File:Teegarden b as hot runaway greehouse world 1.png

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Teegarden b as hot runaway greehouse world

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Description
English: Teegarden b as hot runaway greehouse world
Date
Source Own work
Author Merikanto

Climt source code to produce image

                                                                        1. 3
    1. slushball Earth
    1. seasonal climlab energy balance model
  4. python3/climlab code
  6. 23.10.2023 0000.0004a
                                                    1. 3

import numpy as np import matplotlib.pyplot as plt from matplotlib import cm import climlab from climlab import constants as const from climlab.process.diagnostic import DiagnosticProcess from climlab.domain.field import Field, global_mean

numyears=500 plotvar=0 ## 1,2,3 lot temp, ice, mean albedo

  1. S1=1365.2* 6.24

S1=1365.2*1.08

  1. S1=1361*0.93

co2=1e6*5

  1. orbit={'ecc': 0.0167643, 'long_peri': 280.32687, 'obliquity': 23.459277, 'S0':S1}

orbit={'ecc': 0.03, 'long_peri': 0.0, 'obliquity': 0.0, 'S0':S1}

title0=' Teegarden b ' title1='Temperatures throughout the year °C \n if S0 = '+ str(round(S1)) +' W m-2 , pressure of CO2 = '+str(co2)+' ppm volume'

class tanalbedo(DiagnosticProcess): 

   def __init__(self, **kwargs):
       super(tanalbedo, self).__init__(**kwargs)

       self.add_diagnostic('albedo')
       Ts = self.state['Ts']
       self._compute_fixed()

   def _compute_fixed(self):
       Ts = self.state['Ts']
       try:
           lon, lat = np.meshgrid(self.lon, self.lat)
       except:
           lat = self.lat
       phi = lat

       try:
           #albedo=np.zeros(len(phi));
           #albedo=0.42-0.20*np.tanh(0.052*(Ts-3))
           albedo=0.3;
       except:
           albedo = np.zeros_like(phi)

       dom = next(iter(self.domains.values()))
       self.albedo = Field(albedo, domain=dom)

   def _compute(self):
       self._compute_fixed()

       return {}
  1. creating EBM model
  1. ebm= climlab.EBM(CO2=co2,orbit={'ecc': 0.0167643, 'long_peri': 280.32687, 'obliquity': 23.459277, 'S0':S1})

ebm0= climlab.EBM_seasonal(water_depth=1.0, a0=0.16, num_lat=90, lum_lon=None, num_lev=10,num_lon=None , orbit=orbit)

ebm=climlab.process_like(ebm0)

  1. ebm.step_forward()
  2. print(ebm.diagnostics)
  1. quit(-1)

surface = ebm.domains['Ts']

  1. define new insolation and SW process

ebm.remove_subprocess('insolation') insolation = climlab.radiation.DailyInsolation(domains=surface, orb = orbit, **ebm.param) insolation.S0=S1

    1. sun = climlab.radiation.DailyInsolation(domains=model.Ts.domain)

ebm.add_subprocess('insolation', insolation)

  1. ebm.step_forward()
  1. print(insolation.diagnostics)
  1. print (insolation.insolation)
  2. print (np.max(insolation.insolation))
    1. print(insolation.S0)
  1. quit(-1)

ebm.remove_subprocess('albedo') alb = climlab.surface.albedo.StepFunctionAlbedo(state=ebm.state, Tf=-10, **ebm.param)

  1. alb = climlab.surface.albedo.ConstantAlbedo(domains=surface, **ebm.param)
  2. alb = tanalbedo(state=ebm.state, **ebm.param)

ebm.add_subprocess('albedo', alb) ebm.remove_subprocess('SW') SW = climlab.radiation.absorbed_shorwave.SimpleAbsorbedShortwave(insolation=insolation.insolation, state = ebm.state, albedo = alb.albedo, **ebm.param) ebm.add_subprocess('SW', SW) ebm.remove_subprocess('Lw') LW = climlab.radiation.aplusbt.AplusBT_CO2(CO2=co2,state=ebm.state, **ebm.param) ebm.add_subprocess('LW', LW)

  1. print(SW.diagnostics)
  1. quit(-1)
  1. ebm.CO2=co2

ebm.remove_subprocess('diffusion') diff = climlab.dynamics.MeridionalMoistDiffusion(state=ebm.state, timestep=ebm.timestep) ebm.add_subprocess('diffusion', diff)

  1. print (ebm)

ebm.step_forward()

  1. ebm.diagnostics
  1. ebm.integrate_years(numyears)

ebm.integrate_converge()

