Simulation of Precipitation with a Variable Characteristic Adjustment Time Scale Parameter of Deep Convection in a Global AGCM

The characteristic adjustment time scale τ is always defined as the time allowed for dissipation of Convective Available Potential Energy (CAPE) in convective parameterization schemes. Previous studies indicate that, in the cloud ensemble, τ is one of the most important parameters that have  the greatest influences on the global mean precipitation. Some research work has improved the Kain–Fritsch scheme in the regional model to realize the variable parameters. In the global model, some studies have used machine learning methods to optimize the parameters of the deep convection trigger function. However, changing constant parameters into variable parameters in the global model has not been explored. In our study, the Zhang-McFarlane (ZM) deep convection scheme is improved to realize the variable characteristic adjustment time scale parameter, so as to reduce the precipitation deviation in a global model. In the ZM deep convection scheme, τ is usually the default constant. While in this paper, we use CAPE to modulate τ and propose a calculation formula of τ. In the region where the mean precipitation amount bias is improved, the new scheme mainly increases the deep convective precipitation and reduces the large-scale and shallow convective precipitation. The modified scheme significantly improves the simulation of precipitation over the eastern equatorial Pacific Ocean and some steep terrain regions. The root mean square error of the mean precipitation amount over the eastern equatorial Pacific Ocean and the central Indo-Pacific Warm Pool in boreal summer is reduced after the new scheme is adopted in a global model with the horizontal resolution of 1° longitude and 1° latitude. Moreover, the simulations of precipitation over the Tibet Plateau and South America are also improved. The new scheme reduces the frequency of deep convective precipitation and increases the amount of deep convective precipitation each time.