Articles | Volume 13, issue 3
Research article
06 Sep 2022
Research article |  | 06 Sep 2022

Combining machine learning and SMILEs to classify, better understand, and project changes in ENSO events

Nicola Maher, Thibault P. Tabarin, and Sebastian Milinski


Total article views: 2,081 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,657 401 23 2,081 95 13 14
  • HTML: 1,657
  • PDF: 401
  • XML: 23
  • Total: 2,081
  • Supplement: 95
  • BibTeX: 13
  • EndNote: 14
Views and downloads (calculated since 25 Jan 2022)
Cumulative views and downloads (calculated since 25 Jan 2022)

Viewed (geographical distribution)

Total article views: 2,081 (including HTML, PDF, and XML) Thereof 1,869 with geography defined and 212 with unknown origin.
Country # Views %
  • 1

Discussed (preprint)

Latest update: 30 Mar 2023
Short summary
El Niño events occur as two broad types: eastern Pacific (EP) and central Pacific (CP). EP and CP events differ in strength, evolution, and in their impacts. In this study we create a new machine learning classifier to identify the two types of El Niño events using observed sea surface temperature data. We apply our new classifier to climate models and show that CP events are unlikely to change in frequency or strength under a warming climate, with model disagreement for EP events.
Final-revised paper