Dr. McKitrick’s new paper with Lise Tole is now online at Climate Dynamics. He also has an op-ed in the Financial Post on June 13. A version with the citations provided is here. Part II is here online, and the versions with citations is here.
**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
The abstract is:
We evaluate three categories of variables for explaining the spatial pattern of warming and cooling trends over land: predictions of general circulation models (GCMs) in response to observed forcings; geographical factors like latitude and pressure; and socioeconomic influences on the land surface and data quality. Spatial autocorrelation (SAC) in the observed trend pattern is removed from the residuals by a well-specified explanatory model. Encompassing tests show that none of the three classes of variables account for the contributions of the other two, though 20 of 22 GCMs individually contribute either no significant explanatory power or yield a trend pattern negatively correlated with observations. Non-nested testing rejects the null hypothesis that socioeconomic variables have no explanatory power. We apply a Bayesian Model Averaging (BMA) method to search over all possible linear combinations of explanatory variables and generate posterior coefficient distributions robust to model selection. These results, confirmed by classical encompassing tests, indicate that the geographical variables plus three of the 22 GCMs and three socioeconomic variables provide all the explanatory power in the data set. We conclude that the most valid model of the spatial pattern of trends in land surface temperature records over 1979-2002 requires a combination of the processes represented in some GCMs and certain socioeconomic measures that capture data quality variations and changes to the land surface.
He writes on his website:
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.