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A stochastic scale?¢????aware parameterization of shallow cumulus convection across the convective gray zone

机译:随机尺度?对流灰色区域内浅积云对流的感知参数化

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The parameterization of shallow cumuli across a range of model grid resolutions of kilometre?¢????scales faces at least three major difficulties: (1) closure assumptions of conventional parameterization schemes are no longer valid, (2) stochastic fluctuations become substantial and increase with grid resolution, and (3) convective circulations that emerge on the model grids are under?¢????resolved and grid?¢????scale dependent. Here we develop a stochastic parameterization of shallow cumulus clouds to address the first two points, and we study how this stochastic parameterization interacts with the under?¢????resolved convective circulations in a convective case over the ocean. We couple a stochastic model based on a canonical ensemble of shallow cumuli to the Eddy?¢????Diffusivity Mass?¢????Flux parameterization in the icosahedral nonhydrostatic (ICON) model. The moist?¢????convective area fraction is perturbed by subsampling the distribution of subgrid convective states. These stochastic perturbations represent scale?¢????dependent fluctuations around the quasi?¢????equilibrium state of a shallow cumulus ensemble. The stochastic parameterization reproduces the average and higher order statistics of the shallow cumulus case adequately and converges to the reference statistics with increasing model resolution. The interaction of parameterizations with model dynamics, which is usually not considered when parameterizations are developed, causes a significant influence on convection in the gray zone. The stochastic parameterization interacts strongly with the model dynamics, which changes the regime and energetics of the convective flows compared to the deterministic simulations. As a result of this interaction, the emergence of convective circulations in combination with the stochastic parameterization can even be beneficial on the high?¢????resolution model grids.
机译:跨越千米尺度的模型网格分辨率范围内的浅积云的参数化面临至少三个主要困难:(1)传统参数化方案的封闭假设不再有效;(2)随机波动变得很大;以及随网格分辨率的增加而增加;(3)模型网格上出现的对流环流处于分辨力之下,并且与网格尺度无关。在这里,我们开发了浅积云的随机参数化以解决前两点,并且研究了在海洋对流情况下,这种随机参数化如何与欠解析的对流环流相互作用。我们将基于浅层积积规范集合的随机模型耦合到二十面体非静水压(ICON)模型中的涡流参数化扩散系数。通过对子网格对流状态的分布进行二次采样,可以扰动湿对流面积分数。这些随机扰动代表了浅积云集合的准平衡状态周围的尺度变化。随机参数化可充分再现浅积云情况的平均和高阶统计量,并随着模型分辨率的提高而收敛到参考统计量。参数化与模型动力学的相互作用(在开发参数化时通常不考虑)对灰色区域中的对流产生重大影响。随机参数化与模型动力学有很强的相互作用,与确定性仿真相比,这改变了对流的形式和能量。由于这种相互作用,对流循环的出现与随机参数化相结合甚至可以在高分辨率的模型网格上受益。

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