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A novel probabilistic forecast system predicting anomalously warm 2018-2022 reinforcing the long-term global warming trend

Author

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  • Florian Sévellec

    (UMR6523, Univ. Brest, CNRS-Ifremer-UBO-IRD
    University of Southampton)

  • Sybren S. Drijfhout

    (University of Southampton
    Koninklijk Nederlands Meteorologisch Instituut)

Abstract

In a changing climate, there is an ever-increasing societal demand for accurate and reliable interannual predictions. Accurate and reliable interannual predictions of global temperatures are key for determining the regional climate change impacts that scale with global temperature, such as precipitation extremes, severe droughts, or intense hurricane activity, for instance. However, the chaotic nature of the climate system limits prediction accuracy on such timescales. Here we develop a novel method to predict global-mean surface air temperature and sea surface temperature, based on transfer operators, which allows, by-design, probabilistic forecasts. The prediction accuracy is equivalent to operational forecasts and its reliability is high. The post-1998 global warming hiatus is well predicted. For 2018–2022, the probabilistic forecast indicates a warmer than normal period, with respect to the forced trend. This will temporarily reinforce the long-term global warming trend. The coming warm period is associated with an increased likelihood of intense to extreme temperatures. The important numerical efficiency of the method (a few hundredths of a second on a laptop) opens the possibility for real-time probabilistic predictions carried out on personal mobile devices.

Suggested Citation

  • Florian Sévellec & Sybren S. Drijfhout, 2018. "A novel probabilistic forecast system predicting anomalously warm 2018-2022 reinforcing the long-term global warming trend," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05442-8
    DOI: 10.1038/s41467-018-05442-8
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    Cited by:

    1. Moazami, Amin & Nik, Vahid M. & Carlucci, Salvatore & Geving, Stig, 2019. "Impacts of future weather data typology on building energy performance – Investigating long-term patterns of climate change and extreme weather conditions," Applied Energy, Elsevier, vol. 238(C), pages 696-720.
    2. Jonas Heckenhahn & Moritz A. Drupp, 2024. "Relative Price Changes of Ecosystem Services: Evidence from Germany," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(3), pages 833-880, March.
    3. 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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