) * 86400. * dayfac
theta_rc = adj_theta(theta_850, heating_theta)
theta_adv = adj_theta(theta_850, adv_heating)
theta_net = adj_theta(theta_850, dthetadt, dayfac=20.)
lats = [data.lat[i] for i in range(len(data.lat)) if data.lat[i] >= -60. and data.lat[i] <= 60.]
theta_m.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='0.7')
theta_rc.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle='--')
theta_adv.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle='-.')
theta_850.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='C0')
theta_net.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='C2')
ax.plot([w_max_lat.sel(xofyear=pentad),w_max_lat.sel(xofyear=pentad)], [295,315], color='0.7', linestyle=':')
ax.set_title('')
ax.set_xlabel('')
ax.set_ylim(295.,315.)
ax.set_xlim(-35.,35.)
ax.grid(True,linestyle=':')
ax.set_ylabel('$\Theta$, K')
def fig_9(run, ax, pentad=40):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' + run + '.nc')
convTtotheta=(1000./data.pfull)**mc.kappa
heating = data.dt_tg_condensation + data.dt_tg_convection + data.dt_tg_diffusion + data.tdt_rad
rho = data.pfull*100./mc.rdgas/data.temp
heating_theta = (heating*convTtotheta).mean('lon')
theta = data.temp*convTtotheta
dthetady = gr.ddy(theta.mean('lon'), vector=False)
dthetadp = gr.ddp(theta.mean('lon'))
dthetadt = gr.ddt(theta.mean('lon'))
vcomp_theta = data.vcomp_temp*convTtotheta
vcomp_theta_eddy = vcomp_theta.mean('lon') - data.vcomp.mean('lon')*theta.mean('lon')
vdthetady_mean = data.vcomp.mean('lon') * dthetady
wdthetadp_mean = data.omega.mean('lon') * dthetadp
def column_int(var_in):
var_int = mc.cp_air * var_in.sum('pfull')*5000./mc.grav
return var_int
vdtdy_mean_int = -1. * column_int(vdthetady_mean)
wdtdp_mean_int = -1. * column_int(wdthetadp_mean)
vt_eddy_int = -1. * column_int(vcomp_theta_eddy)
div_vt_eddy_int = gr.ddy(vt_eddy_int, vector=True)
w_850 = data.omega.sel(pfull=850.).mean('lon')
w_max, w_max_lat = get_latmax(-1.*w_850)
heating_theta_int = column_int(heating_theta)
dthetadt_int = column_int(dthetadt)
lats = [data.lat[i] for i in range(len(data.lat)) if data.lat[i] >= -40. and data.lat[i] <= 40.]
dthetadt_int.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='C2')
heating_theta_int.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle='--')
#wdtdp_mean_int.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='C1')
#vdtdy_mean_int.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle='--')
(vdtdy_mean_int + wdtdp_mean_int).sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle='-.')
#(-dthetadt_int + vdtdy_mean_int + wdtdp_mean_int + heating_theta_int + div_vt_eddy_int).sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle=':')
#(vdtdy_mean_int + wdtdp_mean_int + heating_theta_int + div_vt_eddy_int).sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle=':')
div_vt_eddy_int.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle=':')
ax.plot([w_max_lat.sel(xofyear=pentad),w_max_lat.sel(xofyear=pentad)], [-500.,500.], color='0.7', linestyle=':')
ax.set_title('')
ax.set_xlabel('')
ax.set_ylim(-500.,500.)
ax.set_xlim(-35.,35.)
ax.grid(True,linestyle=':')
ax.set_ylabel('Heating rate, W/m$^2$')
def p_cent_rate(data, days=False):
"""
Inputs:
data - xarray dataset climatology including precipitation as either precipitation or as convection_rain and condensation_rain
days - instructs if data is in daily or pentad means
Returns:
dpcentdt - rate of movement of the precipitation centroid
dpcentdt2 - rate of acceleration of the precipitation centroid
"""
# Get total precip
try:
data['precipitation'] = data.condensation_rain + data.convection_rain
except:
data['precipitation'] = data.precipitation
precip_temp = np.zeros(data.precipitation.values.shape)
n = len(data.xofyear.values) // 2
for i in range(0, n):
precip_temp[i, :, :] = (data.precipitation[i, :, :].values +
data.precipitation[i + n, ::-1, :].values) / 2.
precip_temp[i + n, :, :] = precip_temp[i, ::-1, :]
precip_temp = xr.DataArray(
precip_temp,
coords=[data.xofyear.values, data.lat, data.lon],
dims=['xofyear', 'lat', 'lon'])
data['precipitation'] = precip_temp
# Locate precipitation centroid
precip_centroid(data)
# If units in are days rather than pentads (e.g. for shorter years) convert to pentads
if days:
dpcentdt = gr.ddt(data.p_cent, secperunit=86400.) * 86400.
else:
dpcentdt = gr.ddt(data.p_cent) * 86400.
dpcentdt2 = gr.ddt(dpcentdt, secperunit=86400.) * 86400.
return dpcentdt, dpcentdt2
def abs_vort_dt_at_pcent(run, rot_fac=1., lev=150., lat_bound=45.):
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run +
'.nc')
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data, lat_bound=lat_bound)
v_dx = gr.ddx(data.vcomp) # dvdx
u_dy = gr.ddy(data.ucomp) # dudy
omega = 7.2921150e-5 * rot_fac
f = 2 * omega * np.sin(data.lat * np.pi / 180)
vor = (v_dx - u_dy + f).sel(pfull=lev) * 86400.
vor = make_sym(vor, asym=True)
# Take time derivative of absolute vorticity
dvordt = gr.ddt(vor.mean('lon')) * 86400.
# Select latitudes over which to evaluate precip centroid
lats = [
data.lat[i] for i in range(len(data.lat))
if data.lat[i] >= -lat_bound and data.lat[i] <= lat_bound
]
dvordt_lats = dvordt.sel(lat=lats).values
#f_lats = f.sel(lat=lats).values
# Interpolate vorticity tendency and f in latitude
lats_new = np.arange(-lat_bound, lat_bound + 0.1, 0.1)
fun = spint.interp1d(lats,
dvordt_lats,
axis=-1,
fill_value='extrapolate',
kind='quadratic')
dvordt_new = fun(lats_new)
dvordt = xr.DataArray(dvordt_new,
coords=[data.xofyear.values, lats_new],
dims=['xofyear', 'lat'])
#fun = spint.interp1d(lats, f_lats, axis=-1, fill_value='extrapolate', kind='quadratic')
#f_new = fun(lats_new)
#f = xr.DataArray(f_new, coords=[lats_new], dims=['lat'])
dvordt_pcent = [
float(dvordt[i, :].sel(lat=data.p_cent.values[i]).values)
for i in range(len(data.xofyear))
]
#f_pcent = [float(f.sel(lat=data.p_cent.values[i]).values*86400.) for i in range(len(data.xofyear))]
dvordt_pcent = xr.DataArray(np.asarray(dvordt_pcent),
coords=[data.xofyear.values],
dims=['xofyear'])
return dvordt_pcent
def plot_bs_fig_9_moist(run, pentads=[35, 40, 45, 50], rotfac=1.):
plot_dir = '/scratch/rg419/plots/paper_2_figs/revisions/heatbudg_moist/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
rcParams['figure.figsize'] = 5.5, 7.
