Articles | Volume 25, issue 13
https://doi.org/10.5194/acp-25-7227-2025
https://doi.org/10.5194/acp-25-7227-2025
Research article
 | 
14 Jul 2025
Research article |  | 14 Jul 2025

Cirrus formation regimes – data-driven identification and quantification of mineral dust effect

Kai Jeggle, David Neubauer, Hanin Binder, and Ulrike Lohmann

Data sets

Cirrus Formation Regimes -- Data Driven Identification and Quantification of Mineral Dust Effect K. Jeggle et al. https://doi.org/10.5281/zenodo.15102965

Model code and software

Cirrus Formation Regimes -- Data Driven Identification and Quantification of Mineral Dust Effect K. Jeggle et al. https://doi.org/10.5281/zenodo.15102965

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Short summary
This work uncovers the formation regimes of cirrus clouds and how dust particles influence their properties. By applying machine learning to a combination of satellite and reanalysis data, cirrus clouds are classified into different formation regimes. Depending on the regime, increasing dust aerosol concentrations can either decrease or increase the number of ice crystals. This challenges the idea of using cloud seeding to cool the planet, as it may unintentionally lead to warming instead.
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