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Seasonal Arctic sea ice forecasting with probabilistic deep learning

Author

Listed:
  • Tom R. Andersson

    (NERC, UKRI)

  • J. Scott Hosking

    (NERC, UKRI
    The Alan Turing Institute)

  • María Pérez-Ortiz

    (University College London)

  • Brooks Paige

    (The Alan Turing Institute
    University College London)

  • Andrew Elliott

    (The Alan Turing Institute
    University of Glasgow)

  • Chris Russell

    (Amazon Web Services)

  • Stephen Law

    (The Alan Turing Institute
    University College London)

  • Daniel C. Jones

    (NERC, UKRI)

  • Jeremy Wilkinson

    (NERC, UKRI)

  • Tony Phillips

    (NERC, UKRI)

  • James Byrne

    (NERC, UKRI)

  • Steffen Tietsche

    (European Centre for Medium-Range Weather Forecasts (ECMWF))

  • Beena Balan Sarojini

    (European Centre for Medium-Range Weather Forecasts (ECMWF))

  • Eduardo Blanchard-Wrigglesworth

    (University of Washington)

  • Yevgeny Aksenov

    (National Oceanography Centre)

  • Rod Downie

    (WWF)

  • Emily Shuckburgh

    (NERC, UKRI
    University of Cambridge)

Abstract

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss.

Suggested Citation

  • Tom R. Andersson & J. Scott Hosking & María Pérez-Ortiz & Brooks Paige & Andrew Elliott & Chris Russell & Stephen Law & Daniel C. Jones & Jeremy Wilkinson & Tony Phillips & James Byrne & Steffen Tiets, 2021. "Seasonal Arctic sea ice forecasting with probabilistic deep learning," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25257-4
    DOI: 10.1038/s41467-021-25257-4
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    Cited by:

    1. Varvara E. Zemskova & Tai-Long He & Zirui Wan & Nicolas Grisouard, 2022. "A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Diebold, Francis X. & Göbel, Maximilian & Goulet Coulombe, Philippe, 2023. "Assessing and comparing fixed-target forecasts of Arctic sea ice: Glide charts for feature-engineered linear regression and machine learning models," Energy Economics, Elsevier, vol. 124(C).
    3. Francis X. Diebold & Maximilian Goebel & Philippe Goulet Coulombe, 2022. "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," Papers 2206.10721, arXiv.org, revised Jun 2023.

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