Understanding the Roles of Convective Trigger Functions in the Diurnal Cycle of Precipitation in the NCAR CAM5

Abstract

The wrong diurnal cycle of precipitation is a common weakness of current global climate models (GCMs). To improve the simulation of the diurnal cycle of precipitation and understand what physical processes control it, we test a convective trigger function described in Xie et al. (2019) with additional optimizations in the NCAR Community Atmosphere Model version 5 (CAM5). The revised trigger function consists of three modifications: 1) replacing the Convective Available Potential Energy (CAPE) trigger with a dynamic CAPE (dCAPE) trigger, 2) allowing convection to originate above the top of planetary boundary layer (i.e., the unrestricted air parcel launch level - ULL), and 3) optimizing the entrainment rate and threshold value of the dynamic CAPE generation rate for convection onset based on observations. Results from 1°-resolution simulations show that the revised trigger can alleviate the long-standing GCM problem of too early maximum precipitation during the day and missing the nocturnal precipitation peak that is observed in many regions, including the US Southern Great Plains (SGP). The revised trigger also improves the simulation of the propagation of precipitation systems downstream of the Rockies and the Amazon region. A further composite analysis over the SGP unravels the mechanisms through which the revised trigger affects convection. Additional sensitivity tests show that both the peak time and the amplitude of the diurnal cycle of precipitation are sensitive to the entrainment rate and dCAPE threshold values.

Long‐term single‐column model intercomparison of diurnal cycle of precipitation over midlatitude and tropical land

“…(2020) and Cui et al . (2021). Here for SCM, the improvement of DCP is also shown at SGP and MAO (Figures 13 and 14) (blue dashed line and open circle).…”

Description and Evaluation of a New Deep Convective Cloud Model Considering In‐Cloud Inhomogeneity

“…The cause of many of the problems found in the current GCMs can be traced to convection parameterizations. Weak MJO‐like signals (J. L. Lin et al., 2006; G. J. Zhang & Mu, 2005), too early precipitation peaks (Cui et al., 2021), too frequent weak precipitations (Dai, 2006; Pendergrass & Hartmann, 2014; Stephens et al., 2010), and a dilemma between the mean climate and tropical transients (i.e., entrainment dilemma, Jiang et al., 2020; Kim et al., 2012; Y. Lin et al., 2013; Mapes & Neale, 2011) are some examples. Modifying the entrainment parameterizations and improving the existent trigger functions and closures in convection schemes are two commonly used approaches to alleviate these biases.…”

“…Weak MJO-like signals (J. L. Lin et al, 2006; G. J. Zhang & Mu, 2005), too early precipitation peaks (Cui et al, 2021), too frequent weak precipitations (Dai, 2006;Pendergrass & Hartmann, 2014;Stephens et al, 2010), and a dilemma between the mean climate and tropical transients (i.e., entrainment dilemma, Jiang et al, 2020;Y. Lin et al, 2013;Mapes & Neale, 2011) are some examples.…”

An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration

“…Convection and cloud parameterization schemes used in global climate models (GCMs) are a major source of many biases in the simulation of climate and its variability. These include biases in the Intertropical Convergence Zone (ITCZ) (Zhang et al., 2019), intraseasonal variability such as Madden Julian Oscillation (MJO) (Cao & Zhang, 2017; Zhang & Mu, 2005) and diurnal cycle of precipitation (Cui et al., 2021; Xie et al., 2019). They are also the main causes of uncertainties in GCM‐simulated response of cloud radiative forcing and precipitation to global warming (Stevens & Bony, 2013).…”