Someone (thanks Sven) recently drew my attention to Forster and Gregory, 2006: The Climate Sensitivity and Its Components Diagnosed from Earth Radiation Budget Data.

Using results from the ERBE project, they have estimated climate sensitivity to lie in the range 1.0-4.1C (95% confidence interval). This looks like a very impressive headline result, but on closer examination, it's not quite what it seems (the authors are very up-front about this, I'm not for a minute accusing them of trying to mislead anyone). In fact their paper is a rather technical one that presents a method to investigate the different contributions to overall sensitivity, and it does not make a big noise about this overall result.

So, why is this 1.0-4.1C range not an exciting new result? A recent paper by Frame et al (including Gregory) gives a nice explanation and illustration of the point, and I'll give a simple reprise of it here. In order to use Bayes' Theorem to generate a probabilistic estimate from observational data, we need to start from some prior distribution - typically, we choose this to be something fairly ignorant (like a uniform distribution) in order to minimise the risk that this will strongly distort our results.

However, climate sensitivity S is related to something called the feedback parameter L via the equation

Using results from the ERBE project, they have estimated climate sensitivity to lie in the range 1.0-4.1C (95% confidence interval). This looks like a very impressive headline result, but on closer examination, it's not quite what it seems (the authors are very up-front about this, I'm not for a minute accusing them of trying to mislead anyone). In fact their paper is a rather technical one that presents a method to investigate the different contributions to overall sensitivity, and it does not make a big noise about this overall result.

So, why is this 1.0-4.1C range not an exciting new result? A recent paper by Frame et al (including Gregory) gives a nice explanation and illustration of the point, and I'll give a simple reprise of it here. In order to use Bayes' Theorem to generate a probabilistic estimate from observational data, we need to start from some prior distribution - typically, we choose this to be something fairly ignorant (like a uniform distribution) in order to minimise the risk that this will strongly distort our results.

However, climate sensitivity S is related to something called the feedback parameter L via the equation

S=3.7/L

(where 3.7 is the radiative forcing of doubled CO

_{2}, in Wm

^{-2}). S and L are both unknown, of course. This inverse relationship means that a uniform prior in S is a strongly biased prior in L, and vice-versa. Whichever prior we choose has strong implications for our result, one way or the other. There really is no way round this problem, short of a whole new treatment of probabilistic inference such as imprecise probability (which IMO is not really justified in this case, although it's an interesting idea). Frame et al recommend that if you want to estimate S, you should choose the prior to be uniform in S. I disagree with some aspects of their presentation (about which more later) but agree that this is probably a sensible convention. However, F&G chose their prior to be uniform in L, for reasons which they explain and justify - basically, they are primarily interested in L and its components, and do not wish to bias those results.

I've attempted a simple reanalysis of their results to illustrate the importance of their decision. Again, this is rather similar to (but simpler than) the example that Frame et al present. I'm not sure that all details of my analysis are strictly valid because I'm not starting from F&G's original data but merely working backwards from their result, but I think it shouldn't be far off.

Case A, the top row, are the results presented by F&G (click the pic for a larger version). The blue lines give their prior distributions and the red is the posterior. You can see that the top left (feedback) has a nice flat prior, which gives a perfectly Gaussian posterior with 95% CI of 0.9-3.7 (cos that's what I assumed as an input based on F&G). The top right, however, has a strongly skewed prior, which favours low values. The 95% CI of the posterior here is 1.0-4.1C, as F&G state.

The lower row (Case B) are what they would have obtained if they had chosen a uniform prior in S. In this case, the left plot has a strongly skewed prior and the posterior is shifted to lower values compared to Case A - in fact it has a bimodal distribution. The LH peak would have shot off to infinity but I truncated the calculation. The bottom right graph shows the effect on climate sensitivity - the posterior has a long tail that just keeps on going (it's clearer on the larger version below). In this calculation, the 95% confidence interval of the posterior runs from 1-15C, but that's only because I artificially truncated the calculation at 20C. Without that limit, I think it would not have had a finite upper bound at all.

