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Improving lake mixing process simulations in the Community Land Model by using iK/i?profile parameterization

机译:通过使用 k 改善社区土地模型中的湖泊混合过程模拟?配置文件参数化

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We improved lake mixing process simulations by applying a vertical mixing scheme, K?profile parameterization?(KPP), in the Community Land Model?(CLM) version?4.5, developed by the National Center for Atmospheric Research. Vertical mixing of the lake water column can significantly affect heat transfer and vertical temperature profiles. However, the current vertical mixing scheme in CLM requires an arbitrarily enlarged eddy diffusivity to enhance water mixing. The coupled CLM-KPP considers a boundary layer for eddy development, and in the lake interior water mixing is associated with internal wave activity and shear instability. We chose a lake in Arctic Alaska and a lake on the Tibetan Plateau to evaluate this improved lake model. Results demonstrated that CLM-KPP reproduced the observed lake mixing and significantly improved lake temperature simulations when compared to the original CLM. Our newly improved model better represents the transition between stratification and turnover. This improved lake model has great potential for reliable physical lake process predictions and better ecosystem services.
机译:我们通过应用垂直混合方案,k?概况参数化k?(kpp),在社区土地模型中改进湖混合过程模拟?(CLM)版本?4.5,由国家大气研究中心开发。湖水柱的垂直混合可以显着影响传热和垂直温度型材。然而,CLM中的当前垂直混合方案需要任意扩大的涡流扩大率以增强水混合。耦合的CLM-KPP考虑用于涡涡体的边界层,并且在湖内水混合中与内部波活性和剪切不稳定性相关。我们选择了北极阿拉斯加的湖泊和藏高原的湖,以评估这种改进的湖泊模式。结果表明,与原始CLM相比,CLM-KPP再现了观察到的湖泊混合,显着改善了湖泊温度模拟。我们的新改进的模型更好地代表了分层与营业额之间的过渡。这种改进的湖泊模式具有可靠的物理湖泊过程预测和更好的生态系统服务潜力。

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