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The authors write:
The huge negative values of coef-ficients of efficiency show that model predictions are much poorer than an elementary prediction based on the time average. This makes future climate projections at the examined locations not credible. Whether or not this conclusion extends to other locations requires expansion of the study, which we have planned. However, the poor GCM performance in all eight locations examined in this study allows little hope, if any. An argument that the poor performance applies merely to the point basis of our comparison, whereas aggregation at large spatial scales would show that GCM outputs are credible, is an unproved conjecture and, in our opinion, a false one.

Schmidt writes:
Looking at the statistics of local temperature and precipitation is useful but picking just a few long records and comparing to the nearest individual grid cells is not sensible. The differences in topography an local micro-climates are probably large and will make a big difference. A better approach would have been to look at aggregated statistics over larger areas. This has in fact been done though - for instance Blender and Fraedrich (2003), and there was a recent paper that looked the AR4 models (in GRL maybe? - I can't quickly find the reference).

This is what part, I guess, of what Jake found to be off about the writing style. It's certainly odd to me. The point basis will logically introduce more uncertainty - so, it's a valid objection to the conclusion of the authors, even though it is one that does not prove that they are wrong. What's more, as Schmidt writes there have in fact been such aggregated studies.

The IPCC writes (AR4 FAQ):

There is considerable confidence that climate models provide credible quantitative estimates of future climate change, particularly at continental scales and above. This confidence comes from the foundation of the models in accepted physical principles and from their ability to reproduce observed features of current climate and past climate changes. Confidence in model estimates is higher for some climate variables (e.g., temperature) than for others (e.g., precipitation). Over several decades of development, models have consistently provided a robust and unambiguous picture of significant climate warming in response to increasing greenhouse gases.

Their claim at the start of the article that "a common argument that models can perform better at larger spatial scales is unsupported" seems very weird. They don't offer any arguments or evidence for it, other than saying that stating otherwise is "an unproven conjecture". I'd say that's fallacious* (shifting the burden of proof) - in addition to being false.

But we've learned that regional models of precipitation should still be taken with a grain of salt.

* While we're at it, saying McIntyre should be a red flag or that Schmidt never met a critique of climate models he liked aren't arguments either. Interesting post and thread all the same.

by nanne (zwaerdenmaecker@gmail.com) on Fri Aug 1st, 2008 at 09:06:43 AM EST

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