There’s certainly something valuable in each of these postings, but unfortunately the discussion and sometimes even the posts themselves get interpreted by many as proofs of additional uncertainty in climate science, while they should be read as a source of inspiration that may lead to some useful applications rather as any kind of statements on the present climate science. Most of the authors of these posts don’t know enough about the state of art of climate science to make such statements, but many skeptics find evidence for their views from everything
I am at least half guilty of that charge, or guilty of a reduced variation. I do think that some of what I have studied casts doubt on the reasonableness of the claim that the equilibrium climate sensitivity and the transient climate sensitivity can be accurately known. On the other hand, I took some inspiration from the Padill et al atricle posted here a few weeks ago, and I have undertaken two projects for next year’s Joint Statistical Meetings in San Diego, one a data analysis and the other (hoped for) a session of invited papers.
As everyone knows: (1) it is easier to point out a problem than to solve it; (2) experts in a field almost never recognize a problem that is pointed out to them by experts in other fields; (3) with many more ways to be wrong than to be right, many efforts to correct a problem, when it is properly identified, will themselves be problematical. Here, Richard Saumarez has presented aliasing as a problem. In the frequency-domain analysis of stationary time series, undersampling results in too much power attributed to low frequencies; from an autoregressive point of view (mine and Padilla et al’s), undersampling results in failure to identify important covariate relationships (linear or nonlinear.) If I understand the posts of Stephen Mosher, he claims that undersampling in the time and spatial domains is not a problem. I don’t believe that the analysis of within-daily measurements has ever shown that he is correct. Previously I identified the equilibrium approximations as sources of model inaccuracy, and I think that the majority response is that the inaccuracies are too small to matter, though that has never been shown either.
Right now, the GCM-based predictions of temperature increase are running too high. It could be that the inaccuracies are due to something unimportant (and the 50-year prediction will somehow turn out to be accurate), but with aliasing and equilibrium assumptions having been pointed out as potential sources of the error, it should at least be admitted that the evidence to date is insufficient to show that those potential sources of error are in fact negligible.