A 3D Particle-resolved Model to Quantify the Importance of Aerosol Mixing State for CCN Activity

Description

Here we present a 3D particle-resolved aerosol model to investigate the importance of aerosol mixing state in regional models. Understanding the aerosol mixing state impact on aerosol physical and optical properties and its temporal and spatial evolution is currently an open research question. Due to computational constraints, representing aerosol composition in numerical models has been a challenge, and as a result, both modal and sectional representations overly simplify the aerosol mixing state. This leads to uncertainties and errors in physical quantities that are not well understood. To address this, we coupled the Weather Research and Forecast (WRF) model and the particle-resolving aerosol physics and chemistry model PartMC-MOSAIC. The new model explicitly resolves and tracks the size and composition of individual particles as they undergo transformations by coagulation and condensation in the atmosphere and simulates stochastic particle transport between grid cells using velocity and turbulent mixing fields provided by the WRF model. We apply this model to a 3D scenario to examine how the aerosol mixing state evolves spatially and temporally. To contrast the results with traditional aerosol representation, a composition-averaging technique is used. This technique simplifies the highly detailed per-particle composition to a level of detail contained within lower detailed sectional models. To quantify the importance of mixing state for prediction of cloud condensation nuclei (CCN) properties, we compare CCN concentrations of particle-resolved simulations to a composition-averaged simulation. We present the differences in CCN concentrations between the two representations as a function of mixing state parameter chi, a metric where values range from 0 – reflecting a fully externally mixed particle population – to 1, reflecting a fully internally mixed particle population. At low supersaturation thresholds (0.1%) the error in CCN concentration is small, independent of the mixing state. At higher supersaturation thresholds, the error is small for populations with chi > 0.6. The largest errors occur below chi = 0.2 (up to 150%), while the errors in the intermediate regime of 0.2 < chi < 0.6 amount to up to about 40%. We also encounter some cases with small errors for chi < 0.6. In those cases the overestimation and underestimation of CCN concentration compensate each other.