rcParams['font.size'] = 14
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data) # Locate precipitation centroid
dpcentdt = gr.ddt(data.p_cent) * 86400.
p_cent_pos = np.abs(data.p_cent.where(dpcentdt >= 0.))
eq_time = p_cent_pos.xofyear[p_cent_pos.argmin('xofyear').values]
peak_rate_time = dpcentdt.xofyear[dpcentdt.argmax('xofyear').values]
max_lat_time = data.p_cent.xofyear[data.p_cent.argmax('xofyear').values]
edge_loc, psi_max, psi_max_loc = get_edge_psi(data,
lev=850.,
thresh=0.,
intdown=True)
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, sharex=True)
axes = [ax1, ax2, ax3, ax4]
pentads = [
eq_time.values, peak_rate_time.values,
np.floor((peak_rate_time.values + max_lat_time.values) / 2.),
max_lat_time.values
]
for i in range(4):
fig_9_moist(run, ax=axes[i], pentad=pentads[i])
edge = edge_loc.sel(xofyear=pentads[i])
axes[i].plot([edge, edge], [-250., 250.], 'k:')
axes[i].text(20, 240., 'Pentad ' + str(int(pentads[i])))
ax4.set_xlabel('Latitude')
#ax1.text(-55, 315., 'a)')
#ax2.text(-55, 315., 'b)')
#ax3.text(-55, 315., 'c)')
#ax4.text(-55, 315., 'd)')
plt.subplots_adjust(left=0.15, right=0.95, top=0.97, bottom=0.1)
plt.savefig(plot_dir + 'sb08_fig9_moist_' + run + '.pdf', format='pdf')
plt.close()
def abs_vort_dt_plot(run, rot_fac=1., lev=150.):
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run +
'.nc')
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data)
v_dx = gr.ddx(data.vcomp) # dvdx
u_dy = gr.ddy(data.ucomp) # dudy
omega = 7.2921150e-5 * rot_fac
f = 2 * omega * np.sin(data.lat * np.pi / 180)
vor = (v_dx - u_dy + f).sel(pfull=lev) * 86400.
vor = make_sym(vor, asym=True)
psi = make_sym(psi, asym=True)
# Take time derivative of absolute vorticity
dvordt = gr.ddt(vor.mean('lon')) * 86400.
plot_dir = '/scratch/rg419/plots/paper_2_figs/abs_vort_dt/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
rcParams['figure.figsize'] = 5.5, 4.3
rcParams['font.size'] = 16
fig = plt.figure()
ax1 = fig.add_subplot(111)
f1 = dvordt.plot.contourf(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(-0.06, 0.07, 0.01),
add_colorbar=False,
add_labels=False)
psi.sel(pfull=500).plot.contour(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--')
psi.sel(pfull=500).plot.contour(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2)
psi.sel(pfull=500).plot.contour(ax=ax1,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2)
data.p_cent.plot.line(color='k', linewidth=2)
ax1.set_ylim([-60, 60])
ax1.set_ylabel('Latitude')
ax1.set_xticks([12, 24, 36, 48, 60, 72])
ax1.set_yticks([-60, -30, 0, 30, 60])
ax1.set_xlabel('Pentad')
ax1.grid(True, linestyle=':')
plt.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.05)
cb1 = fig.colorbar(f1,
ax=ax1,
use_gridspec=True,
orientation='horizontal',
fraction=0.15,
pad=0.2,
aspect=40)
cb1.set_label('Absolute vorticity tendency, day$^{-2}$')
plt.savefig(plot_dir + 'abs_vort_dt_' + run + '.pdf', format='pdf')
plt.close()
plot_dir = '/scratch/rg419/plots/paper_2_figs/divergence/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
# Set figure parameters
rcParams['figure.figsize'] = 5.5, 7.
rcParams['font.size'] = 14
# Start figure with 4 subplots
fig, (ax1, ax2) = plt.subplots(2, 1)
div_plot(run, ax=ax1)
psi_mean = psi_mean_clim(run)
div_pcent, p_cent = div_at_pcent(run, interp=False, rot_fac=rots[i])
dpcentdt = gr.ddt(p_cent) * 86400.
#dpcentdt.plot(ax=ax2, color='k', linewidth=2)
p_cent.plot(ax=ax2, color='k', linewidth=2)
print(run, div_pcent.max('xofyear').values)
#a = div_pcent.where(p_cent==np.min(np.abs(p_cent)), drop=True).values
#print(run, a)
max_div.append(div_pcent.max('xofyear').values)
#max_div.append(a)
#dpsi_meandt = gr.ddt(psi_mean)*86400./5.
#psi_mean.plot(ax=ax2, color='g', linewidth=2)
ax2_twin = ax2.twinx()
#st_pcent.plot(ax=ax2_twin, color='b', linewidth=2)
div_pcent.plot(ax=ax2_twin, color='b', linewidth=2)
box = ax2.get_position()
ax2.set_position([box.x0, box.y0, box.width * 0.85, box.height])
def abs_vort_dt_plot(run, ax, rot_fac=1., lev=150., dvordt_flag=False):
'''Plot dvordt or 1/vor * dvordt'''
#Load data
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' + run + '.nc')
#Calculate psi to overplot
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
psi = make_sym(psi, asym=True)
# Make precip symmetric and find the precip centroid
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data)
# Calculate vorticity
v_dx = gr.ddx(data.vcomp) # dvdx
u_dy = gr.ddy(data.ucomp) # dudy
omega = 7.2921150e-5 * rot_fac
f = 2 * omega * np.sin(data.lat *np.pi/180)
vor = (v_dx - u_dy + f).sel(pfull=lev)*86400.
vor = make_sym(vor, asym=True)
# Take time derivative of absolute vorticity
dvordt = gr.ddt(vor.mean('lon'))*86400.
# Also normalise this by the value of vorticity
dvordtvor = dvordt/vor.mean('lon')
