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. A stochastic deep convection parameterization is implemented intothe US Department of Energy (DOE) Energy Exascale Earth System Model(E3SM) Atmosphere Model version 1.0 (EAMv1). This study evaluates itsperformance in simulating precipitation. Compared to the default model,the probability distribution function (PDF) of rainfall intensity in the newsimulation is greatly improved. The well-known problem of “toomuch light rain and too little heavy rain” is alleviated, especially over the tropics.As a result, the contribution from different rain rates to the totalprecipitation amount is shifted toward heavier rain. The less frequentoccurrence of convection contributes to suppressed light rain, whilemore intense large-scale and convective precipitation contributes toenhanced heavy total rain. The synoptic and intraseasonal variabilitiesof precipitation are enhanced as well to be closer to observations. Thesensitivity of the rainfall intensity PDF to the model vertical resolutionis examined. The relationship between precipitation and dilute convectiveavailable potential energy in the stochastic simulation agrees better withthat in the Atmospheric Radiation Measurement (ARM) observations comparedwith the standard model simulation. The annual mean precipitation is largelyunchanged with the use of the stochastic scheme except over the tropicalwestern Pacific, where a moderate increase in precipitation represents aslight improvement. The responses of precipitation and its extremes toclimate warming are similar with or without the stochastic deep convectionscheme.

geoscientific model development

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