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arXiv 2306.09045physics.ao-phphysics.flu-dyn
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使用神经网络参数化海洋表面边界层中的垂向混合系数

Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks

Aakash Sane, Brandon G. Reichl, Alistair Adcroft, Laure Zanna

AI总结:

本文利用神经网络学习二阶矩闭合方案的涡动扩散率廓线,并稳定嵌入海洋环流模型,在保持守恒的同时减小混合层深度和上层海洋层结偏差。

摘要:

海洋模型中的垂向混合参数化是根据控制湍流混合的物理原理构建的。然而,许多参数化包含一些缺乏理论或数据良好约束的特设成分。其中一个成分是涡动扩散率模型,其中某一物理量的垂向湍流通量通过可变的涡动扩散系数以及该物理量的平均垂向梯度进行参数化。在本研究中,我们通过使用数据驱动方法(具体为神经网络)增强其涡动扩散率模型,改进了海洋表面边界层中垂向混合的参数化。神经网络被设计为以外在和内在强迫参数作为输入来预测涡动扩散率廓线,并使用来自二阶矩闭合湍流混合方案的输出数据进行训练。改进后的垂向混合方案通过神经网络的在线推断来预测涡动扩散率廓线,并保持标准海洋模型方程的守恒原理,这对于其在气候模拟中的目标应用尤为重要。我们描述了神经网络在一个海洋通用环流模型中的开发与稳定实现,并证明增强后的方案通过减小混合层深度和上层海洋层结中的偏差,表现优于其前身。我们的结果证明了数据驱动且具备物理意识的参数化在改进全球气候模型方面的潜力。

英文摘要:

Vertical mixing parameterizations in ocean models are formulated on the basis of the physical principles that govern turbulent mixing. However, many parameterizations include ad hoc components that are not well constrained by theory or data. One such component is the eddy diffusivity model, where vertical turbulent fluxes of a quantity are parameterized from a variable eddy diffusion coefficient and the mean vertical gradient of the quantity. In this work, we improve a parameterization of vertical mixing in the ocean surface boundary layer by enhancing its eddy diffusivity model using data-driven methods, specifically neural networks. The neural networks are designed to take extrinsic and intrinsic forcing parameters as input to predict the eddy diffusivity profile and are trained using output data from a second moment closure turbulent mixing scheme. The modified vertical mixing scheme predicts the eddy diffusivity profile through online inference of neural networks and maintains the conservation principles of the standard ocean model equations, which is particularly important for its targeted use in climate simulations. We describe the development and stable implementation of neural networks in an ocean general circulation model and demonstrate that the enhanced scheme outperforms its predecessor by reducing biases in the mixed-layer depth and upper ocean stratification. Our results demonstrate the potential for data-driven physics-aware parameterizations to improve global climate models.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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