Exploring the use of machine learning to parameterize vertical mixing in the ocean surface boundary layer
Exploring the use of machine learning to parameterize vertical mixing in the ocean surface boundary layer
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DOI：
10.1016/j.ocemod.2022.102059
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发表时间：
2022-06
期刊：
影响因子：
3.2
通讯作者：
Jun‐Hong Liang;Jian Yuan;X. Wan;Jinliang Liu;Bingqing Liu;H. Jang;M. Tyagi
中科院分区：
文献类型：
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作者：
Jun‐Hong Liang;Jian Yuan;X. Wan;Jinliang Liu;Bingqing Liu;H. Jang;M. Tyagi
在海洋和气候模型中，上层海洋温度和盐度的模拟取决于海洋表面边界层湍流的混合参数化。现有的混合参数化基于具有经验参数的物理原理。然而，它们仍然不完善，导致对上层海洋物理状态的模拟存在偏差。在这项研究中，我们探索使用基于数据的机器学习技术，特别是深度神经网络模型，来研究海洋表面边界层垂直混合的影响。该模型使用面向过程的上层海洋湍流模拟进行训练，该湍流由中纬度海洋气候站帕帕海洋站的真实强迫条件驱动。深度神经网络模型优于传统的基于物理的参数化，后者使用确定性公式将混合效果与表面强迫联系起来。深度神经网络模型还用于探索基于物理的混合参数化开发中当前有争议的两个问题，包括波浪强迫的表示和强迫条件的历史。
In ocean and climate models, the simulation of upper-ocean temperature and salinity depends on mixing parameterizations for ocean surface boundary layer turbulence. Existing mixing parameterizations are based on physical principles with empirical parameters. However, they are still imperfect, leading to biases in the simulation of physical states in the upper ocean. In this study, we explore the use of the data-based machine learning technique, specifically, a deep neural network model, for the effects of vertical mixing in the ocean surface boundary layer. The model is trained using process-oriented simulations of the upper-ocean turbulence driven by realistic forcing conditions at the Ocean Station Papa that is a mid-latitude ocean climate station. The deep neural network model outperforms traditional physics-based parameterizations that relate the mixing effects to surface forcing using deterministic formulas. The deep neural network model is also used to explore two currently debated issues in the development of physics-based mixing parameterizations, including the representation of wave forcing and the history of forcing conditions.