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Advancing decadal-scale climate prediction in the North Atlantic sector

Author

Listed:
  • N. S. Keenlyside

    (Leibniz Institute of Marine Sciences, Düsternbrooker Weg 20, D-24105 Kiel, Germany)

  • M. Latif

    (Leibniz Institute of Marine Sciences, Düsternbrooker Weg 20, D-24105 Kiel, Germany)

  • J. Jungclaus

    (Max Planck Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany)

  • L. Kornblueh

    (Max Planck Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany)

  • E. Roeckner

    (Max Planck Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany)

Abstract

Decadal climate prediction The fluctuating climate of the North Atlantic has profound consequences, inducing changes in hurricane activity, surface temperatures and rainfall from North America to Europe and Africa. In principle, these changes could be predicted if the current state of the ocean were known, but the necessary subsurface observations are lacking. Keenlyside et al. now show that detailed knowledge of the ocean state is not strictly necessary for producing useful predictions on decadal timescales. Their approach, which has proved its worth in 'retro-spective' forecasts, uses existing sea surface temperature observations to improve the forecasting power of climate models. The new model predicts that over the next decade, natural climate variations in the North Atlantic and tropical Pacific oceans will temporarily offset the projected anthropogenic warming: surface temperatures in Europe and North America may even cool a little during this period.

Suggested Citation

  • N. S. Keenlyside & M. Latif & J. Jungclaus & L. Kornblueh & E. Roeckner, 2008. "Advancing decadal-scale climate prediction in the North Atlantic sector," Nature, Nature, vol. 453(7191), pages 84-88, May.
  • Handle: RePEc:nat:nature:v:453:y:2008:i:7191:d:10.1038_nature06921
    DOI: 10.1038/nature06921
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    Cited by:

    1. Ren, Jinfu & Liu, Yang & Liu, Jiming, 2023. "Chaotic behavior learning via information tracking," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    2. W. M. Schaffer, 2009. "A Surfeit of Cycles," Energy & Environment, , vol. 20(6), pages 985-996, October.
    3. Craig Loehle, 2009. "Trend Analysis of Satellite Global Temperature Data," Energy & Environment, , vol. 20(7), pages 1087-1098, November.
    4. Fildes, Robert & Kourentzes, Nikolaos, 2011. "Validation and forecasting accuracy in models of climate change," International Journal of Forecasting, Elsevier, vol. 27(4), pages 968-995, October.
    5. Timothy M. Lenton & Jesse F. Abrams & Annett Bartsch & Sebastian Bathiany & Chris A. Boulton & Joshua E. Buxton & Alessandra Conversi & Andrew M. Cunliffe & Sophie Hebden & Thomas Lavergne & Benjamin , 2024. "Remotely sensing potential climate change tipping points across scales," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    6. J. Isaac Miller & Kyungsik Nam, 2019. "Dating Hiatuses: A Statistical Model of the Recent Slowdown in Global Warming – and the Next One," Working Papers 1903, Department of Economics, University of Missouri.

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