Ross McKitrick writes:
Lise Tole and I have published a paper in Climate Dynamics testing the ability of climate models to reproduce the spatial pattern of temperature trends over land. This builds on previous work of mine looking at the correlation between indicators of industrial development over land and the spatial pattern of warming trends, a relationship that is not predicted by models and is supposed to have been filtered out of the surface climate record. The paper is
- **McKitrick, Ross R. and Lise Tole (2012) “Evaluating Explanatory Models of the Spatial Pattern of Surface Climate Trends using Model Selection and Bayesian Averaging Methods” Climate Dynamics, 2012, DOI: 10.1007/s00382-012-1418-9
Preprint here; data and code archive here; university press release here. We apply classical and Bayesian methods to look at how well 3 different types of variables can explain the spatial pattern of temperature trends over 1979-2002. One type is the output of a collection of 22 General Circulation Models (GCMs) used by the IPCC in the Fourth Assessment Report. Another is a collection of measures of socioeconomic development over land. The third is a collection of geopgraphic indicators including latitude, coastline proximity and tropospheric temperature trends. The question is whether one can justify an extreme position that rules out one or more categories of data, or whether some combination of the three types is necessary. I would describe the IPCC position as extreme since they dismiss the role of socioeconomic factors in their assessments. In the classical tests, we look at whether any combination of one or two types can "encompass" the third, and whether non-nested tests combining pairs of groups reject either 0% or 100% weighting on either. ("Encompass" means provide sufficient explanatory power not only to fit the data but also to account for the apparent explanatory power of the rival model.) In all cases we strongly reject leaving out the socioeconomic data. In only 3 of 22 cases do we reject leaving out the climate model data, but in one of those cases the correlation is negative, so only 2 count--that is, in 20 of 22 cases we find the climate models are either no better than or worse than random numbers. We then apply Bayesian Model Averaging to search over the space of 537 million possible combinations of explanatory variables and generate coefficients and standard errors robust to model selection (aka cherry-picking). In addition to the geographic data (which we include by assumption) we identify 3 socioeconomic variables and 3 climate models as the ones that belong in the optimal explanatory model, a combination that encompasses all remaining data. So our conclusion is that a valid explanatory model of the pattern of climate change over land requires use of both socioeconomic indicators and GCM processes. The failure to include the socioeconomic factors in empirical work may be biasing analysis of the magnitude and causes of observed climate trends since 1979.
I have written a pair of op-eds to explain the work. The first part appeared in the Financial Post on June 21. A version with the citations provided is here. Part II is here online, and the versions with citations is here.