- We use an efficient multivariate parameter estimation method with a state-of-the-art GCM for the first time. This is many many orders of magnitude more efficient than multifactorial sampling, and several people have told us over the last couple of years that it couldn't be done. (I won't deny it has some potential drawbacks, though - but we haven't found any show-stoppers).
- We have tried to take some account of model inadequacy - by which I mean the fact that there are no "correct" parameters for which the model actually looks just like the real world, so the standard "perfect model assumption" is wrong.
- We have used out-of-sample data (in this example, a simulation of the Last Glacial Maximum) to attempt to improve the rigour with which predictive skill is assessed. Merely managing to fit a set of data doesn't automatically mean that the model will skillfully predict climate under a different forcing!
Oh, by the way, we didn't really learn anything startling about climate sensitivity. But we all know that's about 3C anyway :-)