Effects of Cloud Liquid‐Phase Microphysical Processes in Mixed‐Phase Cumuli Over the Tibetan Plateau

Abstract

Numerical simulations often overpredict precipitation over the Tibetan Plateau (TP). To examine the factors causing precipitation overprediction, different parameterizations of liquid-phase microphysical processes (accretion, autoconversion, and entrainment mixing) are implemented into the Morrison microphysics scheme to simulate a TP precipitation event in summer with the Weather Research and Forecasting (WRF) model. The general spatial distribution and temporal trend of precipitation are captured by all simulations, but the precipitation rate is overpredicted. The results from sensitivity experiments suggest that compared to other examined liquid-phase processes, the accretion process is more important in precipitation simulation over the TP region. Further investigation with the Heidke skill scores reveals that accretion parameterization that takes into account the raindrop size produces the most accurate results in terms of the total surface precipitation. This parameterization suppresses spurious accretion and does not produce liquid-phase precipitation until cloud droplets are big enough. It is also confirmed that increasing the model resolution can reduce precipitation overprediction. Results from the case study are confirmed by the use of a 1-month simulation.

Consideration of Initial Cloud Droplet Size Distribution Shapes in Quantifying Different Entrainment‐Mixing Mechanisms

“…Clouds are critical to weather and climate, hydrological cycle, and radiative balance (Del Genio et al., 2015; Guo et al., 2019; Li et al., 2020; Lin et al., 2020; Randall, Bitz, et al., 2019; Wood, 2012; P. Yang et al., 2015; Zhang, Wu, et al., 2018; Zhang, Yan, et al., 2018), and as such, the accurate descriptions of macro and microphysical cloud processes/properties are essential to improving weather and climate simulations/forecasts/projections (Donner et al., 2016; W. Gao et al., 2016; Liu, 2019; Randall, Srinivasan, et al., 2019; Y. Wang et al., 2013; Xu et al., 2020; G. J. Zhang et al., 2019; M. Zhang et al., 2013). One important process is the small‐scale entrainment‐mixing process between subsaturated droplet‐free air and cloud volume containing droplets (Baker et al., 1980; Beals et al., 2015; Kumar et al., 2018).…”

Parameterizations of Entrainment‐Mixing Mechanisms and Their Effects on Cloud Droplet Spectral Width Based on Numerical Simulations

“…Clouds significantly affect the energy balance (Grabowski & Morrison, 2011; Ramanathan et al, 2001; Stevens & Feingold, 2009; Wang, 2015; Yang et al, 2015; Yu et al, 2007; Zelinka et al, 2017), atmospheric motion at all scales (Lee, 2012), and water distribution (Moncrieff et al, 1997; Ramanathan et al, 2001). Cloud liquid‐phase processes over TP play important roles in the development of cloud and precipitation (Li et al, 2008; Li & Zhang, 2017; Qiu et al, 2019; Xu et al, 2020; Zhao, Liu, et al, 2017; Zhao, Xu, et al, 2017), while parameterizations of liquid‐phase processes remain uncertain in microphysics schemes of general circulation models or even cloud‐resolving models (e.g., Gao et al, 2016; Gettelman & Morrison, 2015; Grabowski, 2006). In particular, the turbulent entrainment‐mixing process is still poorly understood, although the process has been studied for decades since Stommel (1947).…”

Precipitation efficiency of cloud and its influencing factors over the Tibetan plateau

“…Due to the thick ice cloud, the cold rain process mainly contributes the wide precipitation range, and the high warm rain intensity in the precipitation centre during the weak convection period also suggests that the warm rain microphysical processes play an important role (Gao et al ., 2016). The simulation studies (Gao et al ., 2018; Xu et al ., 2020) of convective precipitation over the TP also suggests that the accretion processes of liquid‐phase processes are more important for the formation of precipitation, and the supercooled water above the freezing level is abundant, and the deposition of graupel plays a crucial role in the formation of precipitation. For meso‐micro scale systems, PE is an important parameter for evaluating the efficiency of cloud water conversion to precipitation (Li et al ., 2002; McCaul Jr et al ., 2005; Brauer et al ., 2020); and for large‐scale systems, PE is a fundamental tool for investigating the feedback between clouds and climate (Lau and Wu, 2003; Tao et al ., 2004).…”