Aerosol particles provide nuclei on which cloud droplets form (cloud condensation nuclei - CCN). If a cloud develops precipitation some of the CNN are removed from the atmosphere. However, due to evaporation of cloud droplets and drizzle drops, part of the CCN remains in the atmosphere. Remaining CCN have altered physico-chemical properties if the evaporated droplets went through collisional growth or irreversible chemical reactions (e.g. SO2 oxidation). Hence, the aerosol particles influence cloud microphysical properties, and in turn, cloud microphysical and chemical processes may affect aerosol size spectrum and its chemical composition. The main challenge of representing these processes in a numerical cloud model stems from the need to track the properties of activated CCN and the chemical composition of cloud droplets throughout the cloud lifecycle.
Tracking particle properties is an inherent feature of the particle-based frameworks. In this study we apply the particle-based scheme of Shima et al. 2009. Modeled particles (aka super-droplets) are a numerical proxy for a multiplicity of real-world CCN, cloud, drizzle or rain particles of the same size and chemical composition. The super-droplet approach features particle-level formulation of condensational (including CCN activation and evaporation) and collisional growth of cloud droplets.
This study will focus on presenting the newly implemented aqueous chemistry module featuring a particle-level representation of aqueous phase SO2 oxidation by H2O2 and O3. The presented scheme will be used in a 0D-parcel and 2D-kinematic framework. The former will allow to test the implementation of the new chemistry module. The latter will mimic a vertical slice of a stratocumulus cloud, and allow to showcase the preliminary results of simulations with both collisions and chemical reactions present.
The described super-droplet scheme and its aqueous phase chemistry extension is packaged as an open-source software library, ready to download and use on either GPU or CPU. Although the library itself was implemented in C++, it was successfully used from models written in Python and FORTRAN. Both the source-code and the documentation are available at http://libcloudphxx.igf.fuw.edu.pl/ .