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Explainable deep learning for insights in El Niño and river flows

Author

Listed:
  • Yumin Liu

    (Northeastern University
    Northeastern University)

  • Kate Duffy

    (Northeastern University
    NASA Ames Research Center
    Bay Area Environmental Research Institute)

  • Jennifer G. Dy

    (Northeastern University
    Northeastern University)

  • Auroop R. Ganguly

    (Northeastern University
    Northeastern University
    Pacific Northwest National Laboratory)

Abstract

The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.

Suggested Citation

  • Yumin Liu & Kate Duffy & Jennifer G. Dy & Auroop R. Ganguly, 2023. "Explainable deep learning for insights in El Niño and river flows," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-35968-5
    DOI: 10.1038/s41467-023-35968-5
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    References listed on IDEAS

    as
    1. Wenjun Zhang & Feng Jiang & Malte F. Stuecker & Fei-Fei Jin & Axel Timmermann, 2021. "Spurious North Tropical Atlantic precursors to El Niño," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    2. Nerilie J. Abram & Nicky M. Wright & Bethany Ellis & Bronwyn C. Dixon & Jennifer B. Wurtzel & Matthew H. England & Caroline C. Ummenhofer & Belle Philibosian & Sri Yudawati Cahyarini & Tsai-Luen Yu & , 2020. "Coupling of Indo-Pacific climate variability over the last millennium," Nature, Nature, vol. 579(7799), pages 385-392, March.
    3. Yoo-Geun Ham & Jeong-Hwan Kim & Jing-Jia Luo, 2019. "Deep learning for multi-year ENSO forecasts," Nature, Nature, vol. 573(7775), pages 568-572, September.
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