Breakout Summary Report

Aerosol impacts on climate are closely tied to their size distribution and mixing state. Over the past decade, significant progress has been made in characterizing aerosol diversity through recent ARM field campaigns and laboratory experiments. We are now at the point where single-particle data are increasingly accessible, computational power supports mixing-state-aware models, and advanced analytical methods, including machine learning, enable the processing of large, high-dimensional datasets to reveal population-level properties and processes. This session aims to bring together experimentalists and modelers to explore approaches, progress, and challenges related to aerosol mixing state.

Expected outcomes:

We will identify recent progress, new opportunities, and potential barriers to (1) quantifying aerosol mixing state, (2) linking single-particle measurement methods to each other, to CCN and INP measurements, and to simulated mixing state information, and (3) integrating this knowledge into models across various scales.

This breakout session focused on identifying key challenges and research needs related to aerosol mixing state, with an emphasis on how experimental observations can better inform modeling efforts. The discussion brought together diverse perspectives on measurement capabilities, model requirements, and the design of future field and laboratory campaigns. Particular attention was given to the level of detail needed to characterize aerosols, the importance of size and physical properties, and the unique considerations for ice nucleating particles (INPs).

We had three invited speakers, Swarup China(PNNL), Rachel O’Brien (University of Michigan), Susannah Burrows, (PNNL) who gave overview presentations on challenges and progress regarding modeling and measuring particle mixing states and the integration of observations and measurement. We had ample discussion after each talk, and more open discussion after the presentations, as detailed below.

Main Discussion Points

(1) Level of detail for mixing state: Participants discussed the need to determine the appropriate level of experimental detail required to represent aerosol mixing state in models. Striking a balance between measurement capabilities and model relevance was emphasized. Aerosol size was highlighted as a critical factor. There is a strong need for size-resolved, single-particle composition measurements resolving external and internal mixing states to improve model representation.

(2) Need for high particle number counts: High number concentrations are essential for statistically meaningful analysis, particularly for studies that aim to resolve population-level variability.

(3) Physical property characterization: Participants discussed the fact that aerosol physical properties (e.g., coatings, phase state, morphology) are needed to link mixing state to climate-relevant processes, i.e. formation of clouds and interaction with sunlight

(4) INP-specific considerations: For ice nucleating particles (INPs), it was noted that their rarity may necessitate a different approach to thinking about mixing state. Traditional frameworks may not apply, and physical properties and coatings may play a more dominant role.

(5) Lab vs. field campaigns: The group discussed the merits of laboratory versus field campaigns. There was general support for lab-based campaigns, which could provide greater control over aerosol properties and enable targeted exploration of mixing state effects.

(6) Representative aerosol ensembles: Ideas were exchanged on how to design and generate a representative ensemble of aerosol populations for chamber studies. This included considerations for both general aerosol populations and INPs.

(7) Closure studies and existing data: Questions were raised about the feasibility of closure studies using existing data (e.g., from SGP) and whether current observational datasets provide sufficient constraints, especially for INP-related closure.

The level of detail needed to represent aerosol mixing state must be guided by model relevance. A central challenge is determining how much experimental complexity is necessary to meaningfully inform models. This includes not only particle composition, but also size-resolved data, internal heterogeneity of individual particles, corresponding impact on physical properties, and the time-evolving nature of mixing state.

Improved experimental design and measurement strategies are needed to characterize realistic aerosol populations. Current approaches often lack sufficient particle number counts, size resolution, or coverage of critical physical properties. There's a clear need for more representative, well-characterized aerosol ensembles—particularly for lab-based studies.

Special consideration is needed for rare particle types such as ice nucleating particles (INPs). Traditional mixing state frameworks may not apply to INPs due to their low abundance and sensitivity to specific particle properties, indicating the need for tailored measurement and modeling strategies.

Laboratory campaigns offer a practical path for advancing mixing state research. Compared to field campaigns, controlled lab studies provide the opportunity to systematically explore aerosol evolution, closure, and model–measurement integration using carefully generated particle populations.

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(S: Short term, L: Long term)

Modeling and Conceptual Needs

● (S) Model-informed experimental design Facilitate coordination between modelers and experimentalists to define the necessary level of detail for mixing state representation in models—what properties are essential vs. nice-to-have?

● (L) Dynamic frameworks for mixing state representation Advance beyond static assumptions of internal/external mixtures by developing tools and metrics to describe how mixing state evolves over time and under different atmospheric conditions.

● (L) Tailored modeling approaches for INPs Develop new conceptual and numerical models that address the distinct behavior and influence of INPs, beyond bulk aerosol population frameworks.

Community Coordination and Infrastructure

● (S) Design and execute a representative lab campaign Plan a controlled, community-supported laboratory study to explore aerosol mixing state under defined conditions, with well-characterized particle populations relevant to climate and cloud processes.

● (S) Shared protocols for generating representative aerosol populations Establish community-agreed methods for chamber studies, including generation, aging, and classification of particles to ensure reproducibility and comparability across labs.

● (L) Review of existing field datasets for closure studies Assess whether current field data (e.g., from ARM sites like SGP) are sufficient for mixing state and INP-related model–measurement closure, and define data needs for future campaigns.

Measurement Needs

● (S) Enhanced size-resolved and single-particle measurements Improve capabilities to measure aerosol composition, size distribution, morphology, and phase state at the individual particle level, especially for atmospherically relevant size ranges.

● (S) Higher particle number concentrations for statistical robustness Ensure instrumentation and sampling strategies capture sufficient particle counts, particularly important for rare species like INPs.

● (L) Specialized INP detection methods and characterization Develop and deploy improved techniques for detecting and characterizing INPs, recognizing that standard aerosol measurement methods may not suffice due to their low concentrations and unique properties.

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Develop clear guidelines for the level of experimental detail needed to inform models. A collaborative effort between experimentalists and modelers is needed to define what constitutes "actionable" mixing state data, balancing realism and feasibility.

Explore frameworks for representing time-evolving mixing state in models. Modeling efforts should move toward incorporating dynamic representations of mixing state rather than assuming fixed internal or external mixtures.

Design and execute a targeted laboratory campaign focused on aerosol mixing state. A controlled, multi-institution lab campaign could systematically investigate key parameters, test closure approaches, and generate benchmark datasets.

Define protocols for generating representative aerosol populations in chamber studies. Agreeing on target mixtures, size distributions, and aging conditions would facilitate reproducible studies and strengthen comparisons across platforms.

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