def plot_co2_dif(results_dict, output, output_dict, month_list, CO2_conc1, CO2_conc2, time1, figsize, rows, columns, ylim, diff_only = False):
fig,axes = plt.subplots(1, len(month_list), figsize = figsize)
atm_process_dict = {'Tatm':'Temperature (K)',
'atm_diffk':r'Atmospheric $\kappa$ ($\frac{m^2}{s}$)',
'dtheta_dz':r'$\frac{d\theta}{dz}$ ($\frac{K}{m}$)',
'theta':r'$\theta$',
'SW_flux_net_clr':r'Shortwave Flux ($\frac{W}{m^2}$)',
'LW_flux_net_clr':r'Longwave Flux ($\frac{W}{m^2}$)',
'TdotLW_clr':r'Longwave Heating Rate ($\frac{K}{s}$)',
'TdotSW_clr':r'Shortwave Heating Rate ($\frac{K}{s}$)',
'turb_atm_hr':r'Turbulent Heating Rate ($\frac{K}{s}$)',
'advection_Tatm':r'Advective Heating Rate ($\frac{K}{s}$)',
'atm_turbulent_flux':'Turbulent Flux'}
colors = cm.twilight(np.linspace(0,1,7))
for idx_m, month in enumerate(month_list):
ax = axes[idx_m]
if (output == 'atm_diffk' or output == 'dtheta_dz' or output == 'atm_turbulent_flux'):
y1 = np.asarray(results_dict[time1][output_dict['bounds'][output]][CO2_conc1][month][:-1])
else:
y1 = np.asarray(results_dict[time1][output_dict['bounds'][output]][CO2_conc1][month])
yidx = y1 < ylim
y1 = y1[yidx]
if (output == 'TdotLW_clr' or output == 'TdotSW_clr'):
x1 = np.asarray(results_dict[time1][output][CO2_conc1][month])/86400
else:
x1 = np.asarray(results_dict[time1][output][CO2_conc1][month])
x1 = x1[yidx]
if (output == 'atm_diffk' or output == 'dtheta_dz' or output == 'atm_turbulent_flux'):
y2 = np.asarray(results_dict[time1][output_dict['bounds'][output]][CO2_conc2][month][:-1])
else:
y2 = np.asarray(results_dict[time1][output_dict['bounds'][output]][CO2_conc2][month])
y2 = y2[yidx]
if (output == 'TdotLW_clr' or output == 'TdotSW_clr'):
x2 = np.asarray(results_dict[time1][output][CO2_conc2][month])/86400
else:
x2 = np.asarray(results_dict[time1][output][CO2_conc2][month])
x2 = x2[yidx]
if diff_only:
ax.plot(x2-x1, y1, color = colors[4], linewidth = 2, linestyle = '--');
ax.set_title(f'{month}', fontsize = 14)
plt.suptitle(f'{atm_process_dict[output]} difference between {CO2_conc1*(1e6)} ppm and {CO2_conc2*(1e6)} ppm', fontsize = 20, y = 1.05)
else:
ax.plot(x1, y1, color = colors[2], linewidth = 2, label = f'{output} at {CO2_conc1*(1e6)} ppm');
ax.plot(x2, y2, color = colors[4], linewidth = 2,label = f'{output} at {CO2_conc2*(1e6)} ppm');
ax.set_title(f'{month}', fontsize = 14)
plt.suptitle(f'{atm_process_dict[output]} at CO2 {CO2_conc1*(1e6)} ppm and {CO2_conc2*(1e6)} ppm \n at {np.round(time1 / climlab.constants.seconds_per_day, 3)} days', fontsize = 20)
ax.legend()
ax.set_ylabel('Altitude', fontsize = 14)
ax.set_ylim([0,ylim])
ax.set_xlabel(f'{atm_process_dict[output]}', fontsize = 14)
ax.grid(True)
plt.tight_layout()
def plot_time_dif(results_dict, output, output_dict, CO2_conc1, time1, timesteps, month, figsize, ylim, diff_only=False):
fig = plt.figure(figsize = figsize)
atm_process_dict = {'Tatm':'Atmospheric Temperature (K)',
'atm_diffk':r'Atmospheric $\kappa$ ($\frac{m^2}{s}$)',
'dtheta_dz':r'$\frac{d\theta}{dz}$ ($\frac{K}{m}$)',
'theta':r'$\theta$',
'SW_flux_net_clr':r'Shortwave Flux ($\frac{W}{m^2}$)',
'LW_flux_net_clr':r'Longwave Flux ($\frac{W}{m^2}$)',
