Last update:3.30.05

PIs: Michael Stein, George Tiao, Donald Wuebbles, Katharine Hayhoe, Serge Guillas

Postdoctoral Fellow: Mathieu Vrac

Graduate Students: Anne Hertel and Airong Cai

The goal of this project is to explore the potential application of innovative statistical approaches to detection of regional climate change and future projections of change. The first focus of the current stage of this project is to employ the latest statistical techniques to analyze observed and model-simulated historical temperature and precipitation data, in order to identify the primary oscillatory influences on long-term climate over the Midwest; then develop and test a prediction model using an ensemble of naturally-forced and anthropogenic+ naturally-forced general circulation climate model simulations for the historical period, to determine whether GCM forecasts are useful yet for short-term projections on a regional scale. The second focus of the project is to apply innovative clustering techniques to atmospheric geopotential heights in order to identify dominant circulation patterns over the continental U.S., which can then be correlated with surface conditions, particularly extremes.