Study Reveals Need for Better Modeling of Weather Systems for Climate Prediction

Article ID: 682757

Released: 11-Oct-2017 4:05 PM EDT

Source Newsroom: Stony Brook University

Newswise — Stony Brook, NY, October 11, 2017 – Computer-generated models are essential for or scientists to predict the nature and magnitude of weather systems, including their changes and patterns. Using 19 climate models, a team of researchers led by Professor Minghua Zhang of the School of Marine and Atmospheric Sciences at Stony Brook University, discovered persistent dry and warm biases of simulated climate over the region of the Southern Great Plain in the central U.S. that was caused by poor modeling of atmospheric convective systems – the vertical transport of heat and moisture in the atmosphere. Their findings, to be published in Nature Communications, call for better calculations in global climate models.  

The climate models analyzed in the paper “Causes of model dry and warm bias over central U.S. and impact on climate projections,” included a precipitation deficit that is associated with widespread failure of the models in capturing actual strong rainfall events in summer over the region. By correcting for the biases, the authors found that future changes of precipitation over the US Southern Great Plain by the end of the 21st Century would be nearly neutral. This projection is unlike what has been predicted as a drying period by the majority of current climate models. The correction also reduces the projected warming of the region by 20 percent relative to projections of previous climate models.

“Current climate models are limited by available computing powers even when cutting-edge supercomputers are used,” said Professor Zhang. “As a result, some atmospheric circulations systems cannot be resolved by these models, and this clearly impacts the accuracy of climate change predictions as shown in our study.”

Professor Zhang and colleagues believe climate models will become more accurate in the coming years with the use of exsascale supercomputing, now in development worldwide.

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