I recommend this nice discussion of climate change on Andrew Gelman's blog. Physicist Phil, the guest-author of the post, gives his prior and posterior probability distribution for temperature sensitivity as a function of CO2 density. I guess I'm somewhere between Skeptic and Phil Prior.
As an aside, I think it is worth distinguishing between a situation where one has a high confidence level about a probability distribution (e.g., at an honest casino game like roulette or blackjack) versus in the real world, where even the pdf itself isn't known with any confidence (Knightian uncertainty). Personally, I am in the latter situation with climate science.
Here is an excerpt from a skeptic's comment on the post:
... So where are we on global climate change? We have some basic physics that predicts some warming caused by CO2, but a lot of positive and negative feedbacks that could amplify and attenuate temperature increases. We have computer models we can't trust for a variety of reasons. We have temperature station data that might have been corrupted by arbitrary "adjustments" to produce a warming trend. We have the north polar ice area decreasing, while the south polar ice area is constant or increasing. Next year an earth satellite will launch that should give us good measurements of polar ice thickness using radar. Let's hope that data doesn't get corrupted. We have some alternate theories to explain temperature increases such as cosmic ray flux. All this adds up to a confused and uncertain picture. The science is hardly "settled."
Finally the public is not buying AGW. Anyone with common sense can see that the big funding governments have poured into climate science has corrupted it. Until this whole thing gets an independent review from trustworthy people, it will not enjoy general acceptance. You can look for that at the ballot box next year.
For a dose of (justified?) certitude, see this angry letter, signed by numerous National Academy of Science members, that appeared in Science last week. See here for a systematic study of the record of expert predictions about complex systems. Scientists are only slightly less susceptible than others to group think.
