Abstract
Abstract. A stochastic deep convection parameterization is implemented into the US Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1.0 (EAMv1). This study evaluates its performance in simulating precipitation. Compared to the default model, the probability distribution function (PDF) of rainfall intensity in the new simulation is greatly improved. The well-known problem of “too much light rain and too little heavy rain” is alleviated, especially 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 suppressed light rain, while more intense large-scale and convective precipitation contributes to enhanced heavy total rain. The synoptic and intraseasonal variabilities of precipitation are enhanced as well to be closer to observations. The sensitivity of the rainfall intensity PDF to the model vertical resolution is examined. The relationship between precipitation and dilute convective available potential energy in the stochastic simulation agrees better with that in the Atmospheric Radiation Measurement (ARM) observations compared with the standard model simulation. 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.
Highlights
Precipitation plays a vital role in the Earth’s climate: the latent heat released during precipitation formation is a major energy source that drives the atmospheric circulation, and precipitation is an important part of the Earth’s hydrological cycle
We have shown that the introduction of a stochastic convection scheme into the E3SM atmospheric model can significantly improve the simulation of the short-term variability and intensity probability distribution function (PDF) of precipitation
Several improvements are observed with the use of the stochastic convection scheme: (1) the weak intraseasonal and synoptic-scale variabilities in EAMv1 are enhanced to levels much closer to those in observations; (2) the “too much light rain and too little heavy rain” bias over the tropics is significantly alleviated due to less frequent occurrence of drizzling convection and more frequent occurrence of intense large-scale and convective precipitation, contributing to enhanced heavy rain; and (3) the simulated peak precipitation rates in the precipitation amount distribution, which contribute the most to the total amount of precipitation, are larger and in better agreement with those in Tropical Rainfall Measuring Mission 3B42 version 7 (TRMM) and Global Precipitation Climatology Project (GPCP) observations
Summary
Precipitation plays a vital role in the Earth’s climate: the latent heat released during precipitation formation is a major energy source that drives the atmospheric circulation, and precipitation is an important part of the Earth’s hydrological cycle. Not including stochasticity in convective schemes has been suggested to be at least partly responsible for the weak intraseasonal variability and “too much light rain and too little heavy rain” in GCMs (Lin and Neelin, 2000; Wang et al, 2016; Watson et al, 2017; Peters et al, 2017). As suggested in Palmer (2001, 2012), more realistic statistics of the impacts of subgrid convective clouds should be derived by simulating them as random samples from probability distributions conditioned on the grid-scale state so that the influences of different individual realizations are introduced in the convection parameterization In this regard, much effort in the past 2 decades has been made to develop stochastic convection schemes (e.g., Lin and Neelin, 2000, 2002; Plant and Craig, 2008; Khouider et al, 2010; Sakradzija et al, 2015).
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