Conditions for Convective Deep Inflow

Abstract

Accurate simulation and forecasting of weather and climate depends on adequate representations of deep convection in general circulation models (GCMs). This remains a challenging subject (Kuo et al., 2020;Leung et al., 2022;Yano & Plant, 2020) even with recent advances in cloud-resolving models (CRMs) and machine learning (Bretherton et al., 2022;Wing et al., 2020). Challenges arise especially in regards to organized convection, such as mesoscale convective systems (MCSs; Moncrieff et al., 2012;Yano & Moncrieff, 2016) that account for a significant fraction of precipitation (Nesbitt et al., 2006). A major source of uncertainty is the entrainment process of environmental air entering in-cloud updrafts (Plant, 2010;Sherwood et al., 2014). The traditional view of entrainment assumes a plume/parcel rising from near the surface that is modified by its immediate surroundings via localized, small-scale turbulent mixing (Arakawa & Schubert, 1974). This motivated efforts to quantify a postulated local entrainment rate (