Parameterizations of unresolved turbulent processes often compromise the fidelity of large‐scale ocean models. In this work, we argue for a Bayesian approach to the refinement and evaluation of turbulence parameterizations. Using an ensemble of large eddy simulations of turbulent penetrative convection in the surface boundary layer, we demonstrate the method by estimating the uncertainty of parameters in the convective limit of the popular “K‐Profile Parameterization.” We uncover structural deficiencies and propose an alternative scaling that overcomes them.

中文翻译：

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海洋参数化的不确定性量化：在穿透对流K型剖面参数化中的应用

未解决的湍流过程的参数化通常会损害大型海洋模型的保真度。在这项工作中，我们主张采用贝叶斯方法来优化和评估湍流参数化。使用表面边界层中湍流穿透对流的大涡模拟的集合，我们通过估计流行的“ K轮廓参数化”对流范围内参数的不确定性来演示该方法。我们发现了结构缺陷，并提出了克服这些缺陷的替代方法。