Two excellent and accessible papers on climate models have appeared today. Ross McKitrick, writing in the Financial Post, wonders about model evaluation:
So how do models do at predicting the spatial pattern of warming over land? Though the 2007 report of the Intergovernmental Panel on Climate Change (IPCC) devoted a whole chapter to model evaluation, it said almost nothing about this question. The IPCC talked mainly about static features, such as whether the model can make the tropics hot and poles cold, and so forth. But it was mostly silent on the spatial changes. A 2008 report of the U.S. Climate Change Science Program went a bit deeper, but only to report on tests of how daily and seasonal variations in models matched the real world (is winter a suitable amount colder than summer, etc.).
The reports weren’t ignoring anything: There just hasn’t been much work on the topic.
Meanwhile, Tamsin Edwards has this:
Models are always wrong, but what is more important is to know how wrong they are: to have a good estimate of the uncertainty about the prediction. Mark and Patrick explain that our uncertainties are so large because climate prediction is a chain of very many links. The results of global simulators are fed into regional simulators (for example, covering only Europe), and the results of these are fed into another set of simulators to predict the impacts of climate change on sea level, or crops, or humans. At each stage in the chain the range of possibilities branches out like a tree: there are many global and regional climate simulators, and several different simulators of impacts, and each simulator may be used to make multiple predictions if they have parameters (which can be thought of as “control dials”) for which the best settings are not known. And all of this is repeated for several different “possible futures” of greenhouse gas emissions, in the hope of distinguishing the effect of different actions.