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首页> 外文期刊>Journal of the Atmospheric Sciences >Stochastic behavior of tropical convection in observations and a multicloud model
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Stochastic behavior of tropical convection in observations and a multicloud model

机译：热带对流的随机观测和多云模型

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摘要

The aim for a more accurate representation of tropical convection in global circulation models is a long-standing issue. Here, the relationships between large and convective scales in observations and a stochastic multicloud model (SMCM) to ultimately support the design of a novel convection parameterization with stochastic elements are investigated. Observations of tropical convection obtained at Darwin and Kwajalein are used here. It is found that the variability of observed tropical convection generally decreases with increasing large-scale forcing, implying a transition from stochastic to more deterministic behavior with increasing forcing. Convection shows a more systematic relationship with measures related to large-scale convergence compared to measures related to energetics (e.g., CAPE). Using the observations, the parameters in the SMCM are adjusted. Then, the SMCM is forced with the time series of the observed large-scale state and the simulated convective behavior is compared to that observed. It is found that the SMCM cloud fields compare better with observations when using predictors related to convergence rather than energetics. Furthermore, the underlying framework of the SMCM is able to reproduce the observed functional dependencies of convective variability on the imposed large-scale state-an encouraging result on the road toward a novel convection parameterization approach. However, establishing sound cause-and-effect relationships between tropical convection and the large-scale environment remains problematic and warrants further research.
机译：在全球环流模型中更准确地表示热带对流的目标是一个长期存在的问题。在此，研究了观测中的大尺度和对流尺度与随机多云模型（SMCM）之间的关系，以最终支持设计具有随机元素的新型对流参数化。这里使用了在达尔文和夸贾林的热带对流观测资料。研究发现，热带强迫对流的变化通常随着大规模强迫的增加而减小，这意味着随着强迫的增加，从随机行为向确定性行为的转变。与与能量学相关的度量（例如CAPE）相比，对流显示与大规模收敛的度量具有更系统的关系。使用观察值，可以调整SMCM中的参数。然后，以观察到的大尺度状态的时间序列强迫SMCM，并将模拟的对流行为与观察到的对流行为进行比较。结果发现，当使用与收敛有关的预测变量而不是与能量学相关的预测因子时，SMCM云场与观测值相比更好。此外，SMCM的基础框架能够重现所观察到的对流变率对所施加的大规模状态的函数依赖性，这是向新型对流参数化方法迈进的令人鼓舞的结果。然而，在热带对流和大规模环境之间建立合理的因果关系仍然是有问题的，值得进一步研究。

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