Improving a Multilevel Turbulence Closure Model for a Shallow Lake in Comparison With Other 1‐D Models
Improving a Multilevel Turbulence Closure Model for a Shallow Lake in Comparison With Other 1‐D Models
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DOI：
10.1029/2019ms001971
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发表时间：
2020-07
影响因子：
6.8
通讯作者：
Lei Sun;Xin‐Zhong Liang;Tiejun Ling;Min Xu;X. Lee
中科院分区：
文献类型：
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作者：
Lei Sun;Xin‐Zhong Liang;Tiejun Ling;Min Xu;X. Lee
湖泊在水的可用性、热容量、湿度和粗糙度方面与陆地不同，这些都会影响当地的地表-大气相互作用。本研究修改了一个用于湖泊应用的多层上层海洋模式（MM 5），并通过对三个流行的一维（1-D）湖泊模式的综合测量来评估其在太湖（中国）的性能。这些模型基于不同的概念，包括自相似性（Flake）、风驱动涡扩散（LISSS）、k-ε湍流闭合（SIMSTRAT）和简化湍流闭合（SIMSTRAT）。这些模式中的地表通量方案被统一，以排除代表气湖交换的差异。所有模型在其默认配方呈现出明显的冷水温度偏差，在很大程度上低估了湖面温度（LST）的昼夜变化。对于每一个模型，这些缺陷显着减少了新的物理方案或校准的可调参数的基础上系统的灵敏度测试。主要的修改包括：（1）一个新的计划，减少表面粗糙度的长度，以更好地表征浅水湖，（2）太阳辐射的穿透计划，增加消光系数和表面吸收分数，以解释高的水浊度，和（3）湍流普朗特数增加了20倍，以减少湍流垂直混合。所有其他模型都在这三个方面（粗糙度，消光和混合）在其原始配方中进行了改进。鉴于这些改进，在捕捉LST日周期和每日至季节变化以及夏秋季垂直分层变化方面，ESTA显示出优于其他模式的上级性能。新的潜水器非常适合在浅水湖泊中应用。
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.