Articles | Volume 17, issue 12
https://doi.org/10.5194/acp-17-7445-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-17-7445-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
21 Jun 2017
Research article |
| 21 Jun 2017
Metrics to quantify the importance of mixing state for CCN activity
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
now at: Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, Tsukuba 305-0052, Japan
Jerome Fast
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
Matthew West
Department of Mechanical Science and Engineering, University of Illinois at Urbana–Champaign, 1206 W. Green St., Urbana, IL 61801, USA
Nicole Riemer
Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, 105 S Gregory St., Urbana, IL 61801, USA
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34 citations as recorded by crossref.
- The impact of aerosol size-dependent hygroscopicity and mixing state on the cloud condensation nuclei potential over the north-east Atlantic W. Xu et al. 10.5194/acp-21-8655-2021
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- A Closure Study of Total Scattering Using Airborne In Situ Measurements from the Winter Phase of TCAP E. Kassianov et al. 10.3390/atmos9060228
- Quantifying errors in the aerosol mixing-state index based on limited particle sample size J. Gasparik et al. 10.1080/02786826.2020.1804523
- Method to quantify black carbon aerosol light absorption enhancement with a mixing state index G. Zhao et al. 10.5194/acp-21-18055-2021
- Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model Z. Zheng et al. 10.5194/acp-21-17727-2021
- Machine Learning to Predict the Global Distribution of Aerosol Mixing State Metrics M. Hughes et al. 10.3390/atmos9010015
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- 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
- Aerosol mixing state revealed by transmission electron microscopy pertaining to cloud formation and human airway deposition J. Ching et al. 10.1038/s41612-019-0081-9
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- Enhanced Aging of Black Carbon under Recent Clean Air Actions and Future Carbon Neutrality Scenario in China W. Shen et al. 10.1021/acs.est.4c02030
- Comparison of three aerosol representations of NHM-Chem (v1.0) for the simulations of air quality and climate-relevant variables M. Kajino et al. 10.5194/gmd-14-2235-2021
- Aerosol mixing state matters for particles deposition in human respiratory system J. Ching & M. Kajino 10.1038/s41598-018-27156-z
- Estimating Submicron Aerosol Mixing State at the Global Scale With Machine Learning and Earth System Modeling Z. Zheng et al. 10.1029/2020EA001500
- Quantifying Impacts of Aerosol Mixing State on Nucleation-Scavenging of Black Carbon Aerosol Particles J. Ching et al. 10.3390/atmos9010017
- Aerosol Mixing State: Measurements, Modeling, and Impacts N. Riemer et al. 10.1029/2018RG000615
- Effects of Aerosols on the Precipitation of Convective Clouds: A Case Study in the Yangtze River Delta of China C. Liu et al. 10.1029/2018JD029924
- 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
- Large spatiotemporal variability in aerosol properties over central Argentina during the CACTI field campaign J. Fast et al. 10.5194/acp-24-13477-2024
- Evaluating the Impacts of Cloud Processing on Resuspended Aerosol Particles After Cloud Evaporation Using a Particle‐Resolved Model Y. Yao et al. 10.1029/2021JD034992
- The state of the aerosol: Musings on mixing state N. Riemer & M. West 10.1080/02786826.2024.2346220
- Influences of Primary Emission and Secondary Coating Formation on the Particle Diversity and Mixing State of Black Carbon Particles A. Lee et al. 10.1021/acs.est.9b03064
- A Review of the Representation of Aerosol Mixing State in Atmospheric Models R. Stevens & A. Dastoor 10.3390/atmos10040168
- Impacts of variations in aerosol refractive index on the retrieving of the light-absorption and hygroscopicity of ambient black carbon-containing aerosols using SP2 G. Zhao et al. 10.1016/j.atmosenv.2024.120359
- Particle phase-state variability in the North Atlantic free troposphere during summertime is determined by atmospheric transport patterns and sources Z. Cheng et al. 10.5194/acp-22-9033-2022
- Markedly different impacts of primary emissions and secondary aerosol formation on aerosol mixing states revealed by simultaneous measurements of CCNC, H(/V)TDMA, and SP2 J. Tao et al. 10.5194/acp-24-9131-2024
- Aircraft measurements of single particle size and composition reveal aerosol size and mixing state dictate their activation into cloud droplets G. Saliba et al. 10.1039/D3EA00052D
- Quantifying the effects of mixing state on aerosol optical properties Y. Yao et al. 10.5194/acp-22-9265-2022
- Technical note: Mismeasurement of the core-shell structure of black carbon-containing ambient aerosols by SP2 measurements G. Zhao et al. 10.1016/j.atmosenv.2020.117885
- Cloud Condensation Nuclei Closure Study Using Airborne Measurements Over the Southern Great Plains G. Kulkarni et al. 10.1029/2022JD037964
- Spatial Variability of Sources and Mixing State of Atmospheric Particles in a Metropolitan Area Q. Ye et al. 10.1021/acs.est.8b01011
- Remote sensing of aerosol water fraction, dry size distribution and soluble fraction using multi-angle, multi-spectral polarimetry B. van Diedenhoven et al. 10.5194/amt-15-7411-2022
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Latest update: 14 Dec 2024
Short summary
The composition of individual aerosols affects their cloud condensation nuclei (CCN) properties, but is challenging to represent in models. This study quantifies the error in CCN calculations when per-particle information is neglected by using a metric for the composition diversity within a population. With more particle-level measurements from field campaigns, the approach is useful for quantifying uncertainties in composition-dependent quantities regarding aerosol–cloud–climate interactions.
The composition of individual aerosols affects their cloud condensation nuclei (CCN) properties,...
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