I referred (in Dutch) last week to an interesting op-ed of Ross McKitrick on the regional performance of climate models. In that post he mentions an upcoming paper. That paper as well as a press release are now put online on McKitrick’s webpage.
The paper is a logical next step following several papers in which McKitrick showed that surface temperatures on land have a warm bias due to socioeconomic effects. In the new paper he looks how good different climate models are in reproducing regional temperature changes. It won’t surprise many that the models are not so good. The paper shows that a very simple economic model made better predictions.
Below is the full press release of the University of Guelph:
Climate Models Missing Key Component of Temperature Change
June 20, 2012 – News Release
Climate models used to study temperature change from greenhouse gases are missing a key ingredient — economics, according to a new study by a University of Guelph professor.
Economist Ross McKitrick, an expert in environmental policy analysis, says most models ignore the effects of socioeconomic change on land use changes, making those models inaccurate.
The study, co-authored with Lise Tole of Strathclyde University, was published online in the journal Climate Dynamics.
McKitrick has studied how land use changes from urbanization, agriculture and other surface modifications affect temperature trends around the world. Past research suggests these effects might account for some of the warming patterns in weather data. Climate modelers assume that the effects are filtered out at the data processing stage, he said.
“As a result, when researchers look for explanations of regional patterns of climatic changes, they rule out things like urbanization by assumption and give greater weight to global factors like greenhouse gases and solar variations,” McKitrick said.
The study examined data from 22 sophisticated climate models used by the Intergovernmental Panel on Climate Change (IPCC). The researchers compared how accurately those models would have predicted spatial warming patterns over land between 1979 and 2002 with predictions from a much simpler model using data on regional industrialization and socioeconomic growth.
“The contrasts were striking,” McKitrick said. Twenty of the IPCC models made predictions that were no better than random guesses or that contradicted the observed patterns, he said.
“Only two of the 22 models showed any explanatory power for the temperature changes over the same period.”
By contrast, the simple economic model made much more accurate predictions.
Using various statistical techniques to compare modeling approaches, the researchers found that usually the economic model was essential and the climate model could be dropped, but never the other way around.
One technique involved searching more than 537 million combinations of climate model outputs and socioeconomic data for the best possible mix. The research team found that combining three of the 22 climate models and a small number of socioeconomic indicators best explained the spatial pattern of surface temperature trends.
“By assuming the socioeconomic effects are not there, a lot of climate researchers are ignoring a key feature of the data,” McKitrick said.
The researchers also found that the best climate models aren’t necessarily the most well-known ones. The best models came from labs in China and Russia and from one American institute; models from Canada, Japan, Europe and most U.S. research labs lacked explanatory power, either alone or in combination.
The study has important implications for policy-makers, McKitrick said. “Computer forecasts of regional climate changes between now and 2030 can look impressive in their detail, but it would be wise not to make major policy decisions without first looking into the model’s forecast accuracy.”
The findings are also important for researchers, especially those using climate data sets. “A lot of the current thinking about the causes of climate change relies on the assumption that the effects of land surface modification due to economic growth patterns have been filtered out of temperature data sets. But this assumption is not true.”
McKitrick also posted an accessible description of the paper on his webpage:
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. 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 former in empirical work may be biasing analysis of the magnitude and causes of observed climate trends since 1979.