  1. print(ebm.Ts)
  1. plt.plot(ebm.Ts)
  1. plt.show()

num_steps_per_year = int(ebm.time['num_steps_per_year']) mean_year = np.empty(num_steps_per_year) for m in range(num_steps_per_year): ebm.step_forward() mean_year[m] = ebm.global_mean_temperature() Tmean_year = np.mean(mean_year)

print(round(Tmean_year,2))

if(plotvar==0): 

       num_steps_per_year = int(ebm.time['num_steps_per_year'])
       Tyear = np.empty((ebm.lat.size, num_steps_per_year))
       for m in range(num_steps_per_year):
           ebm.step_forward()
           Tyear[:,m] = np.squeeze(ebm.Ts)
       Tmin=np.min(Tyear)
       Tmax=np.max(Tyear)
       
       fig = plt.figure(figsize=(5,5))
       ax = fig.add_subplot(111)
       
       factor = 365. / num_steps_per_year
       #cmap1=plt.cm.seismic
       #cmap1=plt.cm.turbo
       cmap1=plt.cm.hot
       #cmap1=plt.cm.coolwarm
       #cmap1=plt.cm.winter
       #cmap1=plt.cm.cool_r
       #cmap1=plt.cm.cool
       #cmap1=cmap1.reversed()      
       #levels1=[-80,-70,-60,-50,-40,-30]
       levels2=[-200,-100,-80,-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60,80,90, 95,100,105,110,150, 200,250,300,400,500,600,700,800,900,1000,1200]
       #levels2=[90,92,95,98,100,102,105,110,120]
       Tminv=0
       Tmaxv=1000
       #cax = ax.contourf(factor * np.arange(num_steps_per_year),
       #              ebm.lat, Tyear[:,:], 
       #              cmap=cmap1, vmin=Tminv, vmax=Tmaxv, antialiased=False, levels=256)
       ax.imshow(Tyear[:,:],origin="lower", extent=[0,360,-90,90],cmap=cmap1, vmin=Tminv, vmax=Tmaxv, interpolation="bicubic")
       cs1 = ax.contour(factor * np.arange(num_steps_per_year),ebm.lat, Tyear[:,:],   origin="lower", extent=[0,360,-90,90],colors='#00005f', vmin=Tminv, vmax=Tmaxv, levels=levels2)
       ax.clabel(cs1, cs1.levels, inline=True, fontsize=14)                     
       #cbar1 = plt.colorbar(cax)
       ax.set_title(title1, fontsize=12)
       fig.suptitle(title0, fontsize=22)
       ##ax_set_suptitle(title0, fontsize=18)
       ax.tick_params(axis='x', labelsize=12)
       ax.tick_params(axis='y', labelsize=12)
       ax.set_xlabel('Days of year', fontsize=13)
       ax.set_ylabel('Latitude', fontsize=13)
       plt.tight_layout()
       plt.savefig('1000dpi.svg', dpi=1000)

if(plotvar==1): 

       if 'Tf' in ebm.subprocess['albedo'].param.keys():
           Tf = ebm.subprocess['albedo'].param['Tf']
       else:
           print('No ice considered in this model. Can not plot.')

       num_steps_per_year = int(ebm.time['num_steps_per_year'])
       ice_year = np.empty((ebm.lat.size, num_steps_per_year))
       for m in range(num_steps_per_year):
           ebm.step_forward()
           ice_year[:,m] = np.where(np.squeeze(ebm.Ts) <= Tf, 0, 1)
       
       fig = plt.figure(figsize=(5,5))
       ax = fig.add_subplot(111)
       
       factor = 365. / num_steps_per_year
       cax = ax.contourf(factor * np.arange(num_steps_per_year),
                     ebm.lat, ice_year[:,:], 
                     cmap=plt.cm.seismic, vmin=0, vmax=1, levels=2)
       cbar1 = plt.colorbar(cax)
       
       ax.set_title('Ice throughout the year', fontsize=14)
       ax.set_xlabel('Days of year', fontsize=14)
       ax.set_ylabel('Latitude', fontsize=14)

if(plotvar==2): 

       fig = plt.figure(figsize=(7.5,5))

       # Temperature plot
       ax2 = fig.add_subplot(111)
       ax2.plot(ebm.lat,ebm.albedo)

       ax2.set_title('Albedo', fontsize=14)
       ax2.set_xlabel('latitude', fontsize=10)
       ax2.set_ylabel(, fontsize=12)

       ax2.set_xticks([-90,-60,-30,0,30,60,90])
       ax2.set_xlim([-90,90])
       ax2.set_ylim([0,1])
       ax2.grid()



       plt.show()

plt.show()

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w:en:Creative Commons
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