Multiscale Aerosol Modeling Across Space and Composition

Abstract

Data from ASR field campaigns paint a detailed picture of complex aerosol particle compositions and morphologies (including particle shape, phase, and internal structure). These microscale details are crucial in determining the macroscale aerosol impact on climate, but are challenging to mesh with highly simplified aerosol representations in large-scale models such as E3SM. The objective of this proposal is to harness the capabilities of our unique particle-resolved aerosol model PartMC as an integrating tool between models and measurements across scales. This particle-resolved model explicitly resolves aerosol aging processes that must be approximated in large-scale models, making it ideal for benchmarking and rigorously deriving simplified parameterizations (“coarse graining”) to be used in large-scale models. It also directly connects with laboratory and field measurements at both particle and bulk scales.
Our project builds on three key technologies that are only now available: (1) particle-resolved aerosol models pioneered by the PIs as the combined model system PartMC-MOSAIC, (2) machine learning techniques that are suitable to bridge the gap between detailed process modeling and a large-scale climate model and (3) single-particle measurements in field and laboratory settings from recent DOE ASR field campaigns. Building on previous DOE-funded research, this project will innovate by storing and simulating per-particle morphology in the particle-resolved model PartMC-MOSAIC (Task 1). This morphology data will then allow us to include new state-of-the-art models of organic films and immersion freezing into PartMC, and precisely quantify the effect on CCN and IN predictions (Task 2). These new morphology-based models, together with all other particle-resolved simulation data, will next be used to conduct a comprehensive verification and validation study of MAM4 (the E3SM aerosol component) using WRF-PartMC and data from the CARES and HISCALE field campaigns (Task 3). This will provide unprecedented insight into the approximations being made by global-scale aerosol models such as MAM4, and will guide us in using machine learning to construct both global models of approximation error and new coarse-scale parameterizations of aerosol processes (Task 4).
The outcome of the project is a unified modeling framework that can integrate the particle-level observations and scale them up to quantify their climate impacts. We view the particle-resolved modeling techniques as an integral part of a model hierarchy that connects microscale processes with large-scale models. As such, these techniques serve as an integrating-and-connecting tool: integrating the experimental work ongoing within the ASR community and beyond into the model, and connecting to the large-scale global models that we need for the climate predictions and that are central to the ASR mission.