'TdotLW_clr':r'Longwave Heating Rate ($\frac{K}{s}$)',
'TdotSW_clr':r'Shortwave Heating Rate ($\frac{K}{s}$)',
'turb_atm_hr':r'Turbulent Heating Rate ($\frac{K}{s}$)',
'advection_Tatm':r'Advective Heating Rate ($\frac{K}{s}$)',
'atm_turbulent_flux': r'Atmospheric Turbulent Flux ($\frac{W}{m^2}$)'}
ax = fig.add_subplot()
color = iter(cm.twilight(np.linspace(0,1,len(timesteps)+1)))
if (output == 'atm_diffk' or output == 'dtheta_dz' or output == 'atm_turbulent_flux'):
y1 = np.asarray(results_dict[time1][output_dict['bounds'][output]][CO2_conc1][month][:-1])
else:
y1 = np.asarray(results_dict[time1][output_dict['bounds'][output]][CO2_conc1][month])
yidx = y1 < ylim
y1 = y1[yidx]
if (output == 'TdotLW_clr' or output == 'TdotSW_clr'):
x1 = np.asarray(results_dict[time1][output][CO2_conc1][month])/86400
else:
x1 = np.asarray(results_dict[time1][output][CO2_conc1][month])
x1 = x1[yidx]
if diff_only == False:
plt.plot(x1, y1, label = f'{np.round(time1 / climlab.constants.seconds_per_day, 0)}');
for time2 in timesteps:
c=next(color)
if (output == 'atm_diffk' or output == 'dtheta_dz' or output == 'atm_turbulent_flux'):
y2 = np.asarray(results_dict[time2][output_dict['bounds'][output]][CO2_conc1][month][:-1])
else:
y2 = np.asarray(results_dict[time2][output_dict['bounds'][output]][CO2_conc1][month])
y2 = y2[yidx]
if (output == 'TdotLW_clr' or output == 'TdotSW_clr'):
x2 = np.asarray(results_dict[time2][output][CO2_conc1][month])/86400
else:
x2 = np.asarray(results_dict[time2][output][CO2_conc1][month])
x2 = x2[yidx]
if diff_only:
plt.plot(x2-x1, y1, c=c, label = f'Difference at {np.round(time2 / climlab.constants.seconds_per_day, 0)} days' );
plt.title(f'Difference between {np.round(time1 / climlab.constants.seconds_per_day, 0)} and {np.round(time2 / climlab.constants.seconds_per_day, 0)} \n {atm_process_dict[output]} in {month} at CO2 {CO2_conc1*(1e6)} ppm')
plt.legend()
else:
plt.plot(x2, y2, c=c, label = f'{np.round(time2 / climlab.constants.seconds_per_day, 0)}');
plt.title(f'{atm_process_dict[output]} in {month} at CO2 {CO2_conc1*(1e6)} ppm')
plt.legend(loc='best', title = 'Days')
plt.ylabel('Altitude')
plt.ylim([0,ylim])
plt.xlabel(f'{output}')
plt.xticks(rotation = 45)
plt.tight_layout()
def rad_sfc_HR_plot(rad_sfc_HR, results_dict, CO2_conc1, CO2_conc2, month_list, figsize):
fig = plt.figure(figsize = figsize)
colors = cm.twilight(np.linspace(0,1,7))
for idx_m, month in enumerate(month_list):
plt.subplot(1, 2, idx_m+1)
for t in list(results_dict.keys())[0::10]:
plt.plot(t / climlab.constants.seconds_per_day,-rad_sfc_HR[t][CO2_conc1][month], c = colors[2], marker = '.', linestyle = '')
plt.plot(t / climlab.constants.seconds_per_day,-rad_sfc_HR[t][CO2_conc2][month], c = colors[5], marker = '.', linestyle = '')
plt.title(f'{month} Surface Radiative Heating Rate')
fig.legend([f'{CO2_conc1*(1e6)}',f'{CO2_conc2*(1e6)}'], title = 'ppm', bbox_to_anchor=(1.1, .95))
plt.tight_layout()
def plot_sfc_TOA_process(ds, n_col, results_dict, process, months, single_level_process, figsize):
fig, axes = plt.subplots(1,n_col, figsize = figsize)
color=iter(cm.twilight(np.linspace(0,1,20)))
for CO2_conc in results_dict[0][process].keys():
