Atmospheric aerosols are complex mixtures of different chemical species, and individual particles exist in many different shapes and morphologies. Together, these characteristics contribute to the aerosol mixing state. This review provides an overview of measurement techniques to probe aerosol mixing state, discusses how aerosol mixing state is represented in atmospheric models at different scales, and synthesizes our knowledge of aerosol mixing state's impact on climate‐relevant properties, such as cloud condensation and ice nucleating particle concentrations, and aerosol optical properties. We present these findings within a framework that defines aerosol mixing state along with appropriate mixing state metrics to quantify it. Future research directions are identified, with a focus on the need for integrating mixing state measurements and modeling.

Two‐dimensional, Backus‐Gilbert inversion of the EMSLAB land magnetotelluric (MT) data along the 200‐km‐long Lincoln Line has yielded optimally smooth geoelectric sections. Inversions were performed on the apparent resistivity and impedance phase data approximating the transverse magnetic (TM) mode. The land portion of the Lincoln Line traverses the edge of the North American plate that is being underthrust by the Juan de Fuca plate system. The inversion reveals three centralized conductive zones in the depth range of 20–40 km. A slightly conducting (<100 S) zone is centered at 30–35 km depth under the Oregon Coast Range; this feature may be the top of the subducting Juan de Fuca plate since there is complementary evidence here from Consortium for Continental Reflection Profiling seismic data. A prominent conduction zone of several hundred Siemens (S) is also detected at 30–35 km depth under the very resistive (>1000 ohm m) Western Cascades. Here the depth is too shallow for the zone to be the subducting plate. There is also evidence for a highly conducting (>1000 S) lower crust east of the High Cascades on the east end of the Lincoln Line. Two vertical conductive regions are also exposed in the inversion model. One occurs at 70–80 km from the coast under the Willamette Valley where a postulated Eocene trench may have left a suture zone. The second region is coincident with surface hydrothermal activity along the Western‐High Cascades boundary. There are ample sources of water in the crust, e.g., in subducted sediments, from dehydration reactions along the upper plate boundary, and in volcanic arc magmas, to lead us to believe that hot, saline water is the major source of the conductive occurrences along the Lincoln Line. However, the various zones appear to be distinct, and the water may be trapped by different mechanisms.

Abstract. The PartMC-MOSAIC particle-resolved aerosol model was previously developed to predict the aerosol mixing state as it evolves in the atmosphere. However, the modeling framework was limited to a zero-dimensional box model approach without resolving spatial gradients in aerosol concentrations. This paper presents the development of stochastic particle methods to simulate turbulent diffusion and dry deposition of aerosol particles in a vertical column within the planetary boundary layer. The new model, WRF-PartMC-MOSAIC-SCM, resolves the vertical distribution of aerosol mixing state. We verified the new algorithms with analytical solutions for idealized test cases and illustrate the capabilities with results from a 2-day urban scenario that shows the evolution of black carbon mixing state in a vertical column.

Abstract. Calculations of the aerosol direct effect on climate rely on simulated aerosol fields. The model representation of aerosol mixing state potentially introduces large uncertainties into these calculations, since the simulated aerosol optical properties are sensitive to mixing state. In this study, we systematically quantified the impact of aerosol mixing state on aerosol optical properties using an ensemble of 1800 aerosol populations from particle-resolved simulations as a basis for Mie calculations for optical properties. Assuming the aerosol to be internally mixed within prescribed size bins caused overestimations of aerosol absorptivity and underestimations of aerosol scattering. Together, these led to errors in the populations' single scattering albedo of up to −22.3 % with a median of −0.9 %. The mixing state metric χ proved useful in relating errors in the volume absorption coefficient, the volume scattering coefficient and the single scattering albedo to the degree of internally mixing of the aerosol, with larger errors being associated with more external mixtures. At the same time, a range of errors existed for any given value of χ. We attributed this range to the extent to which the internal mixture assumption distorted the particles' black carbon content and the refractive index of the particle coatings. Both can vary for populations with the same value of χ. These results are further evidence of the important yet complicated role of mixing state in calculating aerosol optical properties.

This study integrates machine learning and particle‐resolved aerosol simulations to develop emulators that predict submicron aerosol mixing state indices from the Earth system model (ESM) simulations. The emulators predict aerosol mixing state using only quantities that are predicted by the ESM, including bulk aerosol species concentrations, which do not by themselves carry mixing state information. We used PartMC‐MOSAIC as the particle‐resolved model and NCAR's CESM as the ESM. We trained emulators for three different mixing state indices for submicron aerosol in terms of chemical species abundance (χa), the mixing of optically absorbing and nonabsorbing species (χo), and the mixing of hygroscopic and nonhygroscopic species (χh). Our global mixing state maps show considerable spatial and seasonal variability unique to each mixing state index. Seasonal averages varied spatially between 13% and 94% for χa, between 38% and 94% for χo, and between 20% and 87% for χh with global annual averages of 67%, 68%, and 56%, respectively. High values in one index can be consistent with low values in another index depending on the grouping of species and their relative abundance, meaning that each mixing state index captures different aspects of the population mixing state. Although a direct validation with observational data has not been possible yet, our results are consistent with mixing state index values derived from ambient observations. This work is a prototypical example of using machine learning emulators to add information to ESM simulations.