Articles | Volume 25, issue 13
https://doi.org/10.5194/acp-25-7007-2025
https://doi.org/10.5194/acp-25-7007-2025
Research article
 | 
10 Jul 2025
Research article |  | 10 Jul 2025

Aerosol–cloud interactions in cirrus clouds based on global-scale airborne observations and machine learning models

Derek Ngo, Minghui Diao, Ryan J. Patnaude, Sarah Woods, and Glenn Diskin

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Cited articles

Baker, B. A. and Lawson, R. P.: In Situ Observations of the Microphysical Properties of Wave, Cirrus and Anvil Clouds. Part I: Wave Clouds, J. Atmos. Sci., 63, 3160–3185, https://doi.org/10.1175/JAS3802.1, 2006. 
Barahona, D., Molod, A., and Kalesse, H.: Direct estimation of the global distribution of vertical velocity within cirrus clouds, Sci. Rep., 7, 6840, https://doi.org/10.1038/s41598-017-07038-6, 2017. 
Barahona, D., Breen, K. H., Kalesse-Los, H., and Röttenbacher, J.: Deep Learning Parameterization of Vertical Wind Velocity Variability via Constrained Adversarial Training, Artificial Intelligence for the Earth Systems, 3, e230025, https://doi.org/10.1175/aies-d-23-0025.1, 2023. 
Brown, P. R. A. and Francis, P. N.: Improved Measurements of the Ice Water Content in Cirrus Using a Total-Water Probe, J. Atmos. Ocean. Tech., 12, 410–414, https://doi.org/10.1175/1520-0426(1995)012<0410:imotiw>2.0.co;2, 1995. 
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Short summary
Key controlling factors of cirrus clouds were individually quantified using machine learning models based on global-scale in situ observations from 12 campaigns at 67° S–87° N. Relative humidity shows much larger effects on cirrus occurrences and ice water content (IWC) fluctuations than vertical velocity. Aerosol–cloud interactions are seen for both large and small aerosols, with higher IWC and ice crystal number concentration under higher aerosol concentrations. Large aerosols are more impactful than small aerosols.
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