Articles | Volume 26, issue 14
https://doi.org/10.5194/acp-26-10647-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-10647-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jul 2026
Research article |
| 30 Jul 2026
| 30 Jul 2026
Compensating biases in CCN predictions from composition averaging and neglected surfactant effects
Xiaotian Xu
Department of Climate, Meteorology & Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Jeffrey H. Curtis
Department of Climate, Meteorology & Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Matthew West
Department of Mechanical Science & Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Department of Climate, Meteorology & Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
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Short summary
This study examines how common simplifications in atmospheric models affect predictions of particles that form cloud droplets. Using a detailed simulation that tracks individual particles, we compare it with simplified approaches. We find that these simplifications can produce seemingly accurate results because different errors cancel each other. However, this masks incorrect physical processes. Including surfactants effect improves predictions, highlighting a pathway to better climate modeling.
This study examines how common simplifications in atmospheric models affect predictions of...
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