UCLALES–SALSA v1.0: a large-eddy model with interactive sectional microphysics for aerosol, clouds and precipitation

Abstract

Abstract. Challenges in understanding the aerosol-cloud interactions and their impacts on global climate highlight the need for improved knowledge of the underlying physical processes and feedbacks as well as their interactions with cloud and boundary layer dynamics. To pursue this goal, increasingly sophisticated cloud-scale models are needed to complement the limited supply of observations of the interactions between aerosols and clouds. For this purpose, a new large-eddy simulation (LES) model, coupled with an interactive sectional description for aerosols and clouds, is introduced. The new model builds and extends upon the well-characterized UCLA Large-Eddy Simulation Code (UCLALES) and the Sectional Aerosol module for Large-Scale Applications (SALSA), hereafter denoted as UCLALES-SALSA. Novel strategies for the aerosol, cloud and precipitation bin discretisation are presented. These enable tracking the effects of cloud processing and wet scavenging on the aerosol size distribution as accurately as possible, while keeping the computational cost of the model as low as possible. The model is tested with two different simulation set-ups: a marine stratocumulus case in the DYCOMS-II campaign and another case focusing on the formation and evolution of a nocturnal radiation fog. It is shown that, in both cases, the size-resolved interactions between aerosols and clouds have a critical influence on the dynamics of the boundary layer. The results demonstrate the importance of accurately representing the wet scavenging of aerosol in the model. Specifically, in a case with marine stratocumulus, precipitation and the subsequent removal of cloud activating particles lead to thinning of the cloud deck and the formation of a decoupled boundary layer structure. In radiation fog, the growth and sedimentation of droplets strongly affect their radiative properties, which in turn drive new droplet formation. The size-resolved diagnostics provided by the model enable investigations of these issues with high detail. It is also shown that the results remain consistent with UCLALES (without SALSA) in cases where the dominating physical processes remain well represented by both models.

Aerosol-fog interaction and the transition to well-mixed radiation fog

“…droplet activation and a rise the observed and LES cloud droplet numbers . Fig 9(b) shows a small secondary peak in the size spectra around 10 µm developing due to the increasing turbulence, and the evolution of the spectra and cloud droplet numbers after this point is very similar to that already reported in Price (2011) and Tonttila et al (2017), hence we do not focus on it here. The well-mixed layer continues to grow throughout the night, until the entire fog layer is well-mixed and turbulent by 08 UTC.…”

“…Instead, hydrated aerosol particles and cloud droplets are allowed to sediment onto the surface. Full details can be found in Tonttila et al (2017) and references within.…”

A Model Intercomparison of CCN-Limited Tenuous Clouds in the High Arctic

“…The LES models participating in this study are the University of California, Los Angeles LES with Sectional Aerosol module for Large Scale Applications (UCLALES-SALSA; Tonttila et al, 2017), the MISU MIT Cloud and Aerosol LES model (MIM-…”

“…The participating models are described and compared in Table 1. UCLALES-SALSA is combination of an LES model (UCLALES; Stevens et al, 1999Stevens et al, , 2005 and a sectional aerosol and cloud microphysics module (SALSA; Kokkola et al, 2008). A detailed description of UCLALES-SALSA can be found in Tonttila et al (2017). The properties and microphysical processes of aerosol, cloud droplets, and rain are defined for certain 25 size sections (bins).…”

Fogs: Physical Basis, Characteristic Properties, and Impacts on the Environment and Human Health

“…As far as the distributions of water vapor and hydrometeors are highly variable in space and time, the precise analysis and forecasting are still challenging tasks [43]. In addition, few fog monitoring sites exist [44,45]. Therefore, studying fogs on a spatial scale requires numerical modeling and simulations.…”