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(Rain) cloud computing for climate change predictions

Speed read
  • Scientists set a new record for forecast simulation resolution — from 30 miles to seven.
  • Sharper forecasts aid in future climate predictions.
  • Land managers gain insight with more precise regional forecasts.

Rao Kotamarthi and Jiali Wang spend their days looking at the future.

They're not using a crystal ball — instead, the two scientists work on simulations and techniques at the US Department of Energy’s (DOE’s) Argonne National Laboratory to project what the climate will look like 100 years from now.

Last year, dividing the continent into squares just over seven miles (11 km) on a side, they completed the highest resolution climate forecast ever done for North America — far more detailed than the standard 30 to 60 miles (48 to 96 km).

<strong>All wet. </strong> Argonne scientists evaluated weather research and forecasting model performance for historical simulation and future projections. A comparison of seasonal mean precipitation during the warm (April to September) and cold (October to March) seasons during 1995–2004 is pictured. Courtesy Rao Kotamarthi and Jiali Wang.

Like expanding a video file to contain every minute of your life instead of just every birthday, adding more resolution to climate models is computationally intensive. But the added accuracy is worth the extra computational cost, they say.

“In particular, places with sharp terrain changes, like the Rockies, saw big improvements,” says Kotamarthi, who heads the department of atmospheric science and climate in Argonne’s Environmental Science Division.

Their new method, designed specifically for looking at chunks of the model rather than the entire US, was better at predicting days with extreme heat than conventional techniques. In all, Kotamarthi says, the model’s bias — found by having the model ‘predict’ the climate for the past 30 years and then comparing it with the actual recorded weather — was much improved over a lower resolution model.

The simulation predicted less rain over the Southwest, but more on the eastern seaboard and much of Canada, with effects intensifying 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, Wang says.

Regional and city planners will find much practical benefit from the added data. Knowledge of how their local climates might change will help them decide whether to build roads that withstand more flooding or plant street trees that can handle more heat. The tighter resolution can help provide those regional predictions.

Another practical benefit was found by a Purdue University group modeling the agricultural impact on Midwest corn and soybean crops. 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.

The 100+ TB dataset will be online shortly for anyone to download, Wang and Kotamarthi said.

Adding more resolution to climate models is like expanding a video file to contain every minute of your life instead of just every birthday.

In a more recent study published in Climate Dynamics with researchers from University of Chicago and Purdue, Wang and Kotamarthi explored a new method for calculating the likelihood of extreme weather events. Extreme events, such as severe thunderstorms or number of days with extreme heat, represent a serious threat of climate change — the worst storms will cause the most damage. But because models with large grid sizes are smoothed out over large spaces and time, there’s been suspicion that they aren’t good at predicting infrequent events like disastrous storms.

Their new method, designed specifically for looking at chunks of the model rather than the entire United States, was better at predicting days with extreme heat than conventional techniques, and should add accuracy for other extremes, like precipitation, as well.

Next, Kotamarthi and Wang aim to improve their models’ resolution even further. “For our next task, we want to tackle a two-and-a-half mile resolution,” Kotamarthi said. “This is small enough to capture physical phenomena, like convection in the atmosphere.”

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