Scale-Aware Space-Time Stochastic Parameterization of Subgrid-Scale Velocity Enhancement of Sea Surface Fluxes
Scale-Aware Space-Time Stochastic Parameterization of Subgrid-Scale Velocity Enhancement of Sea Surface Fluxes
复制标题
海面通量亚网格尺度速度增强的尺度感知时空随机参数化
DOI：
10.1029/2020ms002367
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发表时间：
2021
影响因子：
6.8
通讯作者：
Bessac J
中科院分区：
文献类型：
--
作者：
Bessac J
亚网格尺度对天气和气候模型中解析尺度的影响的随机表示已被证明可以改善集合传播和解析变异性。我们提出了一种统计尺度感知时空模型来表征中尺度风变率对海气交换的贡献。在早期的研究中，我们分析了通过高分辨率模拟计算出的“真实”通量与通过粗粒度获得的“解析”通量之间的差异。这种差异在空间和时间上进行建模，以粗粒度风和降水场为条件，以参数化中尺度速度变化引起的通量增强。传统上，随机参数化模型是针对特定模型分辨率而开发的，没有适应模型分辨率的明确能力。我们提出了一种开发随机模型的方法，该模型以尺度感知的方式适应分辨率。尺度感知参数化是根据系统粗粒度高分辨率数值模型输出的经验结果开发的。统计模型是根据三种不同粗化分辨率的数值模型输出拟合的。从这种尺度感知参数化中，我们通过任意分辨率的亚网格速度变化推导出通量增强的随机参数化，并表征了跨模型分辨率通量增强的条件分布和时空结构。
Stochastic representation of the influence of the subgrid‐scales on the resolved scales in weather and climate models has been shown to improve ensemble spread and resolved variability. We propose a statistical scale‐aware space‐time model to characterize the contribution of mesoscale wind variability to air‐sea exchanges. In an earlier study, we analyzed the difference between “true” fluxes computed from a high resolution simulation and “resolved” fluxes obtained by coarse graining. This discrepancy is modeled in space and time, conditioned on the coarse‐grained wind and precipitation fields, to parameterize the enhancement of fluxes by mesoscale velocity variations. Stochastic parameterization models have traditionally been developed for particular model resolutions without the explicit capability to adapt to model resolution. We present an approach to develop stochastic models that adapt to resolution in a scale‐aware fashion. The scale‐aware parameterization is developed from empirical results for systematically coarse‐grained high‐resolution numerical model output. The statistical model is fit from numerical model output at three different coarsening resolutions. From this scale‐aware parameterization, we derive a stochastic parameterization of flux enhancement by subgrid velocity variations for arbitrary resolutions and characterize the conditional distributions and space‐time structures of the flux enhancement across model resolutions.