Diagnosing, modeling, and testing a multiplicative stochastic Gent-McWilliams parameterization
Diagnosing, modeling, and testing a multiplicative stochastic Gent-McWilliams parameterization
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DOI：
10.1016/j.ocemod.2018.10.009
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发表时间：
2019
期刊：
影响因子：
3.2
通讯作者：
I. Grooms;W. Kleiber
中科院分区：
文献类型：
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作者：
I. Grooms;W. Kleiber
通过理想化的涡分辨原始方程模拟，诊断出一个与深度无关的各向同性Gent-McWilliams（GM）输运参数κ。最佳的深度无关的各向同性GM参数化只能模拟不到50%的诊断总趋势的温度引起的未解决的中尺度涡。基于诊断值建立了GM参数的时空随机模型，并利用图形套索估计了空间相关结构。随机模型在低分辨率模式模拟中用作随机参数化。低分辨率随机模拟在再现大尺度温度的时间平均值方面做得很差。确定性GM参数化和乘性随机GM参数化与不切实际的结构导致显着更准确的大尺度温度在低分辨率的模拟。这些结果表明，无论是深度的独立性或各向同性的GM参数化是不现实的模型的涡流示踪剂运输，或随机GM参数化应包括一个附加组件。
A depth-independent isotropic Gent-McWilliams (GM) transport parameterκis diagnosed from an idealized eddy-resolving primitive equation simulation. The optimal depth-independent isotropic GM parameterization is only able to model less than 50% of the diagnosed total tendency of temperature induced by unresolved mesoscale eddies. A spatio-temporal stochastic model of the GM parameter is developed based on the diagnosed values; the graphical lasso is used to estimate the spatial correlation structure. The stochastic model is used as a stochastic parameterization in low-resolution model simulations. The low-resolution stochastic simulation does a poor job of reproducing the temporal mean of large-scale temperature. Deterministic GM parameterizations and multiplicative stochastic GM parameterizations with unrealistic structure result in significantly more-accurate large-scale temperature in the low-resolution simulations. These results suggest that either the depth-independence or the isotropy of the GM parameterization are unrealistic as models of the eddy tracer transport, or that a stochastic GM parameterization should include an additive component.