Effects of Coupling a Stochastic Convective Parameterization with the Zhang–McFarlane Scheme on Precipitation Simulation
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. The accurate simulation of precipitation in global climate models (GCMs) is of great scientific and societal interest. However, GCMs used for current climate simulation and future projections suffer from many biases in the global distribution, frequency, and intensity of simulated precipitation, which have negatively impacted the model’s fidelity. Here we show that GCM precipitation simulations can be much better improved by incorporating a stochastic convective parameterization.
As an important part of the Earth’s hydrological cycle, more accurate simulations of global and regional precipitation distribution, frequency and intensity are critical to better simulations of the current climate and future projected climate changes for decision making and impacts applications.
By Implementing a stochastic deep-convection scheme into the Zang-McFarlan (ZM) deterministic deep-convection scheme, we improve the representation of convection in the US Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The well-known problem of “too much light rain and too little heavy rain” is alleviated, especially over the tropics. The mean precipitation amount distribution is improved with more precipitation contribution from more intense precipitation events. The synoptic and intraseasonal variabilities of precipitation are enhanced and are closer to observations.