Mabinogogiblog: Lindzen on Climate sensitivity

Richard Lindzen makes several statements around climate sensitivity (CS).

He persistently states that whereas temperatures should have increased by 2*C given the amount of CO2 we have added to the atmosphere, they have only increased by 0.8*C. "we should already have seen much more warming than we have seen thus far", he says.

It has been pointed out many times that the difference is due to extra heat being absorbed into the oceans, but he still makes the same pronouncement.

He also claims that aerosols warm as well as cool, which is true. So he ignores them, which is wrong.

Joe Romm says:
"Ramanathan and Carmichael (2008), on the other hand, examined both the warming and cooling effects of aerosols. This study found that black carbon has a warming effect of approximately 0.9 W/m², while aerosol cooling effects account for approximately -2.3 W/m². Thus Ramanathan and Carmichael find that the net radiative forcing from aerosols + black carbon is approximately -1.4 W/m². This is broadly consistent with the IPCC net aerosol + black carbon forcing most likely value of -1.1 W/m²:"

Aerosols have a net cooling effect, and cannot be neglected, as Lindzen would prefer.

Lindzen proposes the Iris effect, whereby cirrus clouds would multiply and cool the planet in response to warming, thus creating a lower CS.

Lin et al. refuted this in 2001, saying "decreases in these clouds would cause a significant but weak positive feedback to the climate system, instead of providing a strong negative feedback."

Lindzen and Choi wrote a paper in 2009 which examined sea surface temperatures and outgoing radiation. The paper was severely criticised, even by Spencer. This year Lindzen and Choi wrote another paper that claimed to have addressed the errors, and still produced a low CS figure.

Amid discussion of the paper on Judith Curry's blog, I find this critique by Robert Dekker:

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Lindzen and Choi obtain different feedback numbers from the same ERBE data than Trenberth 2010 and two other papers, and Lindzen claims (unsurprisingly) that his method is more accurately reproducing feedback numbers.

When I looked at the details of his method however, I found something very concerning :

The Lindzen and Choi method of doing FLUX/SST analysis (called “lead and lag” by Lindzen) seems to have a (strong?) bias towards negative feedback.

Here is why :
L&C analyze fragments of SST changes that are either rising or falling, and then measures the FLUX response over the same period.
No problem there, has been done many times before by numerous other scientists.

The difference is that Lindzen is looking back and forth (lead and lag) in time, and finds the FLUX response that has the highest correlation with the SST change.

First remember that the FLUX (response) has significant noise on it. Let’s note that if you do not look back and forth in time (no lead or lag), then on average the FLUX response will tell you the average FLUX response to that SST change.

But also remember that the FLUX response with the highest correlation with SST will always be the response that starts at one extreme and ends at the other extreme. All other responses will correlate less, since they will show opposite slopes at the start and/or end points, which obviously don’t correlate well with the SST.

So, if you are allowed to look back and forth in time through that noisy signal, you have a high chance of finding a lead or lag time where the FLUX response is larger (and thus correlates better) than the no-lag response alone.

So Lindzen and Choi method will (for each fragment of SST analysed) find the lead or lag time where the FLUX response is the largest !

When the FLUX response is larger for a certain SST change, the calculated feedback will be lower, and thus this method has a bias towards lowering the feedback calculated from the ERBE data.

Let me note that the effect (bias) will be stronger the more lead or lag time is allowed, since there will be more start and end-points in the noise to consider, and the largest response will correlate the best.

So for short lag times and strong negative feedback (large FLUX response), Lindzen’s method will be approximately correct. But for no-feedback or positive feedback the lead-lag bias will be very significant.

In fact Lindzen mentions himself that his method works best for large negative feedbacks .

He also mentions that his method works less well for small feedbacks (and consequently) large lag times, which, as I showed above is consistent with increased bias.

Interestingly enough, he does not show what feedback parameter number he obtains for a system with no feedback or positive feedback, in which case the lead-lag-noise bias will be greatest.

Needless to say that maybe Lindzen drew some very premature conclusions when he discards other scientists’ work (Trenberth et al, Dessler et al) who do NOT use his (biased) lead-lag-correlate method.

Now I have not quantified this bias yet, but this bias should be very easily reproducible using Lindzen’s (Spencer’s) “simple model” simulation,

What I’m saying is that in a climate system without feedback (such as a planet without an atmosphere) the Lindzen and Choi method will find a negative feedback just because of the noise. And that is simply incorrect

Rob Dekker

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This is a pretty devastating critique.

To summarise thus far, therefore:

The claim of low climate sensitivity made by sceptics is refuted by the evidence from a large body of science, from modelling, palaeoclimate and observations, which shows is that CS is in the region of 3*C.
The sceptics have been unable to produce substantial and robust evidence to support their claim for low sensitivity. Their claim has no scientific evidence to support it. All the evidence points in the opposite direction, therefore their position has no scientific credibility.

To clinch the argument, Romm points out that the models only use the fast reacting feedbacks.
If we add in the slow feedbacks, climate sensitivity moves toward much more dangerous levels:

"most climate models focus on the getting the fast-feedbacks right, like water vapor and polar amplification, but utterly neglect the ones that operate over decades: