Articles | Volume 22, issue 14
https://doi.org/10.5194/acp-22-9265-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-22-9265-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jul 2022
Research article |
| 19 Jul 2022
| 19 Jul 2022
Quantifying the effects of mixing state on aerosol optical properties
Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Jeffrey H. Curtis
Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Joseph Ching
Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Ibaraki, 305-0052, Japan
National Institute of Polar Research, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8518, Japan
Research Institute for Humanity and Nature, 457-4 Motoyama, Kamigamo, Kita-ku, Kyoto, 603-8047, Japan
now at: Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori, 680-0001, Japan
Zhonghua Zheng
Computational and Information Systems Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Advanced Study Program, National Center for Atmospheric Research, Boulder, CO 80307, USA
Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
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17 citations as recorded by crossref.
- Contrasting nature of aerosols over South Asian cities and its surrounding environment A. Pandey et al. 10.1016/j.envpol.2024.124622
- Microfluidic platform for coupled studies of freezing behavior and final effloresced particle morphology in Snomax ® containing aqueous droplets M. House & C. Dutcher 10.1080/02786826.2023.2233574
- Comparison of scanning aerosol lidar and in situ measurements of aerosol physical properties and boundary layer heights H. Zhang et al. 10.5194/ar-2-135-2024
- Parameterization of polydisperse aerosol optical properties during hygroscopic growth C. Jung et al. 10.1080/02786826.2023.2211121
- An Aerosol Optical Module With Observation‐Constrained Black Carbon Properties for Global Climate Models G. Chen et al. 10.1029/2022MS003501
- Quantifying structural errors in cloud condensation nuclei activity from reduced representation of aerosol size distributions L. Fierce et al. 10.1016/j.jaerosci.2024.106388
- Diverse sources and aging change the mixing state and ice nucleation properties of aerosol particles over the western Pacific and Southern Ocean J. Xue et al. 10.5194/acp-24-7731-2024
- An improved representation of aerosol mixing state for air quality–weather interactions R. Stevens et al. 10.5194/acp-22-13527-2022
- Explicit stochastic advection algorithms for the regional-scale particle-resolved atmospheric aerosol model WRF-PartMC (v1.0) J. Curtis et al. 10.5194/gmd-17-8399-2024
- Molecular Imaging Reveals Two Distinct Mixing States of PM2.5 Particles Sampled in a Typical Beijing Winter Pollution Case Y. Li et al. 10.1021/acs.est.2c08694
- Experimental phase diagram and its temporal evolution for submicron 2-methylglutaric acid and ammonium sulfate aerosol particles Q. Huang et al. 10.1039/D3CP04411D
- Drivers of Cloud Condensation Nuclei in the Eastern North Atlantic as Observed at the ARM Site V. Ghate et al. 10.1029/2023JD038636
- Quantifying evolution of soot mixing state from transboundary transport of biomass burning emissions X. Chen et al. 10.1016/j.isci.2023.108125
- Calculation of Ice Cloud Bulk Scattering Properties Based on Deep Reinforcement Learning K. Chen et al. 10.1109/LGRS.2024.3453654
- Fog/cloud processing of atmospheric aerosols from a single particle perspective: A review of field observations G. Zhang et al. 10.1016/j.atmosenv.2024.120536
- Overestimation of black carbon light absorption due to mixing state heterogeneity L. Zeng et al. 10.1038/s41612-023-00535-8
- MieAI: a neural network for calculating optical properties of internally mixed aerosol in atmospheric models P. Kumar et al. 10.1038/s41612-024-00652-y
Latest update: 11 Dec 2024
Short summary
Investigating the impacts of aerosol mixing state on aerosol optical properties has a long history from both the modeling and experimental perspective. In this study, we used particle-resolved simulations as a benchmark to determine the error in optical properties when using simplified aerosol representations. We found that errors in single scattering albedo due to the internal mixture assumptions can have substantial effects on calculating aerosol direct radiative forcing.
Investigating the impacts of aerosol mixing state on aerosol optical properties has a long...
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