Large‐Eddy Simulations of a Convection Cloud Chamber: Sensitivity to Bin Microphysics and Advection

Abstract

Clouds play a crucial role in the Earth's energy balance by reflecting solar radiation and absorbing terrestrial radiation. They are also important to the hydrological cycle through precipitation and water redistribution in the atmosphere. A better representation of clouds in atmospheric models can improve weather forecasts and climate projections. One challenge of cloud simulation is to accurately represent cloud properties and processes under various conditions. The challenge arises from limited understanding of complicated cloud microphysical processes at the fundamental level, as well as from imperfect translation of this understanding to the numerical algorithms used to resolve or parameterize microphysical processes (e.g., Khain et al., 2015;Morrison et al., 2020).In general, there are three approaches to simulate microphysical properties of hydrometeors and related processes in atmospheric models: bulk representation of particle properties, bin microphysical schemes, and Lagrangian particle methods. Bulk microphysics schemes use one or more attributes (e.g., moments of the hydrometeor size

Adiabatic Evolution of Cloud Droplet Spectral Width: A New Look at an Old Problem

“…(2019), and Yang et al. (2022). This especially applies to shallow clouds developing in polluted environments.…”

“…Because the effects discussed here cannot be directly included in one-dimensional bin microphysics, results obtained by using such models, especially targeting spectral broadening, need to be treated with caution. Examples of such simulations are those of Kogan et al (1995), Stevens et al (1996), Khairoutdinov and Kogan (2000), Lu and Seinfeld (2005), Ovchinnikov et al (2013), Igel and van den Heever (2017), Witte et al (2019), Thomas et al (2019, Khain et al (2019), andYang et al (2022). This especially applies to shallow clouds developing in polluted environments.…”

“…In numerical cloud modeling, bin microphysics is an often used tool in simulations of cloud droplet formation and growth in both natural clouds (e.g., Grabowski et al., 2015; Igel and van den Heever, 2017; Khain et al., 2019; Kogan, 1991; Ovchinnikov et al., 2013; Reisin et al., 1996; Stevens et al., 1996; Suzuki et al., 2010; Witte et al., 2019) and in laboratory clouds (e.g., Grabowski, 2020; Thomas et al., 2019; Yang et al., 2022). The key issue with bin microphysics is that the droplet growth equation is used with the curvature and solute terms neglected.…”

Scaling of Turbulence and Microphysics in a Convection–Cloud Chamber of Varying Height

“…We consider the question, how well can the microphysical properties be matched as the scale of a convection-cloud chamber is increased? Previous studies show that with increasing aerosol injection rate, the cloud droplet size distribution becomes narrower, leading to an increase in cloud droplet number concentration and a decrease in the mean droplet size (Chandrakar et al, 2016;Thomas et al, 2019;Yang et al, 2022). In this study, we try to address the following questions: (a) can we match the droplet size distribution in convection cloud chambers of different sizes by changing the aerosol injection rates?…”

Preserving tracer correlations in atmospheric transport models

“…The bottleneck is caused by the failure of higherorder advection schemes to preserve correlations between interrelated tracers during transport -a task for which they were never designed. The paper was motivated in part by our recent convective cloud chamber study [Yang et al, 2022], which employed a second-order advection scheme and a two-moment cloud microphysical scheme limited to tracking particle number and mass. The new approach introduces a diffusion limiter, under the idea that achieving minimal spatial variance on an Eulerian model grid implies maximal resolution and elimination of numerical diffusion.…”