Articles | Volume 21, issue 23
Atmos. Chem. Phys., 21, 17727–17741, 2021

Special issue: Particle-based methods for simulating atmospheric aerosol...

Atmos. Chem. Phys., 21, 17727–17741, 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 et al.

Related authors

Quantifying the effects of mixing state on aerosol optical properties
Yu Yao, Jeffrey Curtis, Joseph Ching, Zhonghua Zheng, and Nicole Riemer
Atmos. Chem. Phys. Discuss.,,, 2022
Preprint under review for ACP
Short summary

Related subject area

Subject: Aerosols | Research Activity: Atmospheric Modelling | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
Molecular-level nucleation mechanism of iodic acid and methanesulfonic acid
An Ning, Ling Liu, Lin Ji, and Xiuhui Zhang
Atmos. Chem. Phys., 22, 6103–6114,,, 2022
Short summary
Estimation of secondary PM2.5 in China and the United States using a multi-tracer approach
Haoran Zhang, Nan Li, Keqin Tang, Hong Liao, Chong Shi, Cheng Huang, Hongli Wang, Song Guo, Min Hu, Xinlei Ge, Mindong Chen, Zhenxin Liu, Huan Yu, and Jianlin Hu
Atmos. Chem. Phys., 22, 5495–5514,,, 2022
Short summary
Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality
Chao Gao, Aijun Xiu, Xuelei Zhang, Qingqing Tong, Hongmei Zhao, Shichun Zhang, Guangyi Yang, and Mengduo Zhang
Atmos. Chem. Phys., 22, 5265–5329,,, 2022
Short summary
OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) sea spray organic aerosol emissions – implementation in a global climate model and impacts on clouds
Susannah M. Burrows, Richard C. Easter, Xiaohong Liu, Po-Lun Ma, Hailong Wang, Scott M. Elliott, Balwinder Singh, Kai Zhang, and Philip J. Rasch
Atmos. Chem. Phys., 22, 5223–5251,,, 2022
Short summary
The pathway of impacts of aerosol direct effects on secondary inorganic aerosol formation
Jiandong Wang, Jia Xing, Shuxiao Wang, Rohit Mathur, Jiaping Wang, Yuqiang Zhang, Chao Liu, Jonathan Pleim, Dian Ding, Xing Chang, Jingkun Jiang, Peng Zhao, Shovan Kumar Sahu, Yuzhi Jin, David C. Wong, and Jiming Hao
Atmos. Chem. Phys., 22, 5147–5156,,, 2022
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,, 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,, 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,, 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,, 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,, 2018. a
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.
Final-revised paper