Aerosol mixing state evolution in the atmosphere: A synthesis of measurements and models

Quantifying aerosol impacts on climate is an inherently multiscale problem since macro-scale impacts are determined by processes on the micro-scale. This poses unique modeling challenges, since these microscale processes lead to a continuously evolving aerosol mixing state, which is difficult to represent in large-scale models. This presentation will show how high-detail particle-resolved simulations can be used to predict the evolving aerosol mixing state on the regional scale. In contrast to traditional aerosol models that use bins or modes to represent the aerosol, the particle-resolved approach uses individual computational particles that evolve in size and composition as the particles undergo aging processes in the atmosphere. This approach is therefore not limited by assumptions about particle composition within a given size range and can represent the full aerosol mixing state without simplifying assumptions. I will show results that illustrate the spatio-temporal evolution of aerosol mixing state, going beyond the traditional definitions of “externally” or “internally” mixed populations. I will conclude with a framework to synthesize a picture of the ambient aerosol from models and observations. This focuses on suitable metrics to quantify mixing state and sampling strategies to determine these metrics that are accessible for both models and observations. Together, these provide a unique opportunity for “getting the right answer for the right reasons”.