I’ve been receiving a steady stream of e-mails asking when our latest work on feedbacks in the climate system will be published. Since I’ve been trying to fit the material from three (previously rejected) papers into one unified paper, it has taken a bit longer than expected…but we are now very close to submission.

We’ve tentatively decided to submit to Journal of Geophysical Research (JGR) rather than any of the American Meteorological Society (AMS) journals. This is because it appears that JGR editors are somewhat less concerned about a paper’s scientific conclusions supporting the policy goals of the IPCC — regulating greenhouse gas emissions. Indeed, JGR’s instructions to reviewers is to not reject a paper simply because the reviewer does not agree with the paper’s scientific conclusions. More on that later.

As those who have been following our work already know, our main conclusion is that climate sensitivity has been grossly overestimated due to a mix up between cause and effect when researchers have observed how global cloud cover varies with temperature.

To use my favorite example, when researchers have observed that global cloud cover decreases with warming, they have assumed that the warming caused the cloud cover to dissipate. This would be a positive feedback since such a response by clouds would let more sunlight in and enhance the warming.

But what they have ignored is the possibility that causation is actually working in the opposite direction: That the decrease in cloud cover caused the warming…not the other way around. And as shown by Spencer and Braswell (2008 J. Climate), this can mask the true existence of negative feedback.

All 20 of the IPCC climate models now have positive cloud feedbacks, which amplify the small amount of warming from extra carbon dioxide in the atmosphere. But if cloud feedbacks in the climate system are negative, then the climate system does not particularly care how much you drive your SUV. This is an issue of obvious importance to global warming research. Even the IPCC has admitted that cloud feedbacks remain the largest source of uncertainty in predicting global warming.

Significantly, our new work provides a method for identifying which direction of causation is occurring (forcing or feedback), and for obtaining a more accurate estimate of feedback in the presence of clouds forcing a temperature change. The method involves a new way of analyzing graphs of time filtered satellite observations of the Earth (or even of climate model output).

Well…at least I thought it was new way of analyzing graphs. It turns out that we have simply rediscovered a method used in other physical sciences: phase space analysis. This methodology was first introduced by Willard Gibbs in 1901.

We found that by connecting successively plotted points in graphs of how the global average temperature varies over time, versus how global average radiative balance varies over time, one sees two different structures emerge: linear striations, which are the result of feedback, and spirals which are the result of internal radiative forcing by clouds.

But such a methodology is not new. To quote from Wikipedia on the subject of ‘phase space’:

“Often this succession of plotted points is analogous to the system’s state evolving over time. In the end, the phase diagram…can easily elucidate qualities of the system that might not be obvious otherwise.”

Using a simple climate model we show that these two features that show up in the graphs are a direct result of the two directions of causation: temperature causing clouds to change (revealed by ‘feedback stripes’), and clouds causing temperature to change (revealed by ‘radiative forcing spirals’).

The fact that others have found phase space analysis to be a useful methodology is a good thing. It should lend some credibility to our interpretation. Phase space analysis is what has helped others better understand chaos, along with its Lorenz attractor, strange attractors, etc.

And the fact that we find the exact same structures in the output of the IPCC climate models as we do in the satellite observations of the Earth means that the modelers can not claim our interpretation has no physical basis.

And we can also use some additional buzzwords in the new article…which seems to help from the standpoint of reviewers thinking you know what you are talking about. The new paper title is, “Phase Space Analysis of Forcing and Feedback in Models and Satellite Observations of Climate Variability”.

It just rolls of the tongue, doesn’t it?

I am confident the work will get published…eventually. But even if it didn’t, our original published paper on the issue has laid the groundwork…it would just take awhile before the research community fully understands the implications of that work.

What amazes me is the resistance there has been to ‘thinking out of the box’ when trying to estimate the sensitivity of the climate system. Especially since the ‘new’ manner in which we analyze the data has been considered to be thinking in the box by other sciences for over a century now.

And it is truly unfortunate that the AMS, home of Lorenz’s first published work on chaos in 1963, has decided that political correctness is more important than the advancement of science.