# Plot!
plot_dir = '/scratch/rg419/plots/paper_2_figs/abs_vort_dt/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
rcParams['figure.figsize'] = 5.5, 4.3
rcParams['font.size'] = 14
#fig = plt.figure()
#ax1 = fig.add_subplot(111)
if dvordt_flag:
f1 = dvordt.plot.contourf(ax=ax, x='xofyear', y='lat', levels=np.arange(-0.06,0.07,0.01), add_colorbar=False, add_labels=False)
else:
f1 = dvordtvor.plot.contourf(ax=ax, x='xofyear', y='lat', levels=np.arange(-0.05,0.055,0.005), add_colorbar=False, add_labels=False)
#psi.sel(pfull=500).plot.contour(ax=ax, x='xofyear', y='lat', levels=np.arange(-500.,0.,100.), add_labels=False, colors='0.7', linewidths=2, linestyles='--')
#psi.sel(pfull=500).plot.contour(ax=ax, x='xofyear', y='lat', levels=np.arange(0.,510.,100.), add_labels=False, colors='0.7', linewidths=2)
#psi.sel(pfull=500).plot.contour(ax=ax, x='xofyear', y='lat', levels=np.arange(-1000.,1010.,1000.), add_labels=False, colors='0.5', linewidths=2)
data.p_cent.plot.line(ax=ax, color='k', linewidth=2)
ax.set_ylim([-60,60])
ax.set_ylabel('Latitude')
ax.set_xticks([12,24,36,48,60,72])
ax.set_yticks([-60,-30,0,30,60])
ax.set_xlabel('Pentad')
ax.grid(True,linestyle=':')
#originalSize = get(gca, 'Position')
#set(f1, 'Position', originalSize)
#plt.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.05)
box = ax.get_position()
#ax.set_position([box.x0*1.05, box.y0, box.width, box.height])
axColor = plt.axes([box.x0 + box.width*0.92, box.y0+box.y0*0.12, 0.015, box.height])
#cb1=fig.colorbar(f1, ax=ax, use_gridspec=True, orientation = 'horizontal',fraction=0.15, pad=0.2, aspect=40)
#cb1=plt.colorbar(f1, ax=axColor, use_gridspec=True, orientation = 'vertical',fraction=0.15, pad=0.05, aspect=30)
cb1=fig.colorbar(f1, cax=axColor, orientation = 'vertical')
def surface_plot(run,
lonin=[-1., 361.],
diff_run=None,
scale_fac=1.,
do_make_sym=True):
rcParams['figure.figsize'] = 12, 10
rcParams['font.size'] = 16
plot_dir = '/scratch/rg419/plots/surface_fluxes/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
data = xr.open_dataset('/scratch/rg419/Data_moist/climatologies/' + run +
'.nc')
data['flux_lw_up'] = data.t_surf**4. * mc.stefan
data['dTdt'] = gr.ddt(data.t_surf) * 86400. * scale_fac
if do_make_sym:
for field in [
't_surf', 'dTdt', 'flux_sw', 'flux_lw', 'flux_lw_up', 'flux_t',
'flux_lhe'
]:
data[field] = make_sym(data[field])
lons = pick_lons(data, lonin)
if not diff_run == None:
diff_data = xr.open_dataset(
'/scratch/rg419/Data_moist/climatologies/' + diff_run + '.nc')
data = (data - diff_data).sel(lon=lons).mean('lon')
levels_t = np.arange(-10., 11., 1.)
levels_dt = np.arange(-0.5, 0.51, 0.05)
levels_flux = np.arange(-80., 81., 5.)
else:
data = data.sel(lon=lons).mean('lon')
levels_t = np.arange(250., 311., 1.)
levels_dt = np.arange(-0.5, 0.51, 0.01)
levels_flux = np.arange(-300., 305., 10.)
f, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3,
2,
sharex='col',
sharey='row')
left_column = [ax1, ax3, ax5]
bottom_row = [ax5, ax6]
all_plots = [ax1, ax2, ax3, ax4, ax5, ax6]
#check = data.flux_sw + (data.flux_lw - flux_lw_up) - data.flux_t - data.flux_lhe
#check = data.flux_sw - data.flux_lhe
data.t_surf.plot.contourf(x='xofyear',
y='lat',
levels=levels_t,
ax=ax1,
extend='both',
add_labels=False)
ax1.set_title('SST')
data.dTdt.plot.contourf(x='xofyear',
y='lat',
ax=ax2,
levels=levels_dt,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax2.set_title('d(SST)/dt')
data.flux_sw.plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax3,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax3.set_title('Net downward SW flux')
(data.flux_lw - data.flux_lw_up).plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax4,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax4.set_title('Net downward LW flux')
(-1. * data.flux_t).plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax5,
extend='both',
add_labels=False,
cmap='RdBu_r')
#check.plot.contourf(x='xofyear', y='lat', levels=np.arange(-300.,305.,20.), ax=ax5, extend = 'both', add_labels=False, cmap='RdBu_r')
ax5.set_title('Downward sensible heat flux')
(-1. * data.flux_lhe).plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax6,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax6.set_title('Downward latent heat flux')
for ax in left_column:
ax.set_ylabel('Latitude')
for ax in bottom_row:
ax.set_xlabel('Pentad')
for ax in all_plots:
ax.grid(True, linestyle=':')
ax.set_xticks([12, 24, 36, 48, 60, 72])
ax.set_ylim([-60, 60])
if not diff_run == None:
plt.savefig(plot_dir + 'surface_fluxes_' + run + '_' + diff_run +
'.pdf',
format='pdf')
else:
plt.savefig(plot_dir + 'surface_fluxes_' + run + '.pdf', format='pdf')
plt.close()
def fig_8(run, ax, pentad=45, lev=200., dayfac=3., rotfac=1.):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' + run + '.nc')
sinphi = np.sin(data.lat * np.pi/180.)
cosphi = np.cos(data.lat * np.pi/180.)
zeta = 2.* rotfac * mc.omega*sinphi -1.* gr.ddy(data.ucomp.mean('lon')) #* 86400.
nu = gr.ddp(data.ucomp.mean('lon')) #* 86400.
dzetady = gr.ddy(zeta, vector=False)
dzetadp = gr.ddp(zeta)
dwdy = gr.ddy(data.omega, vector=False)
dvdy = gr.ddy(data.vcomp)
dwdp = gr.ddp(data.omega)
dzetadt = gr.ddt(zeta)
vdzetady_mean = data.vcomp.mean('lon') * dzetady
wdzetadp_mean = data.omega.mean('lon') * dzetadp
adv_tend = -1. *(vdzetady_mean + wdzetadp_mean)
vert_adv = -1.*wdzetadp_mean
horiz_adv = -1.*vdzetady_mean
stretching = -1.* zeta * dvdy
tilting = nu * dwdy
eddy_tend = dzetadt - adv_tend - stretching - tilting
w_850 = data.omega.sel(pfull=850.).mean('lon')
w_max = w_850.min('lat')
w_max_lat = data.lat.values[w_850.argmin('lat').values]
w_max_lat = xr.DataArray(w_max_lat, coords=[data.xofyear], dims=['xofyear'])
cosphimax = np.cos(w_max_lat * np.pi/180.)
u_m = rotfac * mc.omega * mc.a * (cosphimax**2. - cosphi**2.)/cosphi
zeta_m = 2.*mc.omega*sinphi -1.* gr.ddy(u_m)
def adj_zeta(zeta_in, adj_by, dayfac=dayfac):
if 'lon' in adj_by.coords:
return zeta_in + adj_by.sel(pfull=lev).mean('lon') * 86400.**2. * dayfac
else:
return zeta_in + adj_by.sel(pfull=lev) * 86400.**2. * dayfac
zeta_150 = zeta.sel(pfull=150.)*86400.
zeta_adv = adj_zeta(zeta_150, adv_tend); zeta_vadv = adj_zeta(zeta_150, horiz_adv); zeta_hadv = adj_zeta(zeta_150, vert_adv)
zeta_stretching = adj_zeta(zeta_150, stretching)
zeta_tilting = adj_zeta(zeta_150, tilting)
zeta_eddy = adj_zeta(zeta_150, eddy_tend);
zeta_net = adj_zeta(zeta_150, dzetadt, dayfac=10.*dayfac)
lats = [data.lat[i] for i in range(len(data.lat)) if data.lat[i] >= -60. and data.lat[i] <= 60.]
zeta_m.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='0.7', linestyle='-')
zeta_stretching.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle='--')
zeta_tilting.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='0.7', linestyle='--')
zeta_adv.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle='-.')
#u_hadv.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='C1', linestyle='-.')
#u_vadv.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='C2', linestyle='-.')
zeta_eddy.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle=':')
zeta_150.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='C0')
zeta_net.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='C2')
ax.plot([w_max_lat.sel(xofyear=pentad),w_max_lat.sel(xofyear=pentad)], [-7.5,7.5], color='0.7', linestyle=':')
ax.set_title('')
ax.set_xlabel('')
ax.set_xlim(-35.,35.)
ax.set_ylim(-7.5,7.5)
ax.grid(True,linestyle=':')
ax.set_ylabel('zeta, /s')
def bs_mombudg(run, ax, pentad=45, lev=150., dayfac=3., rotfac=1.):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
damping = data.dt_ug_diffusion # + data.tdt_diss_rdamp
dudy = gr.ddy(data.ucomp.mean('lon'))
duvdy = gr.ddy(data.ucomp_vcomp.mean('lon'), uv=True)
dudp = gr.ddp(data.ucomp.mean('lon'))
duwdp = gr.ddp(data.ucomp_omega.mean('lon'))
dudt = gr.ddt(data.ucomp.mean('lon'))
vdudy_mean = data.vcomp.mean('lon') * dudy
wdudp_mean = data.omega.mean('lon') * dudp
adv_tend = -1. * (vdudy_mean + wdudp_mean)
vert_adv = -1. * wdudp_mean
horiz_adv = -1. * vdudy_mean
eddy_tend = -1. * duvdy - 1. * duwdp - adv_tend
vert_eddy = -1. * duwdp - vert_adv
horiz_eddy = -1. * duvdy - horiz_adv
sinphi = np.sin(data.lat * np.pi / 180.)
cosphi = np.cos(data.lat * np.pi / 180.)
coriolis = 2. * rotfac * mc.omega * sinphi * data.vcomp
def get_latmax(data_in):
data_max = data_in.max('lat')
data_max_lat = data_in.lat.values[data_in.argmax('lat').values]
data_max_lat = xr.DataArray(data_max_lat,
coords=[data_in.xofyear],
dims=['xofyear'])
return data_max, data_max_lat
w_850 = data.omega.sel(pfull=850.).mean('lon')
w_max, w_max_lat = get_latmax(-1. * w_850)
convTtotheta = (1000. / data.pfull)**mc.kappa
theta_equiv_850 = ((data.temp + mc.L / mc.cp_air * data.sphum /
(1 - data.sphum)) *
convTtotheta).sel(pfull=850.).mean('lon')
theta_max, theta_max_lat = get_latmax(theta_equiv_850)
cosphimax = np.cos(theta_max_lat * np.pi / 180.)
u_150 = data.ucomp.sel(pfull=lev).mean('lon')
u_m = rotfac * mc.omega * mc.a * (cosphimax**2. - cosphi**2.) / cosphi
def adj_u(u_in, adj_by, dayfac=dayfac):
if 'lon' in adj_by.coords:
return u_in + adj_by.sel(pfull=lev).mean('lon') * 86400. * dayfac
else:
return u_in + adj_by.sel(pfull=lev) * 86400. * dayfac
u_damp = adj_u(u_150, damping)
u_cor = adj_u(u_150, coriolis)
u_adv = adj_u(u_150, adv_tend)
u_vadv = adj_u(u_150, horiz_adv)
u_hadv = adj_u(u_150, vert_adv)
u_eddy = adj_u(u_150, eddy_tend)
u_veddy = adj_u(u_150, horiz_eddy)
u_heddy = adj_u(u_150, vert_eddy)
resid = coriolis + damping + adv_tend + eddy_tend
u_resid = adj_u(u_150, resid, dayfac=10. * dayfac)
u_net = adj_u(u_150, dudt, dayfac=10. * dayfac)
lats = [
data.lat[i] for i in range(len(data.lat))
if data.lat[i] >= -60. and data.lat[i] <= 60.
]
u_m.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='0.7')
u_cor.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle='--')
u_adv.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle='-.')
u_eddy.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle=':')
u_150.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='C0')
u_net.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='C2')
ax.plot([w_max_lat.sel(xofyear=pentad),
w_max_lat.sel(xofyear=pentad)], [-75., 75.],
color='0.7',
linestyle=':')
ax.set_title('')
ax.set_xlabel('')
ax.set_xlim(-35., 35.)
ax.set_ylim(-75., 75.)
ax.grid(True, linestyle=':')
ax.set_ylabel('u, m/s')
def fixed_sst_imbalance(run, lonin=[-1., 361.], mld=10.):
# Plot the imbalance between actual rate of change of temperature and that the fluxes should cause for fixed SST expts
rcParams['figure.figsize'] = 6, 10
rcParams['font.size'] = 16
plot_dir = '/scratch/rg419/plots/surface_fluxes/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
lons = pick_lons(data, lonin)
data = data.sel(lon=lons).mean('lon')
flux_lw_up = data.t_surf**4. * mc.stefan
dTdt = gr.ddt(data.t_surf) * 86400.
f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
sum_of_all = (data.flux_sw + (data.flux_lw - flux_lw_up) - data.flux_t -
data.flux_lhe) / rho_cp / mld * 86400.
dTdt = gr.ddt(data.t_surf) * 86400.
f1 = sum_of_all.plot.contourf(x='xofyear',
y='lat',
levels=np.arange(-1., 1., 0.1),
ax=ax1,
extend='both',
add_labels=False,
add_colorbar=False)
ax1.set_title('Sum of all fluxes')
dTdt.plot.contourf(x='xofyear',
y='lat',
levels=np.arange(-1., 1., 0.1),
ax=ax2,
extend='both',
add_labels=False,
cmap='RdBu_r',
add_colorbar=False)
ax2.set_title('dTs/dt')
(sum_of_all - dTdt).plot.contourf(x='xofyear',
y='lat',
levels=np.arange(-1., 1., 0.1),
ax=ax3,
extend='both',
add_labels=False,
cmap='RdBu_r',
add_colorbar=False)
ax3.set_title('Fluxes-dTsdt')
ax3.set_xlabel('Pentad')
for ax in [ax1, ax2, ax3]:
ax.set_ylabel('Latitude')
ax.grid(True, linestyle=':')
plt.subplots_adjust(left=0.2, right=0.95, top=0.95, bottom=0., hspace=0.2)
#Colorbar
cb1 = f.colorbar(f1,
ax=(ax1, ax2, ax3),
use_gridspec=True,
orientation='horizontal',
fraction=0.15,
pad=0.07,
aspect=30)
cb1.set_label('dT/dt, K/day')
plt.savefig(plot_dir + 'fixed_sst_imbalance_' + run + '.pdf', format='pdf')
plt.close()
def int_flux_plot(run, lonin=[-1., 361.], mld=10.):
rcParams['figure.figsize'] = 6, 13.3
rcParams['font.size'] = 16
plot_dir = '/scratch/rg419/plots/surface_fluxes/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
lons = pick_lons(data, lonin)
data = data.sel(lon=lons).mean('lon')
flux_lw_up = data.t_surf**4. * mc.stefan
dTdt = gr.ddt(data.t_surf) * 86400.
