Updraft Constraints on Entrainment: Insights from Amazonian Deep Convection

Abstract

Mixing of environmental air into clouds, or entrainment, has been identified as a major contributor to erroneous climate predictions made by modern comprehensive climate and numerical weather prediction models. Despite receiving extensive attention, the ad hoc treatment of this convective-scale process in global models remains poor. On the other hand, while limited-area high-resolution nonhydrostatic models can directly resolve entrainment, their sensitivity to model resolution, especially with the lack of benchmark mass flux observations, limits their applicability. Here, the dataset from the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign focusing on radar retrievals of convective updraft vertical velocities is used with the aid of cloud-resolving model simulations of four deep convective events over the Amazon to provide insights into entrainment. Entrainment and detrainment are diagnosed from the model simulations by applying the mass continuity equation over cloud volumes, in which grid cells are identified by some thresholds of updraft vertical velocity and cloud condensates, and accounting for the sources and sinks of the air mass. Entrainment is then defined as the environmental air intruding into convective cores causing cloud volume to shrink, while detrainment is defined as cloudy grid cells departing the convective core and causing cloud volume to expand. It is found that the diagnosed entrainment from the simulated convective events is strongly correlated to the inverse of the updraft vertical velocities in convective cores, which enables a more robust estimation of the mixing time scale. This highlights the need for improved observational capabilities for sampling updraft velocities across diverse geographic and cloud conditions. Evaluation of a number of assumptions used to represent entrainment in parameterization schemes is also presented, as contrasted against the diagnosed one.

Thermal Chains and Entrainment in Cumulus Updrafts. Part I: Theoretical Description

“…Finally, we comment on the negative correlation between « and w (or similarly, the positive correlation between « and w 21 , not shown, which has a correlation coefficient of 0.243 from the theoretical model). Similar relationships have also been found in previous modeling (e.g., Neggers et al 2002;Lu et al 2016;Zhang et al 2016;Anber et al 2019) and observational (Lu et al 2016) studies, with generally larger correlations than found here. A complicating factor in interpreting such relationships is that while w might be expected to influence «, « also directly drives changes in w through momentum mixing and especially buoyancy dilution.…”

“…11, but for the moist environment with R H 5 0.85. in driving variability in « (this can be parsed in our model, which is one of the main benefits of a highly simplified framework). Larger w decreases the time for mixing to occur for rising parcels in our model, which has been invoked to explain an inverse relationship between « and w (e.g., Neggers et al 2002;Anber et al 2019); however, this effect is compensated by increased horizontal shear and larger mixing coefficients when w is larger.…”

“…It has been previously proposed that « is a function of w 21 (e.g., Neggers et al 2002;Anber et al 2019;Zhang et al 2016) (e.g., Jakob and Siebesma 2003), R H (e.g., Bechtold et al 2008), and R 21 (e.g., Simpson and Wiggert 1969), or combinations of parameters including w, B, and dissipation rate (Lu et al 2016). Figure 13 shows scatterplots of the theoretical « values with w, B, B/w 2 , z, R H , and R 21 .…”

An Observational Comparison of Level of Neutral Buoyancy and Level of Maximum Detrainment in Tropical Deep Convective Clouds

“…The treatment of the cloud entrainment/detrainment process has been identified as a bottleneck for current global climate model (GCM) representation of deep convection, as well as simulations to cloud‐resolving model (CRM) scales (e.g., Anber et al., 2019; Bretherton et al., 2004; Del Genio et al., 2012; Kain & Fritsch, 1990; Kim & Kang, 2012; Klocke et al., 2011; Romps, 2010; Stirling & Stratton, 2012). For instance, a small change to the parameterization of the entrainment rate can introduce a significant shift in the timing or amplitude of the convective diurnal cycle (e.g., Del Genio & Wu, 2010; Yang & Slingo, 2001), thereby altering the large‐scale circulation and resulting in significant climate variability (e.g., Hannah & Maloney, 2011; Maloney & Hartmann, 2001; Sanderson et al., 2008; Tokioka et al., 1988).…”

Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence

“…Research over the past 2 decades has focused on refining the formulation of the entrainment rate, a key control on climate model sensitivity to greenhouse gas concentrations (Stainforth et al, 2005;Knight et al, 2007). Typically, this rate is represented by a constant parameter = θ or a parametric function = (z, ζ ; θ ) of height z and (usually local) plume and environmental properties encoded in the model state ζ (e.g., de Rooy et al, 2013;Yeo and Romps, 2013;Anber et al, 2019;Savre and Herzog, 2019;Cohen et al, 2020). Similarly, diffusive closures of various types have been commonly employed for boundary layer turbulence in the atmosphere and oceans.…”