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File:Gliese 12 b temperature if fast rotating ocean planet 1.png

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Gliese 12 b temperature if fast rotating ocean planet

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Description
English: Gliese 12 b temperature if fast rotating ocean planet
Date
Source Own work
Author Merikanto

This image is based on Climlab. 

https://github.com/climlab/climlab
https://climlab.readthedocs.io/en/latest/

Python3 Climlab source code

import math 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 from scipy.interpolate import griddata from skimage.transform import resize

numyears=30 ##n no function

numlat=18 numlev=6

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

waterdepth=20

  1. S1=1365.2*1

au1=1.00

Sk=1/math.pow(au1,2) ## relative sun constant to Earth now

Sk=1.65

S1=1361.5*Sk

  1. ecc=0.0167643,
  2. long_peri=280.32687
  3. obliquity=23.459277

ecc=0.2 long_peri=0 obliquity=0

  1. co2=280 ##co2 amount ppmv

co2=280

diffu1=0.3 # meridional diffusivity in m**2/s

albedo0=0.28

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

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

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))

       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})
  2. ebm0= climlab.EBM_seasonal(water_depth=10.0, a0=0.3, num_lat=90, lum_lon=None, num_lev=10,num_lon=None
  3. , orbit=orbit)

ebm0= climlab.EBM_seasonal(water_depth=waterdepth, a0=albedo0, num_lat=numlat, lum_lon=None, num_lev=numlev,num_lon=None , orb=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, **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.StepFunctionAlbedo(state=ebm.state, Tf=-20, **ebm.param)
  2. alb = climlab.surface.albedo.ConstantAlbedo(domains=surface, **ebm.param)
  3. 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') D=diffu1

  1. meridional diffusivity in m**2/s
  2. K = D / ebm.Tatm.domain.heat_capacity * const.a**2

K= D/ 700* const.a**2 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)
  2. ebm.integrate_years(1)

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=round(np.min(Tyear),1)
       Tmax=round(np.max(Tyear),1)
       
       #Tmean=round( np.mean(Tyear),1)
       tmeans1=np.mean(Tyear, axis=1)
       #print (ebm.lat)
       latrads1=np.radians(ebm.lat)
       latcoeffs1=np.power(np.cos(latrads1),2)
       tmeans2=tmeans1*latcoeffs1
       Tmean=np.mean(tmeans2)
       #print (np.shape(tmeans1))
       #quit(-1)        
       fig = plt.figure(figsize=(5,5))
       ax = fig.add_subplot(111)
       
       factor = 365. / num_steps_per_year
       cmap1=plt.cm.seismic
       cmap1=plt.cm.winter
       cmap1=plt.cm.coolwarm
       #cmap1=plt.cm.cool_r
       #cmap1=plt.cm.cool
       #cmap1=cmap1.reversed()      
       #levels1=[-80,-70,-60,-50,-40,-30]
       levels2=[-200,-150,-120,-100,-70,-60,-50,-40,-30,-20,-10,0,5,10,15,20,25,30,35,40,45,50,60,70,80,90,100,105, 110, 115, 120,150,300]
       Tyear2 = resize(Tyear, (Tyear.shape[0] *3, Tyear.shape[1]*2),anti_aliasing=True)
       ax.imshow(Tyear, origin="lower", extent=[0,360,-90,90], cmap=cmap1, interpolation="bicubic")
       #cax = ax.contourf(factor * np.arange(num_steps_per_year),
       #              ebm.lat, Tyear[:,:], 
       #              cmap=cmap1, vmin=Tmin, vmax=Tmax, antialiased=False, levels=levels2)
       cs1 = ax.contour(factor * np.arange(num_steps_per_year), ebm.lat,Tyear[:,:],
                     origin="lower", extent=[0,360,-90,90], alpha=0.5, 
                     colors='#00005f', vmin=Tmin, vmax=Tmax, levels=levels2)
       ax.clabel(cs1, cs1.levels, inline=True, fontsize=14)                     
       #cbar1 = plt.colorbar(cax)
       title1='Temperatures degC of planet, if ecc='+str(round(ecc,3))++str(round(long_peri,2))+' and obliquity='+str(round(obliquity,2))+' deg \n if S0= '+ str(round(S1,1)) +' W m-2 , pressure of CO2= '+str(round(co2,2))+' ppm volume'
       title2="\nTemperature deg C:  min "+str(round(Tmin,2))+" max "+str(round(Tmax,2))+" mean "+str(round(Tmean,2))
       #ax.set_suptitle(title1, fontsize=12)
       ax.set_title(title1+title2, fontsize=11)
       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.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([-120,-100,-90,-60,-30,0,30,60,90,100,105,110, 120,150])
       ax2.set_xlim([-90,90])
       ax2.set_ylim([0,1])
       ax2.grid()
   
       plt.show()

plt.suptitle("Gliese-12 b") plt.title("Temperature deg C") plt.legend() plt.show()

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I, the copyright holder of this work, hereby publish it under the following license:
Creative Commons CC-Zero This file is made available under the Creative Commons CC0 1.0 Universal Public Domain Dedication.
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