Gregory LeClaire Wagner, Adeline Hillier, Navid C. Constantinou, Simone Silvestri, Andre Souza, Keaton J. Burns, Chris Hill, Jean-Michel Campin, John Marshall, Raffaele Ferrari
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引用次数: 0
摘要
我们介绍了 CATKE,它是对 1 米到 100 米尺度的小尺度或 "微尺度 "海洋湍流混合相关通量的参数化。CATKE 采用下梯度公式,取决于预报性湍流动能(TKE)变量和诊断性混合长度尺度,其中包括动态对流调节(CA)成分。利用动态对流混合长度,CATKE 不仅能预测对流羽流的深度,还能预测对流混合的特征时间尺度,这是较简单的静态 CA 方案无法捕捉到的湍流对流的一个重要方面。因此,CATKE 可以描述对流与剪切力驱动的混合和气压限制等其他过程之间的竞争。为了校准 CATKE,我们使用集合卡尔曼反演(Ensemble Kalman Inversion)来最小化 21 个大型漩涡模拟(LES)与 CATKE 参数化单气柱模拟在三个不同垂直分辨率下对 LES 数据预测之间的误差。我们发现,与气候模式中常用的微尺度湍流参数化相比,CATKE 对理想化和现实的 LES 都能做出准确的预测。
Formulation and Calibration of CATKE, a One-Equation Parameterization for Microscale Ocean Mixing
We describe CATKE, a parameterization for fluxes associated with small-scale or “microscale” ocean turbulent mixing on scales between 1 and 100 m. CATKE uses a downgradient formulation that depends on a prognostic turbulent kinetic energy (TKE) variable and a diagnostic mixing length scale that includes a dynamic convective adjustment (CA) component. With its dynamic convective mixing length, CATKE predicts not just the depth spanned by convective plumes but also the characteristic convective mixing timescale, an important aspect of turbulent convection not captured by simpler static CA schemes. As a result, CATKE can describe the competition between convection and other processes such as shear-driven mixing and baroclinic restratification. To calibrate CATKE, we use Ensemble Kalman Inversion to minimize the error between 21 large eddy simulations (LESs) and predictions of the LES data by CATKE-parameterized single column simulations at three different vertical resolutions. We find that CATKE makes accurate predictions of both idealized and realistic LES compared to microscale turbulence parameterizations commonly used in climate models.
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