IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-42377-1.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-42377-1
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-42377-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vikki Thompson & Dann Mitchell & Gabriele C. Hegerl & Matthew Collins & Nicholas J. Leach & Julia M. Slingo, 2023. "The most at-risk regions in the world for high-impact heatwaves," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    2. Mark D. Risser & William D. Collins & Michael F. Wehner & Travis A. O’Brien & Huanping Huang & Paul A. Ullrich, 2024. "Anthropogenic aerosols mask increases in US rainfall by greenhouse gases," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    3. De Felice, Matteo & Alessandri, Andrea & Catalano, Franco, 2015. "Seasonal climate forecasts for medium-term electricity demand forecasting," Applied Energy, Elsevier, vol. 137(C), pages 435-444.
    4. Isabel Iglesias & María N Lorenzo & Juan J Taboada, 2014. "Seasonal Predictability of the East Atlantic Pattern from Sea Surface Temperatures," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-8, January.
    5. Phong V. V. Le & James T. Randerson & Rebecca Willett & Stephen Wright & Padhraic Smyth & Clément Guilloteau & Antonios Mamalakis & Efi Foufoula-Georgiou, 2023. "Climate-driven changes in the predictability of seasonal precipitation," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    6. Lledó, Llorenç & Ramon, Jaume & Soret, Albert & Doblas-Reyes, Francisco-Javier, 2022. "Seasonal prediction of renewable energy generation in Europe based on four teleconnection indices," Renewable Energy, Elsevier, vol. 186(C), pages 420-430.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42377-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.