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On data-driven induction of the low-frequency variability in a coarse-resolution ocean model
Ocean Modelling ( IF 3.2 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.ocemod.2020.101664
E.A. Ryzhov , D. Kondrashov , N. Agarwal , J.C. McWilliams , P. Berloff

Abstract This study makes progress towards a data-driven parameterization for mesoscale oceanic eddies. To demonstrate the concept and reveal accompanying caveats, we aimed at replacing a computationally expensive, standard high-resolution ocean model with its inexpensive low-resolution analogue augmented by the parameterization. We considered eddy-resolving and non-eddy-resolving double-gyre ocean circulation models characterized by drastically different solutions due to the nonlinear mesoscale eddy effects. The key step of the proposed approach is to extract from the high-resolution reference solution its eddy field varying in space and time, and then to use this information to improve the low-resolution analogue model. By interactively coupling both the continuously supplied history of the eddy field and the explicitly modeled low-resolution large-scale flow, we obtained the additional eddy forcing term which modified the low-resolution model and significantly augmented its solutions. This eddy forcing term represents the action of the eddy field, its coupling with the large-scale flow and is a key dynamical constraint imposed on the augmentation procedure. Although the augmentation drastically improved the low-resolution circulation patterns, it did not recover the robust, intrinsic, large-scale low-frequency variability (LFV), which is an important feature of the high-resolution solution. This is by itself an important (negative) result that has significant implication for any data-driven eddy parameterization, especially, given the fact that we used the most complete information about the space–time history of the eddy fields. Note, when we supplied the reference (true) eddy forcing, rather than just the eddy field, the LFV was recovered. This suggests that the LFV is crucially dependent on the details of the space–time eddy forcing/large-scale flow correlations, which are not fully respected by the proposed augmentation procedure. In order to overcome the deficiency and recover the LFV, we statistically filtered the augmented low-resolution model solution by projecting it onto the leading Empirical Orthogonal Functions (EOFs) of the large-scale component of the high-resolution reference solution. This operation allowed us to remove spurious effects associated with higher EOFs. We tested and confirmed that without using the data-driven eddy information this filtering alone cannot augment the low-resolution solution; but in conjunction with the eddy information, it produced desirable outcome. Moreover, as a natural step towards parameterization, we took advantage of data-driven stochastic inverse modeling to obtain inexpensive emulators of the eddy field and showed generally promising results of augmenting the coarse-resolution model with the obtained emulators. Our results showed that obtaining the LFV characteristics for the eddy parameterization, which is already capable of reproducing the large-scale flow pattern, should become a standard parameterization requirement, but it can be challenging to meet.

中文翻译:

粗分辨率海洋模式低频变化的数据驱动归纳

摘要 这项研究在数据驱动的中尺度海洋涡旋参数化方面取得了进展。为了演示这个概念并揭示伴随的警告,我们的目标是用参数化增强的廉价低分辨率模拟代替计算成本高昂的标准高分辨率海洋模型。我们考虑了涡旋分辨和非涡旋分辨双环流海洋环流模型,其特征是由于非线性中尺度涡流效应而具有截然不同的解决方案。所提出方法的关键步骤是从高分辨率参考解中提取其在空间和时间上变化的涡场,然后利用这些信息改进低分辨率模拟模型。通过交互耦合连续提供的涡场历史和明确建模的低分辨率大规模流,我们获得了额外的涡强迫项,它修改了低分辨率模型并显着增加了其解。这个涡强迫项代表涡场的作用,它与大尺度流动的耦合,是强加于增强过程的关键动力学约束。尽管增强极大地改善了低分辨率环流模式,但它并没有恢复强大的、内在的、大规模的低频变化(LFV),这是高分辨率解决方案的一个重要特征。这本身就是一个重要的(负面)结果,对任何数据驱动的涡流参数化都有重要意义,尤其是,鉴于我们使用了关于涡流场时空历史的最完整信息。请注意,当我们提供参考(真实)涡强迫而不仅仅是涡场时,LFV 被恢复。这表明 LFV 关键依赖于时空涡强迫/大规模流动相关性的细节,而这些细节并没有被提议的增强程序完全考虑。为了克服缺陷并恢复 LFV,我们通过将增强的低分辨率模型解决方案投影到高分辨率参考解决方案的大规模组件的领先经验正交函数 (EOF) 上,对增强的低分辨率模型解决方案进行了统计过滤。此操作使我们能够消除与更高 EOF 相关的虚假效应。我们测试并确认,如果不使用数据驱动的涡流信息,仅靠这种过滤无法增强低分辨率解决方案;但结合涡流信息,它产生了理想的结果。此外,作为参数化的自然步骤,我们利用数据驱动的随机逆建模来获得涡场的廉价模拟器,并显示出用获得的模拟器增强粗分辨率模型的普遍有希望的结果。我们的结果表明,获得涡流参数化的 LFV 特性(已经能够再现大规模流型)应该成为标准的参数化要求,但要满足它可能具有挑战性。它产生了理想的结果。此外,作为参数化的自然步骤,我们利用数据驱动的随机逆建模来获得涡场的廉价模拟器,并显示出用获得的模拟器增强粗分辨率模型的普遍有希望的结果。我们的结果表明,获得涡流参数化的 LFV 特性(已经能够再现大规模流型)应该成为标准的参数化要求,但要满足它可能具有挑战性。它产生了理想的结果。此外,作为参数化的自然步骤,我们利用数据驱动的随机逆建模来获得涡场的廉价模拟器,并显示出用获得的模拟器增强粗分辨率模型的普遍有希望的结果。我们的结果表明,获得涡流参数化的 LFV 特性(已经能够再现大规模流型)应该成为标准的参数化要求,但要满足它可能具有挑战性。
更新日期:2020-09-01
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