Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1575-2021
https://doi.org/10.5194/gmd-14-1575-2021
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

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Cited articles

Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., and Bolvin, D.: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present), J. Hydrometeorol., 4, 1147–1167, 2003. 
Bentamy, A., Queffeulou, P., Quilfen, Y., and Katsaros, K.: Ocean surface wind fields estimated from satellite active and passive microwave instruments, IEEE T. Geosci. R., 37, 2469–2486, 1999. 
Cohen, B. G. and Craig, G. C.: Fluctuations in an Equilibrium Convective Ensemble. Part II: Numerical Experiments, J. Atmos. Sci., 63, 2005–2015, https://doi.org/10.1175/JAS3710.1, 2006. 
Craig, G. C. and Cohen, B. G.: Fluctuations in an Equilibrium Convective Ensemble. Part I: Theoretical Formulation, J. Atmos. Sci., 63, 1996–2004, https://doi.org/10.1175/JAS3709.1, 2006. 
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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.