New methodology improves winter climate forecasting

North Carolina State University News: It’s hot out right now, but new research from North Carolina State University will help us know what to expect when the weather turns cold. Researchers have developed a new methodology that improves the accuracy of winter precipitation and temperature forecasts. The tool should be valuable for government and utility officials, since it provides key information for use in predicting energy consumption and water availability.

“Predicting winter precipitation is extremely useful, because winter is the most important season in terms of re-charging water supplies in the United States, ensuring water will be available in the summer,” says Dr. Sankar Arumugam, author of the study and an assistant professor of civil, construction and environmental engineering at NC State. The study was co-authored by Naresh Devineni, a Ph.D. student at NC State.

Researchers were able to reduce uncertainty in winter climate predictions by developing a methodology that incorporates multiple climate forecast models and also accounts for the activity of El Nino conditions in the Pacific. “Predicting temperature is also important, because temperature determines energy consumption,” Arumugam says. “When it is very cold, people use more energy to heat their homes.”

The researchers were able to reduce uncertainty in winter climate predictions over the United States by developing a methodology that incorporates multiple general climate forecast models (GCMs) and also accounts for the activity – or inactivity – of El Nino conditions in the Pacific.

…Most GCMs are better at predicting the winter climate when ENSO is quite active, and are less accurate under neutral ENSO conditions. The methodology developed by the researchers accounts for the skill of the models under active and neutral ENSO conditions in combining multiple GCMs, resulting in reduced uncertainty in predicting the winter climate….

Sunset in Kuznetsk Alatau, South Siberia, a beautiful shot by Dmitry A. Mottl, Wikimedia Commons, under the Creative Commons Attribution-Share Alike 3.0 Unported license