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Ongoing Breakthroughs in Convective Parameterization
Current Climate Change Reports ( IF 9.5 ) Pub Date : 2019-04-29 , DOI: 10.1007/s40641-019-00127-w
Catherine Rio , Anthony D. Del Genio , Frédéric Hourdin

Purpose of Review

While the increase of computer power mobilizes a part of the atmospheric modeling community toward models with explicit convection or based on machine learning, we review the part of the literature dedicated to convective parameterization development for large-scale forecast and climate models.

Recent Findings

Many developments are underway to overcome endemic limitations of traditional convective parameterizations, either in unified or multiobject frameworks: scale-aware and stochastic approaches, new prognostic equations or representations of new components such as cold pools. Understanding their impact on the emergent properties of a model remains challenging, due to subsequent tuning of parameters and the limited understanding given by traditional metrics.

Summary

Further effort still needs to be dedicated to the representation of the life cycle of convective systems, in particular their mesoscale organization and associated cloud cover. The development of more process-oriented metrics based on new observations is also needed to help quantify model improvement and better understand the mechanisms of climate change.


中文翻译:

对流参数化方面的持续突破

审查目的

虽然计算机功能的增强动员了大气建模界的一部分转向具有显式对流或基于机器学习的模型,但我们回顾了专门针对大尺度预报和气候模型对流参数化开发的文献部分。

最近的发现

为了克服传统对流参数化在地方性或统一性或多对象框架中的局限性,正在进行许多开发工作:规模感知和随机方法,新的预测方程或新成分(例如冷池)的表示。由于参数的后续调整和传统指标所提供的理解有限,因此了解它们对模型新兴属性的影响仍然具有挑战性。

概要

对流系统生命周期的表示仍需进一步努力,特别是对流系统的中尺度组织和相关的云量。还需要根据新的观察结果开发更多面向过程的指标,以帮助量化模型改进并更好地了解气候变化的机制。
更新日期:2019-04-29
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