Preprints
https://doi.org/10.5194/egusphere-2025-4326
https://doi.org/10.5194/egusphere-2025-4326
24 Sep 2025
 | 24 Sep 2025
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

The impact of aerosol mixing state on immersion freezing: Insights from classical nucleation theory and particle-resolved simulations

Wenhan Tang, Sylwester Arabas, Jeffrey H. Curtis, Daniel A. Knopf, Matthew West, and Nicole Riemer

Abstract. Immersion freezing, initiated by ice-nucleating particles (INPs) in supercooled aqueous droplets, plays an important role in the formation of ice crystals within clouds. The efficiency of immersion freezing depends strongly on INP composition and, crucially, on the mixing state—how chemical species are distributed across the particle population. Here, we quantify the impact of aerosol mixing state on immersion freezing using a combined theoretical and particle-resolved modeling approach. We derive analytical expressions for the frozen fraction of internally and externally mixed INP populations based on classical nucleation theory, showing that the frozen fraction is sensitive to whether ice-active species are present in all particles or only in a subset of the population. We introduce a multi-species immersion freezing scheme into the particle-resolved model PartMC, using the water activity-based immersion freezing model (ABIFM) to compute freezing probabilities for mixed-composition particles. To improve computational efficiency, we implement a Binned Tau-Leaping algorithm and demonstrate an order-of-magnitude speedup with minimal accuracy loss. Simulations confirm the theoretical prediction that internally mixed populations yield higher frozen fractions than externally mixed ones under otherwise identical conditions. Sensitivity analyses across particle size, species type, and cooling condition reveal that the mixing state effect is most pronounced when small amounts of highly efficient INPs are mixed with less efficient materials. These findings underscore the need to represent aerosol mixing state explicitly in models of heterogeneous ice nucleation to reduce uncertainty in cloud-phase partitioning.

Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.

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Wenhan Tang, Sylwester Arabas, Jeffrey H. Curtis, Daniel A. Knopf, Matthew West, and Nicole Riemer

Status: open (until 05 Nov 2025)

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Wenhan Tang, Sylwester Arabas, Jeffrey H. Curtis, Daniel A. Knopf, Matthew West, and Nicole Riemer

Data sets

Data for: The impact of aerosol mixing state on immersion freezing: Insights from classical nucleation theory and particle-resolved simulations W. Tang et al. https://doi.org/10.13012/B2IDB-6849781_V1

Model code and software

PartMC: Particle-resolved Monte Carlo code for atmospheric aerosol simulation (Version 2.8.0) M. West et al. https://github.com/tangwhiap/partmc/tree/imf

Interactive computing environment

Jupyter notebook for: The impact of aerosol mixing state on immersion freezing: Insights from classical nucleation theory and particle-resolved simulations W. Tang et al. https://github.com/open-atmos/PyPartMC/blob/main/examples/immersion_freezing.ipynb

Wenhan Tang, Sylwester Arabas, Jeffrey H. Curtis, Daniel A. Knopf, Matthew West, and Nicole Riemer

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Short summary
We studied how aerosol particles help form ice in clouds. Using new theory and detailed computer simulations, we found that the way different materials are mixed within these particles has a strong impact on how much ice forms. When ice-forming material is spread across all particles, more droplets freeze than when it is only in a few. This result means that to better predict clouds and climate, models need to account for how particle materials are mixed.
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