The aerosol mixing state is critical for determining the microphysical interactions of particles with the large-scale atmospheric system. However a tradeoff exists between accurately capturing aerosol mixing state and computational costs which presents a major challenge to regional and global models. Models rely on simplifications of aerosol representation to reduce computational cost, but these assumptions introduce unknown structural uncertainties. To address the issue regarding the importance of representing aersol mixing state and tackle the issue of these unknown unknowns, we coupled the high-detail particle-resolved model PartMC-MOSAIC with the Weather Research and Forecast (WRF) model. This model not only resolves the particle mixing state on a per-particle level but also captures the spatial distribution of aerosol and trace gases of the atmosphere. We applied WRF-PartMC-MOSAIC to simulate the spatial and temporal variations in aerosol size and composition on the regional scale to investigate various mixing state assumptions. First, we apply the mixing state parameter to quantitatively investigate the extent of which particles are internally mixed in the region. To isolate the impact of mixing state assumptions, we project the highly detailed aerosol mixing state into lower dimensional simplified states i.e. full internally or externally mixed aerosols. Under these different mixing state assumptions, cloud properties such as CCN number concentrations and CCN spectra are computed as well as bulk optical properties of absorption and scattering. These bulk errors will be quantified as a function of mixing state to answer where and when mixing state is critical for accurate prediction of climate properties.

Quantifying error in CCN and optical properties under different aerosol mixing state assumptions using a three-dimensional particle-resolved model

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