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

Abstract

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.

Effects of Improved Simulation of Precipitation on Evapotranspiration and Its Partitioning Over Land

“…A detailed description of the implementation of the stochastic scheme and its overall performance in the climate models can be found in Y. Wang et al. (2016, 2021). Two AMIP‐type simulations are conducted using the observed monthly sea surface temperatures and sea ice extent as lower boundary conditions, with the standard ZM scheme (CAM5) and the addition of the stochastic scheme (STOC), respectively.…”

“…Recently, Y. Wang et al. (2016, 2021) successfully suppressed the excessive occurrence of light rain by incorporating a stochastic convective scheme (Plant & Craig, 2008) into the Zhang‐McFarlane deep convection scheme (Zhang & McFarlane, 1995) in two GCMs. The improved rainfall frequency simulation provides a unique opportunity to address the above questions.…”

Impacts of Suppressing Excessive Light Rain on Aerosol Radiative Effects and Health Risks

“…Wang, Xia, et al. (2021) showed that, when light rain was suppressed by improving convection parameterization (Y. Wang, Zhang, et al., 2021; Y. Wang et al., 2016, 2017), it had a profound impact on the atmospheric aerosol concentration. The aerosol optical depth (AOD) increased at the global scale, especially in the rain belts over the tropics and subtropics, by as much as 0.3.…”