Articles | Volume 21, issue 23
https://doi.org/10.5194/acp-21-17727-2021
https://doi.org/10.5194/acp-21-17727-2021
Research article
 | 
03 Dec 2021
Research article |  | 03 Dec 2021

Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

Zhonghua Zheng, Matthew West, Lei Zhao, Po-Lun Ma, Xiaohong Liu, and Nicole Riemer

Related authors

Creating a national urban flood dataset for China from news texts (2000–2022) at the county level
Shengnan Fu, David M. Schultz, Heng Lyu, Zhonghua Zheng, and Chi Zhang
Hydrol. Earth Syst. Sci., 29, 767–783, https://doi.org/10.5194/hess-29-767-2025,https://doi.org/10.5194/hess-29-767-2025, 2025
Short summary
Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev., 16, 6001–6028, https://doi.org/10.5194/gmd-16-6001-2023,https://doi.org/10.5194/gmd-16-6001-2023, 2023
Short summary
A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023,https://doi.org/10.5194/gmd-16-4017-2023, 2023
Short summary
Quantifying the effects of mixing state on aerosol optical properties
Yu Yao, Jeffrey H. Curtis, Joseph Ching, Zhonghua Zheng, and Nicole Riemer
Atmos. Chem. Phys., 22, 9265–9282, https://doi.org/10.5194/acp-22-9265-2022,https://doi.org/10.5194/acp-22-9265-2022, 2022
Short summary

Related subject area

Subject: Aerosols | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
Opinion: The role of AerChemMIP in advancing climate and air quality research
Paul T. Griffiths, Laura J. Wilcox, Robert J. Allen, Vaishali Naik, Fiona M. O'Connor, Michael Prather, Alex Archibald, Florence Brown, Makoto Deushi, William Collins, Stephanie Fiedler, Naga Oshima, Lee T. Murray, Bjørn H. Samset, Chris Smith, Steven Turnock, Duncan Watson-Parris, and Paul J. Young
Atmos. Chem. Phys., 25, 8289–8328, https://doi.org/10.5194/acp-25-8289-2025,https://doi.org/10.5194/acp-25-8289-2025, 2025
Short summary
Uncertainties in the effects of organic aerosol coatings on polycyclic aromatic hydrocarbon concentrations and their estimated health effects
Sijia Lou, Manish Shrivastava, Alexandre Albinet, Sophie Tomaz, Deepchandra Srivastava, Olivier Favez, Huizhong Shen, and Aijun Ding
Atmos. Chem. Phys., 25, 8163–8183, https://doi.org/10.5194/acp-25-8163-2025,https://doi.org/10.5194/acp-25-8163-2025, 2025
Short summary
Source-explicit estimation of brown carbon in the polluted atmosphere over the North China Plain: implications for distribution, absorption, and the direct radiative effect
Jiamao Zhou, Jiarui Wu, Xiaoli Su, Ruonan Wang, Imad EI Haddad, Xia Li, Qian Jiang, Ting Zhang, Wenting Dai, Junji Cao, Andre S. H. Prevot, Xuexi Tie, and Guohui Li
Atmos. Chem. Phys., 25, 7563–7580, https://doi.org/10.5194/acp-25-7563-2025,https://doi.org/10.5194/acp-25-7563-2025, 2025
Short summary
Implications of reduced-complexity aerosol thermodynamics on organic aerosol mass concentration and composition over North America
Camilo Serrano Damha, Kyle Gorkowski, and Andreas Zuend
Atmos. Chem. Phys., 25, 5773–5792, https://doi.org/10.5194/acp-25-5773-2025,https://doi.org/10.5194/acp-25-5773-2025, 2025
Short summary
Trends and drivers of soluble iron deposition from East Asian dust to the Northwest Pacific: a springtime analysis (2001–2017)
Hanzheng Zhu, Yaman Liu, Man Yue, Shihui Feng, Pingqing Fu, Kan Huang, Xinyi Dong, and Minghuai Wang
Atmos. Chem. Phys., 25, 5175–5197, https://doi.org/10.5194/acp-25-5175-2025,https://doi.org/10.5194/acp-25-5175-2025, 2025
Short summary

Cited articles

Asmi, A., Wiedensohler, A., Laj, P., Fjaeraa, A.-M., Sellegri, K., Birmili, W., Weingartner, E., Baltensperger, U., Zdimal, V., Zikova, N., Putaud, J.-P., Marinoni, A., Tunved, P., Hansson, H.-C., Fiebig, M., Kivekäs, N., Lihavainen, H., Asmi, E., Ulevicius, V., Aalto, P. P., Swietlicki, E., Kristensson, A., Mihalopoulos, N., Kalivitis, N., Kalapov, I., Kiss, G., de Leeuw, G., Henzing, B., Harrison, R. M., Beddows, D., O'Dowd, C., Jennings, S. G., Flentje, H., Weinhold, K., Meinhardt, F., Ries, L., and Kulmala, M.: Number size distributions and seasonality of submicron particles in Europe 2008–2009, Atmos. Chem. Phys., 11, 5505–5538, https://doi.org/10.5194/acp-11-5505-2011, 2011. a
Bauer, S. E., Wright, D. L., Koch, D., Lewis, E. R., McGraw, R., Chang, L.-S., Schwartz, S. E., and Ruedy, R.: MATRIX (Multiconfiguration Aerosol TRacker of mIXing state): an aerosol microphysical module for global atmospheric models, Atmos. Chem. Phys., 8, 6003–6035, https://doi.org/10.5194/acp-8-6003-2008, 2008. a
Binkowski, F. S. and Shankar, U.: The Regional Particulate Matter Model: 1. Model Description and Preliminary Results, J. Geophys. Res.-Atmos., 100, 26191–26209, https://doi.org/10.1029/95JD02093, 1995. a
Bogenschutz, P. A., Gettelman, A., Hannay, C., Larson, V. E., Neale, R. B., Craig, C., and Chen, C.-C.: The path to CAM6: coupled simulations with CAM5.4 and CAM5.5, Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018, 2018. a
Bondy, A. L., Bonanno, D., Moffet, R. C., Wang, B., Laskin, A., and Ault, A. P.: The diverse chemical mixing state of aerosol particles in the southeastern United States, Atmos. Chem. Phys., 18, 12595–12612, https://doi.org/10.5194/acp-18-12595-2018, 2018. a
Download
Short summary
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. We present a framework for evaluating the error in aerosol mixing state induced by aerosol representation assumptions, which is one of the important contributors to structural uncertainty in aerosol models. Our study provides insights into potential improvements to model process representation for aerosols.
Share
Altmetrics
Final-revised paper
Preprint