P32 Predictions of lake processes and their effects on precipitation through dynamical downscaling with WRF for the Great Lakes

Zhang, Baoqing, Jiming Jin, and Jonathan Meyer, Utah State University

In this study, we used the Weather Research and Forecasting (WRF) model coupled with a one-dimensional and physically-based lake scheme to predict 1) lake surface temperature (LST) 2) lake ice cover (LIC) and 3) lake-effect precipitation for the Great Lakes over the winter time. Ten-year simulations forced with the North American Regional Reanalysis data were performed at 10 km grid-spacing for the winter months from 2000 through 2010. The results show that the coupled WRF-Lake model is able to reproduce the observed LST and LIC in the Great Lakes and precipitation over the surrounding areas affected by lake processes, indicating that the coupled model can realistically resolve lake and atmospheric processes and their interactions. The bias-corrected global Community Climate System Model output through regression with the reanalysis data was then used to drive the WRF-Lake model to predict LST, LIC, and precipitation in the Great Lakes region for the period of 2090-2099. These predictions were compared with the historical simulations to better understand how global climate changes affect lake-related processes and phenomena. Our results indicate the importance of realistic lake-processes in simulations predicting the weather and climate for the Great Lakes region.