Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 3
Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1575-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-1575-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 3
Development and technical paper
 | 
18 Mar 2021
Development and technical paper |  | 18 Mar 2021

Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model

Yong Wang, Guang J. Zhang, Shaocheng Xie, Wuyin Lin, George C. Craig, Qi Tang, and Hsi-Yen Ma

Data sets

EAMv1 simulation output 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

A mapping file for the EAMv1 simulation output [Data set] Yong Wang https://doi.org/10.5281/zenodo.4543233

Model code and software

Stochastic convection code in the DOE EAMv1 Yong Wang, Guang J. Zhang, and George C. Craig https://doi.org/10.5281/zenodo.4543261

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Short summary
A stochastic deep convection parameterization is implemented into the US 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 the deep convective parameterization.
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