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

Viewed

Total article views: 803 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
391 112 300 803 27 38
  • HTML: 391
  • PDF: 112
  • XML: 300
  • Total: 803
  • BibTeX: 27
  • EndNote: 38
Views and downloads (calculated since 26 Aug 2024)
Cumulative views and downloads (calculated since 26 Aug 2024)

Viewed (geographical distribution)

Total article views: 803 (including HTML, PDF, and XML) Thereof 803 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 14 Jul 2025
Download
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.
Share
Altmetrics
Final-revised paper
Preprint