So, that's why F&G didn't trumpet their results as having solved the climate sensitivity estimation problem.

However, their analysis does clearly favour a moderate value for S over an extreme one, and it appears to be just about entirely independent of all the constraints that we used in our recent paper (hmm...I wonder if anyone will argue that the overlap in obs with the recent temperature record makes this not strictly valid?). Therefore, it can (in fact should) be added into the mix in order to generate an improved overall estimate. I've done this below:

For context, I've left on the original 3 constraints (blue) that we used to generate the main result (red). F&G's constraint (technically a likelihood function, the same shape as the posterior in the lower right plot above) is the green dashed line. You can see how it stubbornly refuses to drop to zero as it leaves the right hand edge of the plot, but that doesn't matter as we already know those values are implausible.

Multiplying the three blue curves and the green dashed one, we get the green solid shape. This is now a new improved estimate of climate sensitivity, which takes account of F&G's work. The upper limit has dropped to about 3.9C. The median is lowered to about 2.5C. You can also see how the peak is slightly higher, indicating the lower uncertainty that arises as a natural consequence of adding more information.

I don't intend to write another paper presenting this result :-) I think it is reasonable to err on the side of overestimating uncertainty, to make some allowance for the "unknown unknowns". And we'd already made it as clear as we could in the paper that we didn't really think there was as much as a 5% chance of S exceeding 4.5C. But in any case F&G's work does, I think, add some significant extra support and credibility to what we've already presented. I await the forthcoming IPCC draft with interest...

## 4 comments:

Sounds like you have got a lot of extra work every time another paper comes out without it generating papers for yourself!

Maybe I should make clear that I think the mathematician in the measure the height with a barometer question should loose marks for effort. However, you shouldn't :)

crandles

James, I read your paper and it was quite ineteresting. I did notice that you guys didn't include the Douglas et al Pinatobu estimates. I've read their work, and the replies, and their counters to the replies... it's sorta interesting and all, but I do have some questions about it.

(note, me no climatologist) but I did think that the math in the douglas et al reply made sense. however, the weird bit (to me) is that the ash might have went all over the troposhere, yet eventually most landed in the ocean (at least 70% or so) and most must have been rather small particulates or some must have been larger "flakes" falling down on the oceans. I have some samples from different volcanic events, and most are pretty light grey and some are almost white.

I imagine that much of the ash from the pinotuba event eventually "landed" in the seas, and it seems likely to me that it would have a prounounced effect on ocean surface temps.(the light colored particulates absorbing more insolation) (and with mixing going a bit deeper) In my opinion that might explain their "quick recovery" anomalous situation. What do ya think?

I'm not sure why you think

whiteash would absorb a lot of heat. But anyway, I'm not an aerosol specialist - I just used the research that looks credible.James, I appreciate your response, and realize I should have made myself a bit clearer in my post and in my questioning. Please fault me for usually being a bit "lit" in my posts to blogs, and even now in my reply ;)

I'm not very sure in what I was trying to get at with my earler response, but think I have a sense of what I might have been asking.

Essentially, I believe it was merely a hypothetical "what if" and "how could that be" in the "what if" scenario in the case of the Douglas paper.

As far as the "white" ash falling on SS being a net absorber, I imagine I was thinking that even though the color is bright, if it ended up floating on the surface, even limitedly temporally, it would tend? to reduce some oceanic insolation absorptions in the sense that it might reduce the liquid water's specular reflectivity,( a -) as well as being an absorber for the higher frequency photons which would otherwise penetrate much deeper. Also as the ash sank, I thought it might increase the vertical mixing (due to gravity and the shear tonnage of the ash) a little bit, (as well as adding a tad more heat frictionally) which might have the net effect to release more heat from the waters into the near troposphere (if I understand those processes correctly).

Granted, the net effects are probably quite small in magnitude. I imagine I might have been just blabbering on inconsequentials a bit. No more for now :)

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