Abstract

Abstract. The large-eddy model UCLALES–SALSA, with an exceptionally detailed aerosol description for both aerosol number and chemical composition, has been extended for ice and mixed-phase clouds. Comparison to a previous mixed-phase cloud model intercomparison study confirmed the accuracy of newly implemented ice microphysics. A further simulation with a heterogeneous ice nucleation scheme, in which ice-nucleating particles (INPs) are also a prognostic variable, captured the typical layered structure of Arctic mid-altitude mixed-phase cloud: a liquid layer near cloud top and ice within and below the liquid layer. In addition, the simulation showed a realistic freezing rate of droplets within the vertical cloud structure. The represented detailed sectional ice microphysics with prognostic aerosols is crucially important in reproducing mixed-phase clouds.

Highlights

  • Clouds are known to have a prominent influence on the hydrological cycle and the atmospheric radiation balance

  • We focus on how ice crystals and ice-nucleating particles (INPs) interact with clouds while tracking sectional aerosol size distribution

  • We implemented in UCLALES–SALSA model runs with the same semi-idealised simulation setup given in Ovchinnikov et al (2014) that attempted to minimise intermodel differences by applying identical descriptions for the following processes: surface properties and fluxes, large-scale forcings, radiation, cloud droplet freezing and ice growth processes and sedimentation, and the nudging of horizontal winds, potential temperature and water content above the altitude of 1200 m

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Summary

Introduction

Clouds are known to have a prominent influence on the hydrological cycle and the atmospheric radiation balance. While significant advances have been made in characterisation of liquid-phase clouds, the microphysical processes, especially heterogeneous ice nucleation, dynamics and radiative effects of mixed-phase and ice clouds remain more poorly constrained This is mainly because of challenges in obtaining representative observations and a lack of a detailed enough representation of microphysics in climate and numerical weather prediction models. The loss of INPs along with precipitating ice crystals limits cloud glaciation and dissipation (Rauber and Tokay, 1991; Harrington et al, 1999; Avramov and Harrington, 2010) Describing this process is not possible without a detailed description of aerosols, as is demonstrated in a 1-D cloud model study by Morrison et al (2005). We demonstrate the benefits of this approach to handle heterogeneous freezing over more simplified aerosol–ice–cloud treatments

Model description
Model evaluation
Sensitivity on ice concentration
Prognostic ice simulation
Conclusions
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