Preprints
https://doi.org/10.5194/esd-2021-105
https://doi.org/10.5194/esd-2021-105
 
25 Jan 2022
25 Jan 2022
Status: a revised version of this preprint is currently under review for the journal ESD.

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

Nicola Maher1,2, Thibault Paul Tabarin3, and Sebastian Milinski4,5 Nicola Maher et al.
  • 1Max Planck Institute for Meteorology, Hamburg, Germany
  • 2Cooperative Institute for Research in Environmental Sciences (CIRES) and Department of Atmospheric and Oceanic Sciences (ATOC), University of Colorado at Boulder, Boulder, CO 80309, USA
  • 3Freelancer, Boulder, CO 80303, USA
  • 4Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO 80307, USA
  • 5Cooperative Programs for the Advancement of Earth System Science, University Corporation for Atmospheric Research, Boulder, CO 80307, USA

Abstract. The El Niño Southern Oscillation (ENSO) occurs in three phases: neutral, warm (El Niño) and cool (La Niña). While classifying El Niño and La Niña is relatively straightforward, El Niño events can be broadly classified into two types: Central Pacific (CP) and Eastern Pacific (EP). Differentiating between CP and EP events is currently dependent on both the method and observational dataset used. In this study, we create a new classification scheme using supervised machine learning trained on 18 observational and reanalysis products. This builds on previous work by identifying classes of events using the temporal evolution of sea surface temperature in multiple regions across the tropical Pacific. By applying this new classifier to seven single model initial-condition large ensembles (SMILEs) we investigate both the internal variability and forced changes in each type of ENSO event, where events identified behave similar to those observed. It is currently debated whether the observed increase in the frequency of CP events after the late 1970s is due to climate change. We found it to be within the range of internal variability in the SMILEs. When considering future changes, we do not project a change in CP frequency or amplitude under a strong warming scenario (RCP8.5/SSP370) and we find model differences in EP El Niño and La Niña frequency and amplitude projections. Finally, we find that models show differences in projected precipitation and SST pattern changes for each event type that do not seem to be linked to the Pacific mean state SST change, although the SST and precipitation changes in individual SMILEs are linked. Our work demonstrates the value of combining machine learning with climate models, and highlights the need to use SMILEs when evaluating ENSO in climate models due to the large spread of results found within a single model due to internal variability alone.

Nicola Maher et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esd-2021-105', Anonymous Referee #1, 23 Feb 2022
    • AC1: 'Reply on RC1', Nicola Maher, 21 Apr 2022
  • RC2: 'Comment on esd-2021-105', Anonymous Referee #2, 24 Feb 2022
    • AC2: 'Reply on RC2', Nicola Maher, 21 Apr 2022

Nicola Maher et al.

Nicola Maher et al.

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Latest update: 29 Jun 2022
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Short summary
El Niño's 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, which can be used to identify two types of El Niño event using observed sea surface temperature data. We then apply our new classifier to climate models and show that CP events are unlikely to change in frequency of strength under a warming climate, with model disagreement for EP events.
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