Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 23, issue 1
Articles | Volume 23, issue 1
https://doi.org/10.5194/acp-23-523-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-523-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 1
Research article
 | 
13 Jan 2023
Research article |  | 13 Jan 2023

Machine learning of cloud types in satellite observations and climate models

Peter Kuma, Frida A.-M. Bender, Alex Schuddeboom, Adrian J. McDonald, and Øyvind Seland

Viewed

Total article views: 3,879 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,930 904 45 3,879 47 49 34
  • HTML: 2,930
  • PDF: 904
  • XML: 45
  • Total: 3,879
  • Supplement: 47
  • BibTeX: 49
  • EndNote: 34
Views and downloads (calculated since 11 Mar 2022)
Cumulative views and downloads (calculated since 11 Mar 2022)

Viewed (geographical distribution)

Total article views: 3,879 (including HTML, PDF, and XML) Thereof 3,966 with geography defined and -87 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 25 Apr 2024
Download
Short summary
We present a machine learning method for determining cloud types in climate model output and satellite observations based on ground observations of cloud genera. We analyse cloud type biases and changes with temperature in climate models and show that the bias is anticorrelated with climate sensitivity. Models simulating decreasing stratiform and increasing cumuliform clouds with increased CO2 concentration tend to have higher climate sensitivity than models simulating the opposite tendencies.
Read more
Altmetrics
Final-revised paper
Preprint