A Particle-Resolved Model on the Regional Scale to Quantify the Importance of Aerosol Mixing State for CCN Activity

Observational data shows that aerosol populations are complex distributions of particle size and composition. These details are crucial in determining the propensity of particles to form cloud droplets. However, they are challenging to incorporate in regional and global climate models, which rely on highly simplified aerosol representations that introduce structural uncertainty into the model. To quantify errors due to these simplifying assumptions, we developed a detailed particle-resolved aerosol model on the regional scale. This model is fully coupled to the WRF meteorological driver and tracks the size and composition of individual particles as they are emitted and then transformed in the atmosphere by coagulation and condensation. Embedding the particle-resolved aerosol representation in the regional WRF model fully resolves the 3D spatial distribution of aerosol mixing state. In this work, we used this modeling approach to quantify the spatial and temporal distribution of error in CCN concentrations when assuming that the aerosol is internally mixed for the model domain of northern California. Our results indicate that the internal mixture assumption leads to overestimations of CCN concentrations of up to 200%, with larger errors occurring at higher altitudes and at lower supersaturation.