def int_flux_fwd(flux):
#cp dTdt = SW + LW + LH + SH
n = data.t_surf.shape[0]
t_out = np.zeros([n, 64])
t_out[0, :] = data.t_surf[0, :]
for i in range(n - 1):
t_out[i +
1, :] = t_out[i, :] + flux[i, :] / rho_cp / mld * 86400. * 5.
t_out = xr.DataArray(t_out,
coords=[data.xofyear.values, data.lat.values],
dims=['xofyear', 'lat'])
return t_out
t_all = int_flux_fwd(data.flux_sw - data.flux_lhe + data.flux_lw -
flux_lw_up - data.flux_t)
t_sw = int_flux_fwd(data.flux_sw +
(-data.flux_lhe + data.flux_lw - flux_lw_up -
data.flux_t).mean('xofyear'))
t_lw = int_flux_fwd(data.flux_sw + data.flux_lw - flux_lw_up -
data.flux_lhe.mean('xofyear'))
t_lh = int_flux_fwd(data.flux_sw - data.flux_lhe +
(data.flux_lw - flux_lw_up).mean('xofyear'))
f, (ax1, ax2, ax3, ax4) = plt.subplots(4, sharex=True)
f1 = t_all.plot.contourf(x='xofyear',
y='lat',
levels=np.arange(250., 311., 5.),
ax=ax1,
extend='both',
add_labels=False,
add_colorbar=False)
ax1.set_title('Sum of all fluxes')
f1 = t_sw.plot.contourf(x='xofyear',
y='lat',
levels=np.arange(250., 311., 5.),
ax=ax2,
extend='both',
add_labels=False,
add_colorbar=False)
ax2.set_title('SW')
t_lw.plot.contourf(x='xofyear',
y='lat',
levels=np.arange(250., 311., 5.),
ax=ax3,
extend='both',
add_labels=False,
add_colorbar=False)
ax3.set_title('SW + LHE')
t_lh.plot.contourf(x='xofyear',
y='lat',
levels=np.arange(250., 311., 5.),
ax=ax4,
extend='both',
add_labels=False,
add_colorbar=False)
ax4.set_title('SW + LW')
ax4.set_xlabel('Pentad')
for ax in [ax1, ax2, ax3, ax4]:
ax.set_ylabel('Latitude')
ax.grid(True, linestyle=':')
plt.subplots_adjust(left=0.2, right=0.95, top=0.95, bottom=0., hspace=0.2)
#Colorbar
cb1 = f.colorbar(f1,
ax=(ax1, ax2, ax3, ax4),
use_gridspec=True,
orientation='horizontal',
fraction=0.15,
pad=0.07,
aspect=30)
cb1.set_label('Temperature, K')
plt.savefig(plot_dir + 'int_flux_plot_' + run + '.pdf', format='pdf')
plt.close()
def surf_cooling_plot(run, lonin=[-1., 361.], mld=10.):
rcParams['figure.figsize'] = 6, 10
rcParams['font.size'] = 16
plot_dir = '/scratch/rg419/plots/surface_fluxes/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
lons = pick_lons(data, lonin)
data = data.sel(lon=lons).mean('lon')
flux_lw_up = data.t_surf**4. * mc.stefan
dTdt = gr.ddt(data.t_surf) * 86400.
f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
sum_of_all = (data.flux_sw + (data.flux_lw - flux_lw_up) - data.flux_t -
data.flux_lhe) / rho_cp / mld * 86400.
sw_and_lhe = (data.flux_sw - data.flux_lhe +
(data.flux_lw - flux_lw_up -
data.flux_t).mean('xofyear')) / rho_cp / mld * 86400.
sw_and_lw = (
data.flux_sw + (data.flux_lw - flux_lw_up) -
(data.flux_lhe + data.flux_t).mean('xofyear')) / rho_cp / mld * 86400.
f1 = sum_of_all.plot.contourf(x='xofyear',
y='lat',
levels=np.arange(-1., 1.05, 0.1),
ax=ax1,
extend='both',
add_labels=False,
add_colorbar=False)
ax1.set_title('Sum of all fluxes')
sw_and_lhe.plot.contourf(x='xofyear',
y='lat',
levels=np.arange(-1., 1.05, 0.1),
ax=ax2,
extend='both',
add_labels=False,
cmap='RdBu_r',
add_colorbar=False)
ax2.set_title('SW + LHE')
sw_and_lw.plot.contourf(x='xofyear',
y='lat',
levels=np.arange(-1., 1.05, 0.1),
ax=ax3,
extend='both',
add_labels=False,
cmap='RdBu_r',
add_colorbar=False)
ax3.set_title('SW + LW')
ax3.set_xlabel('Pentad')
for ax in [ax1, ax2, ax3]:
ax.set_ylabel('Latitude')
ax.grid(True, linestyle=':')
plt.subplots_adjust(left=0.2, right=0.95, top=0.95, bottom=0., hspace=0.2)
#Colorbar
cb1 = f.colorbar(f1,
ax=(ax1, ax2, ax3),
use_gridspec=True,
orientation='horizontal',
fraction=0.15,
pad=0.07,
aspect=30)
cb1.set_label('Downward flux, W/m^2')
plt.savefig(plot_dir + 'surf_cooling_plot_' + run + '.pdf', format='pdf')
plt.close()
if __name__ == "__main__":
# Set plotting directory
plot_dir = '/scratch/rg419/plots/paper_2_figs/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
# Set figure parameters
rcParams['figure.figsize'] = 10, 9
rcParams['font.size'] = 12
subsolar_point = -23.439 * np.cos(np.arange(0., 361., 1.) * np.pi / 180.)
subsolar_point = xr.DataArray(subsolar_point,
coords=[np.arange(0., 361., 1.)],
dims=['xofyear'])
subsolar_rate = gr.ddt(subsolar_point, secperunit=86400.) * 86400.
