Assessing Aerosol CCN Structural Uncertainty With a Regional Particle-resolved Model

The aerosol mixing state is critical for determining the microphysical interactions of particles with the large-scale atmospheric system. Despite aerosols presenting a large source of uncertainty, models must choose between accurately capturing mixing state and lower computational costs. To reduce costs, models often assume that all particles within a given size range, or all particles contained in a given mode, have identical composition, i.e., internally mixed subpopulations. These assumptions introduce unknown structural uncertainties. To address this, we coupled the particle-resolved model PartMC-MOSAIC with the regional WRF model. This model resolves complexities in both the aerosol mixing state and the spatial distribution of aerosols. After simulating a regional domain, we then computed the mixing state parameter to quantitatively investigate the extent to which particles are internally mixed. To replicate lower dimensional aerosol representations, we projected the highly detailed aerosol mixing state onto simplified states by reassigning per-particle mass fractions based on those assumptions while conserving size and species-mass distributions. Under these different assumptions, CCN concentrations and CCN spectra were computed. The resulting differences were quantified as a function of mixing state to answer where and when mixing state is critical for the accurate prediction of climate properties.