Tackling structural uncertainty in aerosol models



West, Matthew — University of Illinois at Urbana-Champaign
Riemer, Nicole — University of Illinois at Urbana-Champaign

Area of research:

Aerosol Processes

Journal Reference:

Zheng Z, M West, L Zhao, P Ma, X Liu, and N Riemer. 2021. "Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model." Atmospheric Chemistry and Physics, 21(23), 10.5194/acp-21-17727-2021.


The aerosol mixing state predicted by a global climate model differs in important structural ways from the one estimated by a high-detail aerosol model. The aerosol mixing state describes the distribution of chemical species across particles in the atmosphere and is important for predicting aerosol-climate interactions.


Understanding aerosol-climate interactions is challenging because they depend on detailed properties of microscale aerosol particles. To simulate aerosols, global climate models necessarily make simplifying assumptions about the aerosol structure and mixing state, which result in difficult-to-quantify “structural uncertainty” in predictions. Our work presents a new methodology for attempting to quantify these uncertainties.


This study aims to verify the global distribution of aerosol mixing state represented by modal models. To quantify the aerosol mixing state, we used the aerosol mixing state indices for submicron aerosol based on the mixing of optically absorbing and non-absorbing species, the mixing of primary carbonaceous and non-primary carbonaceous species, and the mixing of hygroscopic and non-hygroscopic species. To achieve a spatiotemporal comparison, we calculated the mixing state indices using output from the Community Earth System Model with the 4-mode version of the Modal Aerosol Module (MAM4), and compared the results with the mixing state indices from a benchmark machine-learned model trained on high-detail, particle-resolved simulations from the particle-resolved stochastic aerosol model PartMC-MOSAIC. The two methods yielded very different spatial patterns of the mixing state indices. In some regions, the yearly averaged mixing state index computed by the MAM4 model differed by up to 70 percentage points from the benchmark values. These errors tended to be zonally structured, with the MAM4 model predicting a more internally mixed aerosol at low latitudes, and a more externally mixed aerosol at high latitudes, compared to the benchmark.