human
I am working on the file "GEOL351/ZeroD.ipynb".
The first task is:
Answer 4
write your answer here
Finally, we could ask what kind of climate would be obtained by varying the atmosphere's opacity to outgoing longwave radiation (i.e. its concentration of greenhouse gases). We seek a stability diagram similar to the one above, but cast in terms of radiative pressure $p_{rad}$ instead of $L$. 
Question 5
 What is the effect of lowering $p_{rad}$ on the surface temperature $T_s$? 
You may answer using purely mathematical, physical, or heuristic arguments (one way to do this is to vary $p_{rad}$ and see how it affects the OLR). 
Answer 5
write your answer here
Greenhouse Bifurcation
To draw the bifurcation diagram in terms of $p_{rad}$ we need a bit more machinery. Let's define a function that solves for radiative pressure given the solar flux and the temperature. (if you really want to know, it uses Newton-Raphson's method, but you needn't worry about that)
Can you write Python code for it?
 Write me a the effect of lowering $p_{rad}$ on the surface temperature $T_s$?
assistant
#Here is the effect of lowering $p_{rad}$ on the surface temperature $T_s$.

def radPress(flux,T):
    def g(p,const):
        return OLR(const.T,p) - const.flux
    root = newtSolve(g)
    const= Dummy()
    const.T = T
    const.flux = flux
    root.setParams(const)
    return root(500.)

L = 1370.
TList = [220.+i for i in range(131)]
gList = [radPress((1.-albedo(T))*L/4.,T) for T in TList]
cG = Curve()
cG.addCurve(gList,'pRad')
cG.addCurve(TList,'T')
#Just for variety, here I've shown that instead of
# switching axes, you can just put the data into
# the Curve in a different order
cG.PlotTitle = 'Bifurcation diagram, L = %f W/m**2'%L
cG.Ylabel = 'Surface Temperature (K)'
cG.Xlabel = 'Radiating pressure (mb)'
cG.reverseX = True #Reverse pressure axis so warmer is to the right
plot(cG)