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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-249
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-249
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 29 Sep 2020

Submitted as: development and technical paper | 29 Sep 2020

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This preprint is currently under review for the journal GMD.

Effects of Coupling a Stochastic Convective Parameterization with Zhang-McFarlane Scheme on Precipitation Simulation in the DOE E3SMv1 Atmosphere Model

Yong Wang1, Guang J. Zhang2, Shaocheng Xie3, Wuyin Lin4, George C. Craig5, Qi Tang3, and Hsi-Yen Ma3 Yong Wang et al.
  • 1Ministry of Education Key Laboratory for Earth System Modeling & Department of Earth System Science, Tsinghua University, Beijing, 100084, China
  • 2Scripps Institution of Oceanography, La Jolla, CA, USA
  • 3Lawrence Livermore National Laboratory, CA, USA
  • 4Brookhaven National Laboratory, Upton, NY, USA
  • 5Meteorologisches Institut, Ludwig-Maximilians-Universität, Munich, Germany

Abstract. A stochastic deep convection parameterization is implemented into the U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). This study evaluates its performance on the precipitation simulation. Compared to the default model, the probability distribution function (PDF) of rainfall intensity in the new simulation is greatly improved. Especially, the well-known problem of too much light rain and too little heavy rain is alleviated over the tropics. As a result, the contribution from different rain rates to the total precipitation amount is shifted toward heavier rain. The less frequent occurrence of convection contributes to the suppressed light rain, while both more intense large-scale and convective precipitation contribute to the enhanced heavy total rain. The synoptic and intraseasonal variabilities of precipitation are enhanced as well to be closer to observations. A sensitivity of the rainfall intensity PDF to the model vertical resolution is identified and explained in terms of the relationships between convective precipitation and convective available potential energy (CAPE) and between large-scale precipitation and resolved-scale upward moisture flux. The annual mean precipitation is largely unchanged with the use of the stochastic scheme except over the tropical western Pacific, where a moderate increase in precipitation represents a slight improvement. The responses of precipitation and its extremes to climate warming are similar with or without the stochastic deep convection scheme.

Yong Wang et al.

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The EAMv1 simulation datasets for the manuscript Yong Wang, Guang J. Zhang, Shaocheng Xie, Wuyin Lin, George C. Craig, Qi Tang, and Hsi-Yen Ma https://doi.org/10.5281/zenodo.3902998

Yong Wang et al.

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Short summary
A stochastic deep convection parameterization is implemented into the U.S. Department of Energy Energy Exascale Earth System Model Atmosphere Model version 1 (EAMv1). Compared to the default model, the well-known problem of too much light rain and too little heavy rain is largely alleviated over the tropics with the stochastic scheme. Results from this study provide important insights into the model performance of EAMv1 when stochasticity is included in deep convective parameterization.
A stochastic deep convection parameterization is implemented into the U.S. Department of Energy...
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