Particle-Resolved Modeling: A Bridge between Scales in Aerosol Science

NICOLE RIEMER, University of Illinois at Urbana-Champaign

     Abstract Number: 462
     Working Group: Plenary Lecture Invited by Conference Chair

Abstract
Atmospheric aerosols are mixtures of different chemical species, and individual particles exist in many different shapes and morphologies. This “aerosol state” continuously evolves in the atmosphere. Thanks to advances in measurement techniques, we have made great progress in our process-level understanding of the atmospheric aerosol. Why is it then that aerosols and aerosol-cloud interactions are still associated with the largest uncertainties in global climate predictions? A key reason for this is the inherent multiscale nature of the problem—processes on the micro-scale determine macro-scale impacts—and the challenges that this poses for our modeling efforts. In this presentation I will show how high-detail particle-resolved modeling fills an important gap in the hierarchy of aerosol models. The particle-resolved approach represents the atmospheric aerosol using individual computational particles that evolve in size and composition as they undergo transformation processes in the atmosphere. While computationally expensive, this approach is therefore not limited by assumptions about particle composition within a given size range. As a result, it can represent the evolution of the full aerosol mixing state without simplifying assumptions. My presentation will illustrate how particle-resolved modeling can provide insights into the spatio-temporal evolution of aerosol mixing state, going beyond the traditional definitions of “externally” or “internally” mixed populations. I will show how simplifying the diversity of aerosol composition introduces errors in our estimates of climate-relevant properties, such as cloud condensation nuclei concentration and aerosol optical properties. I’ll conclude the presentation by summarizing the measurement challenges that we face in constraining particle-resolved models, but which provide a unique opportunity in “getting the right answer for the right reasons”.