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Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022

Author

Listed:
  • Nick Dunstone

    (Met Office Hadley Centre)

  • Doug M. Smith

    (Met Office Hadley Centre)

  • Steven C. Hardiman

    (Met Office Hadley Centre)

  • Paul Davies

    (Met Office Hadley Centre)

  • Sarah Ineson

    (Met Office Hadley Centre)

  • Shipra Jain

    (Centre for Climate Research Singapore (CCRS))

  • Chris Kent

    (Met Office Hadley Centre)

  • Gill Martin

    (Met Office Hadley Centre)

  • Adam A. Scaife

    (Met Office Hadley Centre
    University of Exeter)

Abstract

Skilful predictions of near-term climate extremes are key to a resilient society. However, standard methods of analysing seasonal forecasts are not optimised to identify the rarer and most impactful extremes. For example, standard tercile probability maps, used in real-time regional climate outlooks, failed to convey the extreme magnitude of summer 2022 Pakistan rainfall that was, in fact, widely predicted by seasonal forecasts. Here we argue that, in this case, a strong summer La Niña provided a window of opportunity to issue a much more confident forecast for extreme rainfall than average skill estimates would suggest. We explore ways of building forecast confidence via a physical understanding of dynamical mechanisms, perturbation experiments to isolate extreme drivers, and simple empirical relationships. We highlight the need for more detailed routine monitoring of forecasts, with improved tools, to identify regional climate extremes and hence utilise windows of opportunity to issue trustworthy and actionable early warnings.

Suggested Citation

  • Nick Dunstone & Doug M. Smith & Steven C. Hardiman & Paul Davies & Sarah Ineson & Shipra Jain & Chris Kent & Gill Martin & Adam A. Scaife, 2023. "Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42377-1
    DOI: 10.1038/s41467-023-42377-1
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    References listed on IDEAS

    as
    1. Francisco J. Doblas‐Reyes & Javier García‐Serrano & Fabian Lienert & Aida Pintó Biescas & Luis R. L. Rodrigues, 2013. "Seasonal climate predictability and forecasting: status and prospects," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 4(4), pages 245-268, July.
    2. Dáithí A. Stone, 2019. "A hierarchical collection of political/economic regions for analysis of climate extremes," Climatic Change, Springer, vol. 155(4), pages 639-656, August.
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    Cited by:

    1. Lei Chen & Xiaohui Zhong & Hao Li & Jie Wu & Bo Lu & Deliang Chen & Shang-Ping Xie & Libo Wu & Qingchen Chao & Chensen Lin & Zixin Hu & Yuan Qi, 2024. "A machine learning model that outperforms conventional global subseasonal forecast models," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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