A Dynamical Downscaling Study in the Great Lakes Region Using the WRF/Lake Model: Historical Simulation and Future Projection

As the largest group of fresh water bodies on Earth, the Laurentian Great Lakes have significant influence on local and regional weather and climate through their unique physical features. Due to the limited spatial resolution and computational efficiency of general circulation models (GCMs), the Great Lakes are geometrically ignored or idealized into several grid cells in GCMs. Thus, the dynamical downscaling technique serves as a feasible solution. The Weather Research and Forecasting model (WRF) with an updated lake scheme is employed to conduct this study downscaled from CMIP5 models. It is a one-dimensional mass and energy balance scheme with 20-25 model layers, including up to 5 snow layers on the lake ice, 10 water layers, and 10 soil layers on the lake bottom, based on the actual lake points and lake depth. The preliminary results show that WRF-Lake model, with a fine horizontal resolution and realistic lake representation, provides significantly improved hydroclimate, in terms of lake surface temperature, annual cycle of precipitation, ice content, and lake-effect snowfall. Those improvements suggest that better resolution of the lakes and the mesoscale processes of lake-atmosphere interaction are crucial to understanding the climate and climate change in the Great Lakes region.