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Current Climate Change Reports

, Volume 5, Issue 2, pp 95–111 | Cite as

Ongoing Breakthroughs in Convective Parameterization

  • Catherine RioEmail author
  • Anthony D. Del Genio
  • Frédéric Hourdin
Convection and Climate (C Muller, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Convection and Climate

Abstract

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.

Keywords

Convective parameterizations for large-scale models Stochastic approaches Convective memory Mesoscale circulation Cold pools Process-oriented metrics 

Notes

Acknowledgements

CR and FH are supported by CNRS and thank the LEFE/INSU french national program DEPHY. AD was supported by the NASA Precipitation Measurement Missions, CloudSat/CALIPSO Mission, and Modeling and Analysis Programs and by the DOE Atmospheric System Research Program. The authors thank Jean-Yves Grandpeix for useful discussions as well as two anonymous reviewers who helped improve the manuscript.

Compliance with Ethical Standards

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre National de Recherches MétéorologiquesUniversité de Toulouse, Météo-France, CNRSToulouseFrance
  2. 2.NASA Goddard Institute for Space StudiesNew YorkUSA
  3. 3.Laboratoire de Météorologie Dynamique, IPSL, CNRSSorbonne UniversitéParisFrance

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