By Rob Mitchum // March 14, 2016
It’s a common misconception that climate models work like weather models, capable of forecasting temperature, precipitation and other elements at the same scale as your ten o’clock weatherman’s green screen. But climate models must deal with much more data, often making predictions about the entire globe for hundreds of years into the future. As a result, the resolution of climate models is much lower, with researchers chopping the Earth up into a grid of large polygons in order to run simulations in reasonable time. While these models provide valuable information about the future of our planet, the resolution constrains the conclusions that can be drawn about smaller regions, such as individual cities, or regions with complex geography.
But last year, CI senior fellow V. Rao Kotamarthi and Argonne postdoctoral researcher Jiali Wang pushed the resolution limits of climate modeling further than ever before, completing the highest resolution forecast of North America ever conducted. An article from Argonne describes the work, which simulated 100 years of climate using grids of just seven miles on each side — up to ten times more detailed than standard models.
The new forecast, writes Argonne’s Louise Lerner, offers important new information about extreme weather and seasonal features.
The model was also better at predicting seasonal features, like Southwestern monsoons. The simulation predicted less rain over the Southwest but more on the eastern seaboard and much of Canada. These effects intensify later in the century.
“By far the largest uncertainty in a climate model is the water cycle,” said Wang, an Argonne postdoctoral researcher. According to Wang, a higher resolution model targets that issue. Scientists noticed a bias in their models that always seemed to make the Northern Great Plains wetter than it actually was; the higher resolution reduced the bias, and preliminary results for an upcoming even higher resolution run lower it by nearly a third, she said.
In addition, the data itself has a multitude of uses. For example, regional and city planners want to know how their local climates might change, so they can build roads to withstand more flooding or plant street trees that can handle more heat. The tighter resolution can help provide those regional predictions.
Other teams are already using the dataset to analyze particular areas: a group with Purdue University is modeling the agricultural impact on Midwest corn and soybean crops, for example, and University of Chicago researcher Colin Kyle is using the data to study how the range of a fungal pathogen that kills invasive gypsy moths might expand or contract in the future.