Broadening of Cloud Droplet Spectra through Eddy Hopping: Turbulent Entraining Parcel Simulations

Abstract

This paper discusses the effects of cloud turbulence, turbulent entrainment, and entrained cloud condensation nuclei (CCN) activation on the evolution of the cloud droplet size spectrum. We simulate an ensemble of idealized turbulent cloud parcels that are subject to entrainment events modeled as a random process. Entrainment events, subsequent turbulent mixing inside the parcel, supersaturation fluctuations, and the resulting stochastic droplet activation and growth by condensation are simulated using a Monte Carlo scheme. Quantities characterizing the turbulence intensity, entrainment rate, CCN concentration, and the mean fraction of environmental air entrained in an event are all specified as independent external parameters. Cloud microphysics is described by applying Lagrangian particles, the so-called superdroplets. These are either unactivated CCN or cloud droplets that grow from activated CCN. The model accounts for the addition of environmental CCN into the cloud by entraining eddies at the cloud edge. Turbulent mixing of the entrained dry air with cloudy air is described using the classical linear relaxation to the mean model. We show that turbulence plays an important role in aiding entrained CCN to activate, and thus broadening the droplet size distribution. These findings are consistent with previous large-eddy simulations (LESs) that consider the impact of variable droplet growth histories on the droplet size spectra in small cumuli. The scheme developed in this work is ready to be used as a stochastic subgrid-scale scheme in LESs of natural clouds.

Aerosol–Cloud Closure Study on Cloud Optical Properties using Remotely Piloted Aircraft Measurements during a BACCHUS Field Campaign in Cyprus

“…Entrainment is well-known for influencing the boundary layer and clouds (e.g., Blyth (1993); Baker (1992); Carman et al (2012)). Recent works have been published, investigating the role of entrainment and turbulence for broadening the cloud droplet spectra with an adiabatic parcel model (Grabowski and Abade (2017); Abade et al (2018)), aiming to improve subgrid- 15 scale representation for a large eddy simulation cloud model. Studies of entrainment-mixing mechanisms in cumulus clouds used manned aircraft observations (CIRPAS Twin Otter) to highlight the scale dependence of the mixing processes (Lu et al, 2018).…”

Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0/2.2.1

“…Particle-based cloud microphysics modeling is a new approach that has emerged since the mid-2000s (e.g., Paoli et al, 2004;Jensen and Pfister, 2004;Shirgaonkar and Lele, 2006;Andrejczuk et al, 2008Andrejczuk et al, , 2010Shima et al, 2009;Sölch and Kärcher, 2010;Riechelmann et al, 2012;Brdar and Seifert, 2018;Seifert et al, 2019;Jaruga and Pawlowska, 2018;Grabowski and Abade, 2017;Abade et al, 2018;Hoffmann et al, 2019;Grabowski et al, 2018). During particle-based modeling's early development, calculating the collision-coalescence process was a numerical challenge.…”

Droplet size distributions in turbulent clouds: experimental evaluation of theoretical distributions

“…Several different theoretical models have been proposed to characterize the shape of cloud DSDs formed through the condensation process. Two approaches that have received considerable recent attention are systems-theory derivations based on the principle of maximum entropy (Liu and Hallett, 1998;Liu et al 2002;Yano et al 2016;Wu and McFarquhar, 2018) and derivations based on a Langevin equation representation of stochastic condensation (McGraw and Liu, 2006;Paoli and Shariff, 2009;Chandrakar et al 2016;Siewert et al 2017;Abade et al 2018;Saito et al 2019). By and large, however, these theoretical models have not been subjected to systematic experimental evaluation to validate the proposed functional forms for the DSD.…”