# Start figure with 4 subplots
#fig, ((ax1, ax2, ax3), (ax4, ax5, ax6), (ax7, ax8, ax9)) = plt.subplots(3, 3)
fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2)
# Switch on errorbars
errorbars = True
# Set colors for lines
#colors=['b','g','r','c','m','y']
colors = ['C0', 'C1', 'C2', 'C3', 'C4', 'C5']
'''Seasons'''
runs_sn = [
'sn_0.250', 'sn_0.500', 'sn_1.000', 'sn_2.000', 'sn_3.000', 'sn_4.000'
]
def local_pcent_plots(run,
regions=[[350, 10], [80, 100], [170, 190], [260, 280]],
do_make_sym=True):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
if do_make_sym:
data['precipitation'] = make_sym(data.precipitation)
data = precip_centroid_ll(data, lat_bound=30.)
def get_lons(lonin, data):
if lonin[1] > lonin[0]:
lons = [
data.lon[i] for i in range(len(data.lon))
if data.lon[i] >= lonin[0] and data.lon[i] < lonin[1]
]
else:
lons = [
data.lon[i] for i in range(len(data.lon))
if data.lon[i] >= lonin[0] or data.lon[i] < lonin[1]
]
return lons
lons_1 = get_lons(regions[0], data)
lons_2 = get_lons(regions[1], data)
lons_3 = get_lons(regions[2], data)
lons_4 = get_lons(regions[3], data)
dpcentdt_1 = gr.ddt(data.p_cent.sel(lon=lons_1).mean('lon')) * 86400.
dpcentdt_2 = gr.ddt(data.p_cent.sel(lon=lons_2).mean('lon')) * 86400.
dpcentdt_3 = gr.ddt(data.p_cent.sel(lon=lons_3).mean('lon')) * 86400.
dpcentdt_4 = gr.ddt(data.p_cent.sel(lon=lons_4).mean('lon')) * 86400.
# Set figure parameters
rcParams['figure.figsize'] = 10, 7
rcParams['font.size'] = 14
# Start figure with 4 subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
ax1.plot(data.p_cent.sel(lon=lons_1).mean('lon'),
dpcentdt_1,
'kx',
mew=2,
ms=10,
alpha=0.5)
ax1.plot(data.p_cent.sel(lon=lons_1).mean('lon'),
dpcentdt_1,
alpha=0.3,
color='k',
linewidth=2)
ax1.set_title('West coast')
ax2.plot(data.p_cent.sel(lon=lons_2).mean('lon'),
dpcentdt_2,
'kx',
mew=2,
ms=10,
alpha=0.5)
ax2.plot(data.p_cent.sel(lon=lons_2).mean('lon'),
dpcentdt_2,
alpha=0.3,
color='k',
linewidth=2)
ax2.set_title('Land')
ax3.plot(data.p_cent.sel(lon=lons_3).mean('lon'),
dpcentdt_3,
'kx',
mew=2,
ms=10,
alpha=0.5)
ax3.plot(data.p_cent.sel(lon=lons_3).mean('lon'),
dpcentdt_3,
alpha=0.3,
color='k',
linewidth=2)
ax3.set_title('East coast')
ax4.plot(data.p_cent.sel(lon=lons_4).mean('lon'),
dpcentdt_4,
'kx',
mew=2,
ms=10,
alpha=0.5)
ax4.plot(data.p_cent.sel(lon=lons_4).mean('lon'),
dpcentdt_4,
alpha=0.3,
color='k',
linewidth=2)
ax4.set_title('Ocean')
ax3.set_xlabel('ITCZ latitude')
ax4.set_xlabel('ITCZ latitude')
ax1.set_ylabel('ITCZ migration rate')
ax3.set_ylabel('ITCZ migration rate')
for ax in [ax1, ax2, ax3, ax4]:
ax.grid(True, linestyle=':')
ax.set_xlim(-25, 25)
ax.set_ylim(-1.0, 1.0)
plt.subplots_adjust(left=0.1,
right=0.97,
top=0.95,
bottom=0.1,
hspace=0.3,
wspace=0.3)
# Save as a pdf
plt.savefig(plot_dir + 'bowties_' + run + '.pdf', format='pdf')
plt.close()
# Start figure with 4 subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
precip_mse_plot(data, ax1, lonin=regions[0], lat_bound=30.)
precip_mse_plot(data, ax2, lonin=regions[1], lat_bound=30.)
precip_mse_plot(data, ax3, lonin=regions[2], lat_bound=30.)
precip_mse_plot(data, ax4, lonin=regions[3], lat_bound=30.)
ax1.set_title('West coast')
ax2.set_title('Land')
ax3.set_title('East coast')
ax4.set_title('Ocean')
ax3.set_xlabel('Pentad')
ax4.set_xlabel('Pentad')
ax1.set_ylabel('Latitude')
ax3.set_ylabel('Latitude')
for ax in [ax1, ax2, ax3, ax4]:
ax.grid(True, linestyle=':')
plt.subplots_adjust(left=0.1,
right=0.97,
top=0.95,
bottom=0.1,
hspace=0.3,
wspace=0.3)
# Save as a pdf
plt.savefig(plot_dir + 'precip_' + run + '.pdf', format='pdf')
plt.close()
data.close()
def fig_9_moist(run, ax, pentad=40):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
convTtotheta = (1000. / data.pfull)**mc.kappa
heating = data.dt_tg_condensation + data.dt_tg_convection + data.dt_tg_diffusion + data.tdt_rad
hum_tend = data.dt_qg_condensation + data.dt_qg_convection + data.dt_qg_diffusion
heating_theta_equiv = ((heating + mc.L / mc.cp_air * hum_tend /
(1 - data.sphum)**2.) * convTtotheta).mean('lon')
theta_equiv = (data.temp + mc.L / mc.cp_air * data.sphum /
(1 - data.sphum)) * convTtotheta
dthetady_equiv = gr.ddy(theta_equiv.mean('lon'), vector=False)
dthetadp_equiv = gr.ddp(theta_equiv.mean('lon'))
dthetadt_equiv = gr.ddt(theta_equiv.mean('lon'))
vcomp_theta_equiv = (data.vcomp_temp +
mc.L / mc.cp_air * data.sphum_v) * convTtotheta
vcomp_theta_eddy_equiv = vcomp_theta_equiv.mean(
'lon') - data.vcomp.mean('lon') * theta_equiv.mean('lon')
vdthetady_mean_equiv = data.vcomp.mean('lon') * dthetady_equiv
wdthetadp_mean_equiv = data.omega.mean('lon') * dthetadp_equiv
def column_int(var_in):
var_int = mc.cp_air * var_in.sum('pfull') * 5000. / mc.grav
return var_int
vdtdy_mean_int_equiv = -1. * column_int(vdthetady_mean_equiv)
wdtdp_mean_int_equiv = -1. * column_int(wdthetadp_mean_equiv)
vt_eddy_int_equiv = -1. * column_int(vcomp_theta_eddy_equiv)
div_vt_eddy_int_equiv = gr.ddy(vt_eddy_int_equiv, vector=True)
heating_theta_int_equiv = column_int(heating_theta_equiv)
dthetadt_int_equiv = column_int(dthetadt_equiv)
lats = [
data.lat[i] for i in range(len(data.lat))
if data.lat[i] >= -60. and data.lat[i] <= 60.
]
w_850 = data.omega.sel(pfull=850.).mean('lon')
w_max, w_max_lat = get_latmax(-1. * w_850)
dthetadt_int_equiv.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='C2')
heating_theta_int_equiv.sel(xofyear=pentad, lat=lats).plot(ax=ax,
color='k',
linestyle='--')
(vdtdy_mean_int_equiv + wdtdp_mean_int_equiv).sel(
xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle='-.')
