Constraining stochastic parametrisation schemes using high‐resolution simulations
Constraining stochastic parametrisation schemes using high‐resolution simulations
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DOI：
10.1002/qj.3717
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发表时间：
2019-04
影响因子：
8.9
通讯作者：
H. Christensen
中科院分区：
文献类型：
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作者：
H. Christensen
随机参数化可用于天气和气候模式，以改善不可预测的未解决过程的表示。与确定性模型相比，随机模型代表了“模型的不确定性”，即由于预测模型的局限性而导致预测误差的来源。提出了一种系统地导出新的随机参数或约束现有随机方法的技术。一个高分辨率的模型模拟是粗粒度到所需的预测模型分辨率。这提供了驱动单列模型（SCM）所需的初始条件和强制数据。将SCM参数化趋势与高分辨率模型的演变进行比较，可以估计随机参数化试图表示的SCM趋势中的误差。这种方法被用来评估广泛使用的随机摄动参数化趋势（SPPT）方案的物理基础。为SPPT的乘法性质找到了理由，以及使用时空相关随机扰动的一些证据。有证据表明随机扰动应该是正偏斜的，这表明偶尔的大量级正扰动在物理上是现实的。然而，SPPT的其他关键假设不太合理，包括随机扰动与高度的一致性，不同物理参数化方案的扰动的一致性，以及不同预测变量的一致性。放松这些SPPT假设允许一个错误模型，它可以解释比传统SPPT更大的分数方差。特别地，建议独立地扰动与不同参数化方案相关的趋势是合理的，并将提高SPPT方法的现实性。
Stochastic parametrisations can be used in weather and climate models to improve the representation of unpredictable unresolved processes. When compared with a deterministic model, a stochastic model represents “model uncertainty”, that is, sources of error in the forecast due to the limitations of the forecast model. A technique is presented for systematically deriving new stochastic parametrisations or constraining existing stochastic approaches. A high‐resolution model simulation is coarse‐grained to the desired forecast model resolution. This provides the initial conditions and forcing data needed to drive a single‐column model (SCM). Comparing the SCM parametrised tendencies with the evolution of the high‐resolution model provides an estimate of the error in the SCM tendencies that a stochastic parametrisation seeks to represent. This approach is used to assess the physical basis of the widely used stochastically perturbed parametrisation tendencies (SPPT) scheme. Justification is found for the multiplicative nature of SPPT, along with some evidence for the use of spatio‐temporally correlated stochastic perturbations. Evidence that the stochastic perturbation should be positively skewed is found, indicating that occasional large‐magnitude positive perturbations are physically realistic. However, other key assumptions of SPPT are less well justified, including coherency of the stochastic perturbations with height, coherency of the perturbations for different physical parametrisation schemes, and coherency for different prognostic variables. Relaxing these SPPT assumptions allows for an error model that explains a larger fractional variance than traditional SPPT. In particular, it is suggested that independently perturbing the tendencies associated with different parametrisation schemes is justifiable and would improve the realism of the SPPT approach.