c=next(color)
for idx, month in enumerate(months):
ax = axes[idx]
x = CO2_conc*10e5
y = results_dict[0][process][CO2_conc][month]
ax.plot(x, y,'o', c = c,mec ='k',ms = 9)
ax.set_xlabel('$CO_2$ Concentration (ppm)', fontsize = 14)
ax.set_ylabel(single_level_process[process], fontsize = 14)
plt.tight_layout()
ax.set_title(f'{month}', fontsize = 14)
def plot_temp(ds, results_dict):
fig, ax = plt.subplots(figsize = [6,8])
color=iter(cm.twilight(np.linspace(0,1,13)))
for idx, month in enumerate(ds['month'].values):
c=next(color)
x = np.asarray(results_dict[0]['Tatm'][0.00038][month])
x = np.append(x, results_dict[0]['Ts'][0.00038][month])
y = results_dict[0]['lev'][0.00038][month]
y = np.append(0., y)
plt.plot(x, y, c = c, label = month, lw = 2)
plt.xlabel('Temperature (K)', fontsize = 16)
plt.ylabel('Pressure (hPa)', fontsize = 16)
plt.ylim([750,-50])
plt.legend(title = 'Months')
def plot_sum_hr(results_dict, month, CO2_conc, timesteps):
fig = plt.figure(figsize = [8,8])
ax = fig.add_subplot(1, 1, 1)
color=iter(cm.twilight(np.linspace(0,1,len(timesteps)+1)))
for time in timesteps:
c = next(color)
x1 = np.asarray(results_dict[time]['TdotLW_clr'][CO2_conc][month]/86400) + np.asarray(results_dict[time]['TdotSW_clr'][CO2_conc][month]/86400) + np.asarray(results_dict[time]['turb_atm_hr'][CO2_conc][month]) + np.asarray((results_dict[time]['advection_Tatm'][CO2_conc][month]))
y1 = results_dict[time]['z_bounds'][CO2_conc][month][:-1]
plt.plot(x1, y1, c= c, label = np.round(time/climlab.constants.seconds_per_day, 0));
plt.legend(title = 'Days')
plt.ylabel('Altitude', fontsize = 14)
plt.xlabel(f'Heating Rate (K/s)', fontsize = 14)
plt.title(f'Sum of heating rates in {month} at CO2 {CO2_conc * 1e6} ppm', fontsize = 16);
def plot_turbulent_flux(ds, results_dict, month, CO2, timesteps, ylim):
fig, ax = plt.subplots(figsize = [6,8])
color=iter(cm.twilight(np.linspace(0,1,len(timesteps)+1)))
for idx, time in enumerate(timesteps):
c=next(color)
y = results_dict[time]['z_bounds'][CO2][month]
yidx = y < ylim
y = y[yidx]
x = np.asarray(results_dict[time]['atm_turbulent_flux'][CO2][month])
x = np.append(x, results_dict[time]['sfc_turbulent_flux'][CO2][month])
x = x[yidx]
plt.plot(x, y, c = c, label = np.round(time / climlab.constants.seconds_per_day, 0), lw = 2)
plt.xlabel('Turbulent Flux', fontsize = 14)
plt.ylabel('Altitude relative to surface level (km)', fontsize = 14)
plt.title(f'Turbulent Flux Profiles {month}', fontsize = 20)
plt.legend(title="Days")
plt.xticks(rotation = '45')
def single_level_plot(results_dict, output_list, CO2_conc1, CO2_conc2, month_list, figsize):
fig, axes = plt.subplots(len(output_list), len(month_list), figsize = figsize)
colors = cm.twilight(np.linspace(0,1,7))
for idx_m, month in enumerate(month_list):
for idx_o, output in enumerate(output_list):
ax = axes[idx_o, idx_m]
for t in list(results_dict.keys())[0::10]:
if output == 'total_sfc_flux':
ax.plot(t / climlab.constants.seconds_per_day,results_dict[t][output][CO2_conc1][month]*-1, c = colors[2], marker = '.', linestyle = '')
ax.plot(t / climlab.constants.seconds_per_day,results_dict[t][output][CO2_conc2][month]*-1, c = colors[5], marker = '.', linestyle = '')