#(vdtdy_mean_int_equiv + wdtdp_mean_int_equiv + heating_theta_int_equiv + div_vt_eddy_int_equiv).sel(xofyear=pentad, lat=lats).plot(ax=ax, color='k', linestyle=':')
div_vt_eddy_int_equiv.sel(xofyear=pentad, lat=lats).plot(ax=ax,
color='k',
linestyle=':')
ax.plot([w_max_lat.sel(xofyear=pentad),
w_max_lat.sel(xofyear=pentad)], [-250., 250.],
color='0.7',
linestyle=':')
ax.set_title('')
ax.set_xlabel('')
ax.set_ylim(-250., 250.)
ax.set_xlim(-35., 35.)
ax.grid(True, linestyle=':')
ax.set_ylabel('Heating rate, W/m$^2$')
def heat_budg_hm_fluxform(run, lev=850, filename='plev_pentad', timeav='pentad', period_fac=1.,lonin=[-1.,361.], plot_precip=True, do_theta=False):
rcParams['figure.figsize'] = 12, 8
rcParams['font.size'] = 18
rcParams['text.usetex'] = True
plot_dir = '/scratch/rg419/plots/heat_budg/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/'+run+'.nc')
uT_dx = -86400. * gr.ddx(data.ucomp_temp.sel(pfull=lev)).mean('lon')
vT_dy = -86400. * gr.ddy(data.vcomp_temp.sel(pfull=lev)).mean('lon')
wT_dp = -86400. * gr.ddp(data.omega_temp).sel(pfull=lev).mean('lon')
uT_eddy = data.ucomp_temp - data.ucomp * data.temp
vT_eddy = data.vcomp_temp - data.vcomp * data.temp
wT_eddy = data.omega_temp - data.omega * data.temp
#uT_dx = -86400. * (data.ucomp * gr.ddx(data.temp)).sel(pfull=lev).mean('lon')
#vT_dy = -86400. * (data.vcomp * gr.ddy(data.temp, vector=False)).sel(pfull=lev).mean('lon')
#wT_dp = -86400. * (data.omega * gr.ddp(data.temp)).sel(pfull=lev).mean('lon')
uT_dx_eddy = -86400. * gr.ddx(uT_eddy.sel(pfull=lev)).mean('lon')
vT_dy_eddy = -86400. * gr.ddy(vT_eddy.sel(pfull=lev)).mean('lon')
wT_dp_eddy = -86400. * gr.ddp(wT_eddy).sel(pfull=lev).mean('lon')
tdt_rad = data.tdt_rad.sel(pfull=lev).mean('lon')*86400.
tdt_conv = data.dt_tg_convection.sel(pfull=lev).mean('lon')*86400.
tdt_cond = data.dt_tg_condensation.sel(pfull=lev).mean('lon')*86400.
tdt_diff = data.dt_tg_diffusion.sel(pfull=lev).mean('lon')*86400.
diabatic = tdt_rad + tdt_conv + tdt_cond + tdt_diff
eddies = uT_dx_eddy + vT_dy_eddy + wT_dp_eddy
rho = data.pfull*100./data.temp/mc.rdgas
asc_cool = (data.omega /(mc.cp_air * rho)).sel(pfull=lev).mean('lon') *86400.
heat_sum = uT_dx + vT_dy + wT_dp + diabatic + asc_cool #+ eddies
Tdt = gr.ddt(data.temp.sel(pfull=lev)).mean('lon') *86400.
horiz_adv = uT_dx + vT_dy
vertical_term = wT_dp + asc_cool
levels = np.arange(-100,101.1,10.)
#levels = np.arange(-20,21.,2.)
#levels = np.arange(-3,3.1,0.2)
#levels = np.arange(-1,1.1,0.1)
# Nine subplots
fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3, sharex='col', sharey='row')
plt.set_cmap('RdBu_r')
plot_vars = [horiz_adv, vertical_term, diabatic, heat_sum, Tdt, eddies]
axes = [ax1,ax2,ax3,ax4,ax5,ax6]
labels = ['a)','b)','c)','d)','e)','f)']
for i in range(6):
f1=plot_vars[i].plot.contourf(ax=axes[i], x='xofyear', y='lat', extend='both', add_labels=False, cmap='RdBu_r', levels = levels, add_colorbar=False)
axes[i].set_ylim(-60,60)
axes[i].grid(True,linestyle=':')
axes[i].set_yticks(np.arange(-60.,61.,30.))
for i in [0,3]:
axes[i].set_ylabel('Latitude')
axes[i].text(-15, 60, labels[i])
for i in [1,2,4,5]:
axes[i].text(-5, 60, labels[i])
#for i in [3,4,5]:
# axes[i].set_xticklabels(ticklabels,rotation=25)
# set titles
ax1.set_title('Horizontal advection', fontsize=17)
ax2.set_title('Vertical advection and expansion', fontsize=17)
ax3.set_title('Diabatic', fontsize=17)
ax4.set_title('Residual', fontsize=17)
ax5.set_title('Temperature tendency', fontsize=17)
ax6.set_title('Eddy convergence', fontsize=17)
plt.subplots_adjust(right=0.97, left=0.1, top=0.95, bottom=0., hspace=0.25, wspace=0.12)
#Colorbar
cb1=fig.colorbar(f1, ax=axes, use_gridspec=True, orientation = 'horizontal',fraction=0.15, pad=0.15, aspect=30, shrink=0.5)
figname = 'heat_budg_fluxform_' +run+ '.pdf'
plt.savefig(plot_dir + figname, format='pdf')
plt.close()
def surface_plot(run,
lonin=[-1., 361.],
diff_run=None,
scale_fac=1.,
do_make_sym=True):
print('what?')
rcParams['figure.figsize'] = 15, 10
rcParams['font.size'] = 14
plot_dir = '/scratch/rg419/plots/surface_fluxes/'
mkdir = sh.mkdir.bake('-p')
mkdir(plot_dir)
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
#Calculate psi to overplot
psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)
psi /= 1.e9
psi = make_sym(psi, asym=True)
# Make precip symmetric and find the precip centroid
data['precipitation'] = make_sym(data.precipitation)
precip_centroid(data)
data['flux_lw_up'] = data.t_surf**4. * mc.stefan
data['dTdt'] = gr.ddt(data.t_surf) * 86400. * scale_fac
if do_make_sym:
for field in [
't_surf', 'dTdt', 'flux_sw', 'flux_lw', 'flux_lw_up', 'flux_t',
'flux_lhe'
]:
data[field] = make_sym(data[field])
lons = pick_lons(data, lonin)
if not diff_run == None:
diff_data = xr.open_dataset(
'/disca/share/rg419/Data_moist/climatologies/' + diff_run + '.nc')
data = (data - diff_data).sel(lon=lons).mean('lon')
levels_t = np.arange(-10., 11., 1.)
levels_dt = np.arange(-0.5, 0.51, 0.05)
levels_flux = np.arange(-80., 81., 5.)
else:
data = data.sel(lon=lons).mean('lon')
levels_t = np.arange(270., 306., 2.5)
levels_dt = np.arange(-0.3, 0.31, 0.05)
levels_flux = np.arange(-300., 305., 20.)
f, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3,
2,
sharex='col',
sharey='row')
left_column = [ax1, ax3, ax5]
bottom_row = [ax5, ax6]
all_plots = [ax1, ax2, ax3, ax4, ax5, ax6]
#check = data.flux_sw + (data.flux_lw - flux_lw_up) - data.flux_t - data.flux_lhe
#check = data.flux_sw - data.flux_lhe
data.t_surf.plot.contourf(x='xofyear',
y='lat',
levels=levels_t,
ax=ax1,
extend='both',
add_labels=False)
ax1.set_title('SST')
data.dTdt.plot.contourf(x='xofyear',
y='lat',
ax=ax2,
levels=levels_dt,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax2.set_title('d(SST)/dt')
data.flux_sw.plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax3,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax3.set_title('Net downward SW flux')
(data.flux_lw - data.flux_lw_up).plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax4,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax4.set_title('Net downward LW flux')
(-1. * data.flux_t).plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax5,
extend='both',
add_labels=False,
cmap='RdBu_r')
#check.plot.contourf(x='xofyear', y='lat', levels=np.arange(-300.,305.,20.), ax=ax5, extend = 'both', add_labels=False, cmap='RdBu_r')
ax5.set_title('Downward sensible heat flux')
(-1. * data.flux_lhe).plot.contourf(x='xofyear',
y='lat',
levels=levels_flux,
ax=ax6,
extend='both',
add_labels=False,
cmap='RdBu_r')
ax6.set_title('Downward latent heat flux')
i = 0
labels = ['a)', 'b)', 'c)', 'd)', 'e)', 'f)']
for ax in all_plots:
ax.grid(True, linestyle=':')
ax.set_xticks([12, 24, 36, 48, 60, 72])
ax.set_ylim([-60, 60])
ax.set_yticks([-60, -30, 0, 30, 60])
ax.text(-8, 60., labels[i])
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-500., 0., 100.),
add_labels=False,
colors='0.7',
linewidths=2,
linestyles='--')
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(0., 510., 100.),
add_labels=False,
colors='0.7',
linewidths=2)
psi.sel(pfull=500).plot.contour(ax=ax,
x='xofyear',
y='lat',
levels=np.arange(-1000., 1010., 1000.),
add_labels=False,
colors='0.5',
linewidths=2)
data.p_cent.plot.line(ax=ax, color='k', linewidth=2)
ax.set_ylabel('')
ax.set_xlabel('')
i = i + 1
for ax in left_column:
ax.set_ylabel('Latitude')
for ax in bottom_row:
ax.set_xlabel('Pentad')
plt.subplots_adjust(left=0.07,
right=0.97,
top=0.97,
bottom=0.05,
hspace=0.2,
wspace=0.1)
if lonin == [-1., 361.]:
if not diff_run == None:
plt.savefig(plot_dir + 'surface_fluxes_' + run + '_' + diff_run +
'.pdf',
format='pdf')
else:
plt.savefig(plot_dir + 'surface_fluxes_' + run + '.pdf',
format='pdf')
else:
figname = plot_dir + 'surface_fluxes_' + run + '_' + str(int(
lonin[0])) + '_' + str(int(lonin[1])) + '.pdf'
plt.savefig(figname, format='pdf')
plt.close()
def fig_8_moist(run, ax, pentad=45, rotfac=1., dayfac=5.):
data = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/' +
run + '.nc')
heating = data.dt_tg_condensation + data.dt_tg_convection + data.dt_tg_diffusion + data.tdt_rad
hum_tend = data.dt_qg_condensation + data.dt_qg_convection + data.dt_qg_diffusion
convTtotheta = (1000. / data.pfull)**mc.kappa
theta = data.temp * convTtotheta
heating_theta = heating * convTtotheta
heating_equiv_theta = heating_theta + mc.L / mc.cp_air * hum_tend / (
1 - data.sphum)**2. * convTtotheta
sinphi = np.sin(data.lat * np.pi / 180.)
cosphi = np.cos(data.lat * np.pi / 180.)
theta_850 = theta.sel(pfull=850.).mean('lon')
theta_equiv = (data.temp + mc.L / mc.cp_air * data.sphum /
(1 - data.sphum)) * convTtotheta
theta_equiv_850 = theta_equiv.sel(pfull=850.).mean('lon')
lats = [
data.lat[i] for i in range(len(data.lat))
if data.lat[i] >= -10. and data.lat[i] <= 10.
]
Tt = (data.temp.sel(pfull=150.).mean(('lon')) *
cosphi).sel(lat=lats).sum('lat') / cosphi.sel(lat=lats).sum('lat')
Ts = (data.t_surf.mean(('lon')) *
cosphi).sel(lat=lats).sum('lat') / cosphi.sel(lat=lats).sum('lat')
dthetady_equiv = gr.ddy(theta_equiv.mean('lon'), vector=False)
dthetadp_equiv = gr.ddp(theta_equiv.mean('lon'))
dthetadt_equiv = gr.ddt(theta_equiv.mean('lon'))
vdthetady_mean = data.vcomp.mean('lon') * dthetady_equiv
wdthetadp_mean = data.omega.mean('lon') * dthetadp_equiv
adv_heating_equiv = -1. * (vdthetady_mean + wdthetadp_mean)
w_850 = data.omega.sel(pfull=850.).mean('lon')
w_max, w_max_lat = get_latmax(-1. * w_850)
theta_max, theta_max_lat = get_latmax(theta_equiv_850)
cosphimax = np.cos(theta_max_lat * np.pi / 180.)
chi = (rotfac * mc.omega)**2 * mc.a**2. / mc.cp_air / (Ts - Tt)
theta_m_equiv = theta_max * np.exp(
-1. * chi * (cosphimax**2. - cosphi**2.)**2. / cosphi**2.)
def adj_theta(theta_in, adj_by, dayfac=dayfac):
if 'lon' in adj_by.coords:
return theta_in + adj_by.sel(
pfull=850.).mean('lon') * 86400. * dayfac
else:
return theta_in + adj_by.sel(pfull=850.) * 86400. * dayfac
theta_equiv_rc = adj_theta(theta_equiv_850,
heating_equiv_theta,
dayfac=dayfac)
theta_equiv_adv = adj_theta(theta_equiv_850,
adv_heating_equiv,
dayfac=dayfac)
theta_equiv_net = adj_theta(theta_equiv_850,
dthetadt_equiv,
dayfac=dayfac * 3.)
lats = [
data.lat[i] for i in range(len(data.lat))
if data.lat[i] >= -60. and data.lat[i] <= 60.
]
theta_m_equiv.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='0.7')
theta_equiv_rc.sel(xofyear=pentad, lat=lats).plot(ax=ax,
color='k',
linestyle='--')
theta_equiv_adv.sel(xofyear=pentad, lat=lats).plot(ax=ax,
color='k',
linestyle='-.')
theta_equiv_850.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='C0')
theta_equiv_net.sel(xofyear=pentad, lat=lats).plot(ax=ax, color='C2')
ax.plot([w_max_lat.sel(xofyear=pentad),
w_max_lat.sel(xofyear=pentad)], [300., 380.],
color='0.7',
linestyle=':')
ax.set_title('')
ax.set_xlabel('')
ax.set_ylim(300., 380.)
ax.set_xlim(-35., 35.)
ax.grid(True, linestyle=':')
ax.set_ylabel('$\Theta$, K')
