Abstract
In this study, we coupled the two-layer one-dimensional Freshwater lake (FLake) model into the Climate Forecast System (CFS) version 2 to improve simulations of the effects of the Great Lakes on winter climate forecasts in that region. We first incorporated global lake fraction and depth data into the coupled CFS-FLake through a subgridding system. An interface between the CFS and FLake was then developed to link the two models for energy and water flux exchanges. The lake scheme was triggered only if the lake fraction was more than 10% in one CFS grid cell. We conducted ensemble retrospective forecasts with CFS-FLake for the period of 1997 through 2016 with 9 monthly leads. These forecasts were assessed to obtain a better understanding of the role of the Great Lakes in the climate system for winter, when significant lake-effect precipitation often occurs. Our results indicate that forecasts of lake surface temperature (LST), lake ice coverage (LIC), and precipitation with CFS-FLake were consistently better than those with CFS. The major improvements resulted from changes in land use type for the Great Lakes, from ocean and land in CFS to lakes in CFS-FLake. With the change from ocean to lake, LST mostly decreased with increasing LIC, resulting in lower surface heat and water fluxes to the atmosphere during the winter. However, with the change from land to lake, LST increased, leading to higher heat and water fluxes. In the meantime, precipitation predicted by CFS-FLake was reduced quite significantly over the Great Lakes compared to that by CFS. This reduction was caused by suppressed rising motion due to increased stability in the lower atmosphere as a result of lowered surface heat and water fluxes. The results from this study indicate that local and mesoscale surface and atmospheric processes significantly affect regional climate forecasts, and the coupled CFS-FLake model will have a broader impact on climate and hydrology research and forecasts.
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Acknowledgements
This research was supported by NOAA MAPP-CTB (No. NA14OAR4310191), the National Natural Science Foundation of China (No. 91437219, No. 91637209, No. 41175005), and the Utah Agricultural Experiment Station and the National Science Foundation (No. 1603088). We thank Northwest Agriculture & Forestry University for providing us with high-performance computing resources. We thank ** Liu from Stony Brook University for helpful discussions. Finally, we thank two anonymous reviewers for their constructive comments and suggestions to improve the quality of this study.
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Lv, Z., Zhang, S., **, J. et al. Coupling of a physically based lake model into the climate forecast system to improve winter climate forecasts for the Great Lakes region. Clim Dyn 53, 6503–6517 (2019). https://doi.org/10.1007/s00382-019-04939-2
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DOI: https://doi.org/10.1007/s00382-019-04939-2