else:
ax.plot(t / climlab.constants.seconds_per_day,results_dict[t][output][CO2_conc1][month], c = colors[2], marker = '.', linestyle = '')
ax.plot(t / climlab.constants.seconds_per_day,results_dict[t][output][CO2_conc2][month], c = colors[5], marker = '.', linestyle = '')
axes[-1,idx_m].set_xlabel('Time (days)', fontsize = 14)
#ax.set_xticks(rotation = 45)
axes[idx_o, 0].set_ylabel(f'{util.single_level_process[output]}', fontsize = 14, wrap=True)
axes[0,idx_m].set_title(month, fontsize = 20)
fig.legend([f'{CO2_conc1*(1e6)}',f'{CO2_conc2*(1e6)}'], title = 'ppm', bbox_to_anchor=(1.1, .95))
plt.tight_layout()
def plot_adv_LW_hr(results_dict, month, CO2_conc1, CO2_conc2, time, diff_only = False):
fig = plt.figure(figsize = [8,8])
ax = fig.add_subplot(1, 1, 1)
color=cm.twilight(np.linspace(0,1,5))
x1 = np.asarray(results_dict[time]['TdotLW_clr'][CO2_conc1][month]/86400) + np.asarray((results_dict[time]['advection_Tatm'][CO2_conc1][month]))
y1 = results_dict[time]['z_bounds'][CO2_conc1][month][:-1]
x2 = np.asarray(results_dict[time]['TdotLW_clr'][CO2_conc2][month]/86400) + np.asarray((results_dict[time]['advection_Tatm'][CO2_conc2][month]))
y2 = results_dict[time]['z_bounds'][CO2_conc2][month][:-1]
if diff_only:
plt.plot(x2-x1, y2, c= color[2], label = f"Difference between {CO2_conc1}, {CO2_conc2}")
else:
plt.plot(x1, y1, c= color[2], label = CO2_conc1);
plt.plot(x2, y2, c= color[4], label = CO2_conc2);
plt.legend(title = 'Days')
plt.ylabel('Altitude', fontsize = 14)
plt.xlabel(f'Heating Rate (K/s)', fontsize = 14)
plt.xticks(rotation = '45')
plt.title(f'Sum of LW and Adv HR in {month} at {time} ppm', fontsize = 16);
def plot_temp_timestepped(ds, results_dict, month1, month2, time1, timesteps, ylim, diff_only):
fig, axes = plt.subplots(2,2,figsize = [8,8])
for idx_c, CO2 in enumerate([.00038, .00076]):
for idx_m, month in enumerate([month1,month2]):
ax = axes[idx_c, idx_m]
color=iter(cm.twilight(np.linspace(0,1,len(timesteps)+2)))
y1 = results_dict[time1]['z'][CO2][month]
y1 = np.append(y1, 0.)
yidx = y1 < ylim
y1 = y1[yidx]
x1 = np.asarray(results_dict[time1]['Tatm'][CO2][month])
x1 = np.append(x1, results_dict[time1]['Ts'][CO2][month])
x1 = x1[yidx]
c=next(color)
if diff_only == False:
ax.plot(x1, y1, c = c, label = np.round(time1 / climlab.constants.seconds_per_day, 0), lw = 2)
for idx, time in enumerate(timesteps):
c=next(color)
y2 = results_dict[time]['z'][CO2][month]
y2 = np.append(y2, 0.)
yidx = y2 < ylim
y2 = y2[yidx]
x2 = np.asarray(results_dict[time]['Tatm'][CO2][month])
x2 = np.append(x2, results_dict[time]['Ts'][CO2][month])
x2 = x2[yidx]
if diff_only:
ax.plot(x2-x1, y2, c = c, label = np.round(time / climlab.constants.seconds_per_day, 0), lw = 2)
else:
ax.plot(x2, y2, c = c, label = np.round(time / climlab.constants.seconds_per_day, 0), lw = 2)
axes[1, idx_m].set_xlabel('Temperature (K)', fontsize = 14)
axes[idx_c, 0].set_ylabel('Altitude (km)', fontsize = 14)
axes[0, idx_m].set_title(f'{month}', fontsize = 20)
axes[idx_c, 1].annotate(f'{CO2*1e6} ppm', fontsize = 14, xycoords = 'axes fraction', xy =(1.05,.8))
ax.grid(True)
plt.legend(title="Days", bbox_to_anchor= [1.5,.5])
plt.tight_layout()
