Document Type

Article

Publication Date

2017

Abstract

Accurate representations of lake–ice–atmosphere interactions in regional climate modeling remain one of the most critical and unresolved issues for understanding large-lake ecosystems and their watersheds. To date, the representation of the Great Lakes two-way interactions in regional climate models is achieved with one-dimensional (1D) lake models applied at the atmospheric model lake grid points distributed spatially across a 2D domain. While some progress has been made in refining 1D lake model processes, such models are fundamentally incapable of realistically resolving a number of physical processes in the Great Lakes. In this study, a two-way coupled 3D lake-ice–climate modeling system [Great Lakes–Atmosphere Regional Model (GLARM)] is developed to improve the simulation of large lakes in regional climate models and accurately resolve the hydroclimatic interactions. Model results are compared to a wide variety of observational data and demonstrate the unique skill of the coupled 3D modeling system in reproducing trends and variability in the Great Lakes regional climate, as well as in capturing the physical characteristics of the Great Lakes by fully resolving the lake hydrodynamics. Simulations of the climatology and spatiotemporal variability of lake thermal structure and ice are significantly improved over previous coupled, 1D simulations. At seasonal and annual time scales, differences inmodel results are primarily observed for variables that are directly affected by lake surface temperature (e.g., evaporation, precipitation, sensible heat flux) while no significant differences are found in other atmospheric variables (e.g., solar radiation, cloud cover). Underlying physical mechanisms for the simulation improvements using GLARM are also discussed.

Recommended Citation

Xue Pengfei, et al. “Improving the Simulation of Large Lakes in Regional Climate Modeling : Two-Way Lake–Atmosphere Coupling with a 3D Hydrodynamic Model of the Great Lakes.” Journal of Climate, vol. 30, no. 5, Mar. 2017, pp. 1605–1627. DOI: 10.1175/JCLI-D-16-0225.1

Share

COinS