Abstract

Atmospheric aerosols are evolving mixtures of chemical species. In global climate models (GCMs), this “aerosol mixing state” is represented in a highly simplified manner. This can introduce errors in the estimates of climate-relevant aerosol properties, such as the concentration of cloud condensation nuclei. The goal for this study is to determine a global spatial distribution of aerosol mixing state with respect to hygroscopicity, as quantified by the mixing state metric $\chi$ . In this way, areas can be identified where the external or internal mixture assumption is more appropriate. We used the output of a large ensemble of particle-resolved box model simulations in conjunction with machine learning techniques to train a model of the mixing state metric $\chi$ . This lower-order model for $\chi$ uses as inputs only variables known to GCMs, enabling us to create a global map of $\chi$ based on GCM data. We found that $\chi$ varied between 20% and nearly 100%, and we quantified how this depended on particle diameter, location, and time of the year. This framework demonstrates how machine learning can be applied to bridge the gap between detailed process modeling and a large-scale climate model. View Full-Text