Improving a Multilevel Turbulence Closure Model for a Shallow Lake in Comparison With Other 1-D Models

Lei Sun, Xin Zhong Liang, Tiejun Ling, Min Xu, Xuhui Lee

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9 Scopus citations

Abstract

Lakes differ from lands in water availability, heat capacity, albedo, and roughness, which affect local surface-atmospheric interactions. This study modified a multilevel upper ocean model (UOM) for lake applications and evaluated its performance in Lake Taihu (China) with comprehensive measurements against three popular one-dimensional (1-D) lake models. These models were based on different concepts, including the self-similarity (FLake), the wind-driven eddy diffusion (LISSS), the k-ε turbulence closure (SIMSTRAT), and a simplified turbulence closure (UOM). The surface flux scheme in these models was unified to exclude the discrepancies in representing air-lake exchanges. All models in their default formulations presented obvious cold water temperature biases and largely underestimated the lake surface temperature (LST) diurnal range. For each model, these deficiencies were significantly reduced by incorporating new physics schemes or calibrated tunable parameters based on systematic sensitivity tests. The primary modifications for UOM included (1) a new scheme of decreased surface roughness lengths to better characterize the shallow lake, (2) a solar radiation penetration scheme with increased light extinction coefficient and surface absorption fraction to account for the high water turbidity, and (3) turbulent Prandtl number increased by a factor of 20 to reduce the turbulent vertical mixing. All other models were improved in these three aspects (roughness, extinction, and mixing) within their original formulations. Given these improvements, UOM showed superior performance to other models in capturing LST diurnal cycle and daily to seasonal variations, as well as summer-autumn vertical stratification changes. The new UOM is well suited for application in shallow lakes.

Original languageEnglish
Article numbere2019MS001971
JournalJournal of Advances in Modeling Earth Systems
Volume12
Issue number7
DOIs
StatePublished - Jul 1 2020

Funding

We acknowledge two anonymous reviewers for their constructive comments and suggestions for improving the manuscript. The research was supported by the National Key Research and Development Program of China grant to Ling (2016YFC1401400), the Jiangsu Institute of Meteorological Sciences subcontract (NJCAR2016ZD03), and the China Meteorological Administration/National Climate Center research subcontract (2211011816501) both to Nanjing University of Information Science and Technology, and the China Scholar Council fellowship to Sun for his visit at the University of Maryland. Liang was partially supported by U.S. National Science Foundation Innovations at the Nexus of Food, Energy and Water Systems under Grant EAR1903249. The simulations and analyses were conducted on supercomputers, including the Maryland Advanced Research Computing Center's Bluecrab and the Computational and Information Systems Lab of the National Center for Atmospheric Research.

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