Matt Briggs has penned his own critique of the BEST paper, noting in the process that he broadly agrees with Doug Keenan's points. It's not for the mathematically faint of heart though. Here's the conclusion.
Statisticians and those who use statistics never or rarely speak of model uncertainty (same with your more vocal sort of climatologist). The reason is simple: there aren't cookbook recipes that give automatic measures of this uncertainty. There can't be, either, because the truth of a model can only be ascertained externally.
Yet all statistical results are conditioned on the models' truth. Experience with statistical models shows that they are often too sure, especially when they are complex, as the BEST model is (and which assumes that temperature varies so smoothly over geography). No, I can't prove this. But I have given good reason to suspect it is true. You may continue to believe in the certainty of the model, but this would be yet another example of the triumph of hope over experience. What it means is that the uncertainty bounds should be widened further still. By how much, I don't know.