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

Related authors

Climate impacts of parameterizing subgrid variation and partitioning of land surface heat fluxes to the atmosphere with the NCAR CESM1.2
Ming Yin, Yilun Han, Yong Wang, Wenqi Sun, Jianbo Deng, Daoming Wei, Ying Kong, and Bin Wang
Geosci. Model Dev., 16, 135–156,,, 2023
Short summary
Effective radiative forcing of anthropogenic aerosols in E3SM version 1: historical changes, causality, decomposition, and parameterization sensitivities
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160,,, 2022
Short summary
What rainfall rates are most important to wet removal of different aerosol types?
Yong Wang, Wenwen Xia, and Guang J. Zhang
Atmos. Chem. Phys., 21, 16797–16816,,, 2021
Short summary

Related subject area

Climate and Earth system modeling
Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151,,, 2023
Short summary
Simulated stable water isotopes during the mid-Holocene and pre-industrial periods using AWI-ESM-2.1-wiso
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178,,, 2023
Short summary
Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
Andrew Gettelman
Geosci. Model Dev., 16, 4937–4956,,, 2023
Short summary
ModE-Sim – a medium-sized atmospheric general circulation model (AGCM) ensemble to study climate variability during the modern era (1420 to 2009)
Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
Geosci. Model Dev., 16, 4853–4866,,, 2023
Short summary
MESMAR v1: a new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833,,, 2023
Short summary

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,, 2006. 
Craig, G. C. and Cohen, B. G.: Fluctuations in an Equilibrium Convective Ensemble. Part I: Theoretical Formulation, J. Atmos. Sci., 63, 1996–2004,, 2006. 
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.