Atmospheric aerosols are intricate mixtures of chemical species, with individual particles exhibiting diverse compositions, shapes, and morphologies. This evolving "aerosol state" plays a crucial role in atmospheric processes, yet remains one of the largest sources of uncertainty in global climate predictions. Despite advances in measurement techniques and process-level understanding, capturing the full complexity of aerosol-cloud interactions remains a major challenge.
A key difficulty arises from the multiscale nature of the problem: microscopic processes drive macroscopic climate impacts, complicating efforts to accurately model aerosol dynamics. In this presentation, I will demonstrate how particle-resolved modeling helps bridge this gap by explicitly tracking the size and composition of individual atmospheric particles as they undergo transformations. Unlike traditional modeling approaches, which rely on simplifying assumptions about aerosol composition, this method provides a detailed representation of mixing state evolution, capturing spatio-temporal variations more realistically.
I will discuss how this approach challenges conventional classifications of aerosols as purely “externally” or “internally” mixed, revealing the dynamic and continuous nature of mixing. Furthermore, I will illustrate how neglecting aerosol diversity leads to significant errors in predicting key climate-relevant properties, such as cloud condensation nuclei and ice nuclei concentrations and aerosol optical effects. Finally, I will address the measurement challenges in validating particle-resolved models and highlight their potential to improve climate predictions by ensuring we "get the right answer for the right reasons."