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© 2021. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. 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 (χo), the mixing of primary carbonaceous and non-primary carbonaceous species (χc), and the mixing of hygroscopic and non-hygroscopic species (χh). To achieve a spatiotemporal comparison, we calculated the mixing state indices using output from the Community Earth System Model with the four-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 χ value 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. Our study quantifies potential model bias in simulating mixing state in different regions and provides insights into potential improvements to model process representation for a more realistic simulation of aerosols towards better quantification of radiative forcing and aerosol–cloud interactions.

Details

Title
Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model
Author
Zheng, Zhonghua 1   VIAFID ORCID Logo  ; West, Matthew 2   VIAFID ORCID Logo  ; Zhao, Lei 3   VIAFID ORCID Logo  ; Po-Lun Ma 4   VIAFID ORCID Logo  ; Liu, Xiaohong 5 ; Riemer, Nicole 6   VIAFID ORCID Logo 

 Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA 
 Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA 
 Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA; National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA 
 Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA 
 Department of Atmospheric Sciences, Texas A&M University, College Station, TX, USA 
 Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA 
Pages
17727-17741
Publication year
2021
Publication date
2021
Publisher
Copernicus GmbH
ISSN
16807316
e-ISSN
16807324
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2605599395
Copyright
© 2021. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.