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

Maher, Nicola; Tabarin, Thibault P.; Milinski, Sebastian

doi:https://doi.org/10.5194/esd-13-1289-2022

Articles | Volume 13, issue 3

https://doi.org/10.5194/esd-13-1289-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esd-13-1289-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Supplement

https://doi.org/10.5194/esd-13-1289-2022-supplement

Data sets

Multi-Model Large Ensemble Archive NCAR/UCAR https://www.cesm.ucar.edu/projects/community-projects/MMLEA/

Gridded Climate Data NOAA Physical Sciences Laboratory https://www.psl.noaa.gov/data/gridded/

Model code and software

nicolamaher/classification: ENSO ML Classification - Maher, Tabarin, Milinski 2022 (v1.1.0) Maher, N. https://doi.org/10.5281/zenodo.7032576

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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.