Climate change models omit key component

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.”

 

Originally published on June 20, 2012 by the University of Guelph. Ross McKitrick speaks at the What Matters Now event in Thunder Bay on March 4.

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