Thanks to reader Roger Cohen for brining this to my attention.
Atmospheric scientist Judith Curry recently ran a series of blog posts entitled, “Overconfidence in IPCCâ€™s detection and attribution.” In Part III of that series, she set out a public appeal:
I am no expert in logic. My only formal exposure was a course in freshman logic nearly 40 years ago…I can understand most Bayesian arguments that I’ve encountered, although I’ve never attempted to make one on my own…My point is that I think there are some glaring logical errors in the IPCC’s detection and attribution argument, that it doesn’t take an expert in logic to identify. I look forward to the input from logicians, bayesians, and lawyers in terms of assessing the IPCC’s argument.
I flatter myself that I am an expert in the matter of how evidence supports belief; further, I am happy to offer my assistance. Let me first answer a potential criticism that others might use after reading your appeal. It is possible to argue that when the term “Bayesian” is used, it is synonymous with “better”, especially in statistical analysis contexts. But “Bayesian” is not synonymous with “good results” or “correct modeling.” In other words, following a Bayesian procedure does not guarantee validity. Thus, even though you in your criticism of the IPCC, and the IPCC in its self assessments, use Bayesian procedures, both of you can be wrong in your conclusions. I’m guessing you knew that.
One of your questions involved a potential circular argument used by the IPCC:
The most serious circularity enters into the determination of the forcing data. Given the large uncertainties in forcings and model inadequacies (including a factor of 2 difference in CO2 sensitivity), how is it that each model does a credible job of tracking the 20th century global surface temperature anomalies…? This agreement is accomplished through each modeling group selecting the forcing data set that produces the best agreement with observations, along with model kludges that include adjusting the aerosol forcing to produce good agreement with the surface temperature observations. If a model’s sensitivity is high, it is likely to require greater aerosol forcing to counter the greenhouse warming, and vice versa for a low model sensitivity…Any climate models that uses inverse modeling to determine any aspect of the forcing substantially weakens the attribution argument owing to the introduction of circular reasoning.
A succinct way to state the fallacy is this: A climate modeler assumes the hypothesis that increasing atmospheric CO2 greatly increases atmospheric surface temperature, perhaps through some feedback mechanism. The scientist builds a model that contains a routine which increases (modeled) atmospheric surface temperature when atmospheric CO2 is increased.
He then runs the model using both low and high levels of CO2, and compares the surface temperatures produced by the model under both scenarios. If the temperatures are higher under high levels of CO2, then he writes a press release which says, “Increasing CO2 greatly increases atmospheric surface temperatures.”
As you guessed, this argument is circular. This is not to say the CO2 hypothesis wrong: in fact, the conclusion that “Increasing CO2 greatly increases atmospheric surface temperatures” is certain given the premise that “Increasing CO2 greatly increases atmospheric surface temperatures.” And this hypothesis might still be true given other evidence (a.k.a. premises).
Of course, real models are more complicated, but only slightly. As you say, one goal is to have a model “track” historic temperature. No climate model does this exactly—part of the broad agreement between models is because each model is not an independent creation—but models can be “tuned” so that they crudely mimic previously observed data while still containing the CO2 subroutine. This tuning is accomplished just as you say: in an ad hoc fashion.
A model that tracks historic data is not a reason to believe the CO2 hypothesis. There are an infinite number of hypotheses that might account for the historic observations: the CO2 hypothesis (coupled with other hypotheses about how the atmosphere works) is just one of these.
We can whittle down the set of explanatory hypotheses by asking how each of them fit in with true or highly likely non-climate hypotheses, such as the theories of thermodynamics, etc. Indeed, the CO2 hypothesis is consonant with some of these theories. Thus far, this is the only evidence we have for the CO2 hypothesis.
The true test of climate models, hence of the CO2 hypothesis, will be in how well they predict data not yet seen. If they do that more skillfully than other, parsimonious climate models—like “persistence, or something like it”—then we would have non-circular evidence that the CO2 hypothesis is true. We do not (yet?) have this evidence.
Later, I’ll try to talk about the other matters you mentioned.