UAlbany Atmospheric Scientists Advance Weather and Climate Prediction Research

ALBANY, N.Y. (September 22, 2015) -- As governments, scientists and policymakers grapple with the long-term impact of climate change, researchers at the University at Albany are seeking to improve the prediction models utilized to study global warming.

Jiping Liu, assistant professor in the Department of Atmospheric and Environmental Sciences, has received $276,577 from the National Oceanic and Atmospheric Administration (NOAA) to improve the seasonal predictability of Arctic sea ice in climate forecast systems. The potential climate, ecological, economic (e.g. shipping routes and fossil fuel resources), and geopolitical and military impacts of seasonal sea ice prediction need increasing efforts to improve the treatment of sea ice in climate forecast systems that is critical to predicting the impact of global warming on the Arctic.

While some models currently exist to capture seasonal cycles and trends of Arctic sea ice, the predicted sea ice is off by a large margin compared to observations according to Liu. "A major cause of the bias is a lack of observations of sea ice thickness over broad areas of the Arctic that would aid in the forecast procedures," he said.

UAlbany atmospheric scientists Jiping Liu, left and Robert Fovell (Photos by Mark Schmidt)

Liu and his team proposes to advance understanding of Arctic sea ice interactions – namely how radiative interactions among the atmosphere, sea ice and ocean all play a role in sea ice variability. "This serves as an important incremental step toward achieving an improved operational prediction system, enhancing prediction of northern mid-latitude weather and climate associated with rapid decline of Arctic sea ice," said Liu.

'Big Data' Infrastructure

Professor Robert Fovell of the Department of Atmospheric and Environmental Sciences is also spearheading an initiative to improve prediction models. Fovell and colleague Kevin Tyle have received $246,111 from the National Science Foundation (NSF) to establish a common and sustainable ‘Big Data’ infrastructure in support of weather prediction research and education at Universities across the United States. The scientists will lead a team of researchers who are looking into the predictability of mesoscale convective systems (MCSs) – a complex of thunderstorms that becomes organized and on a larger scale than individual storms, and can persist for several hours or more.

"The intense surface winds and heavy rainfall that accompany MCSs routinely pose significant impacts to both property and life across the United States," said Fovell. "The ability to mitigate these impacts through the development of more accurate, longer-lead forecasts of MCSs hinges upon our ability to better understand the intrinsic controls upon their predictability.  That effort, however, requires an enormous ensemble of model simulations, which in turn necessitates new ideas about how data will be created and shared.  This multi-institutional project will also tackle those challenges."

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