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Quantifying the risk of extreme seasonal precipitation events in a changing climate

Author

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  • T. N. Palmer

    (European Centre for Medium-Range Weather Forecasts)

  • J. Räisänen

    (Rossby Centre, SMHI)

Abstract

Increasing concentrations of atmospheric carbon dioxide will almost certainly lead to changes in global mean climate1. But because—by definition—extreme events are rare, it is significantly more difficult to quantify the risk of extremes. Ensemble-based probabilistic predictions2, as used in short- and medium-term forecasts of weather and climate, are more useful than deterministic forecasts using a ‘best guess’ scenario to address this sort of problem3,4. Here we present a probabilistic analysis of 19 global climate model simulations with a generic binary decision model. We estimate that the probability of total boreal winter precipitation exceeding two standard deviations above normal will increase by a factor of five over parts of the UK over the next 100 years. We find similar increases in probability for the Asian monsoon region in boreal summer, with implications for flooding in Bangladesh. Further practical applications of our techniques would be helped by the use of larger ensembles (for a more complete sampling of model uncertainty) and a wider range of scenarios at a resolution adequate to analyse average-size river basins.

Suggested Citation

  • T. N. Palmer & J. Räisänen, 2002. "Quantifying the risk of extreme seasonal precipitation events in a changing climate," Nature, Nature, vol. 415(6871), pages 512-514, January.
  • Handle: RePEc:nat:nature:v:415:y:2002:i:6871:d:10.1038_415512a
    DOI: 10.1038/415512a
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    Cited by:

    1. Nigel W. Arnell & Emma L. Tompkins & W. Neil Adger, 2005. "Eliciting Information from Experts on the Likelihood of Rapid Climate Change," Risk Analysis, John Wiley & Sons, vol. 25(6), pages 1419-1431, December.
    2. Hongbo Ma & Jeffrey A. Nittrouer & Xudong Fu & Gary Parker & Yuanfeng Zhang & Yuanjian Wang & Yanjun Wang & Michael P. Lamb & Julia Cisneros & Jim Best & Daniel R. Parsons & Baosheng Wu, 2022. "Amplification of downstream flood stage due to damming of fine-grained rivers," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Xueke Li & Amanda H. Lynch, 2023. "New insights into projected Arctic sea road: operational risks, economic values, and policy implications," Climatic Change, Springer, vol. 176(4), pages 1-16, April.
    4. Jianting Zhu & William Forsee & Rina Schumer & Mahesh Gautam, 2013. "Future projections and uncertainty assessment of extreme rainfall intensity in the United States from an ensemble of climate models," Climatic Change, Springer, vol. 118(2), pages 469-485, May.
    5. Hang Gao & Chun Shen & Xuesong Wang & Pak-Wai Chan & Kai-Kwong Hon & Jianbing Li, 2024. "Interpretable semi-supervised clustering enables universal detection and intensity assessment of diverse aviation hazardous winds," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    6. Igor Leščešen & Mojca Šraj & Biljana Basarin & Dragoslav Pavić & Minučer Mesaroš & Manfred Mudelsee, 2022. "Regional Flood Frequency Analysis of the Sava River in South-Eastern Europe," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    7. Nam, Won-Ho & Hayes, Michael J. & Svoboda, Mark D. & Tadesse, Tsegaye & Wilhite, Donald A., 2015. "Drought hazard assessment in the context of climate change for South Korea," Agricultural Water Management, Elsevier, vol. 160(C), pages 106-117.
    8. Nam, Won-Ho & Choi, Jin-Yong & Hong, Eun-Mi, 2015. "Irrigation vulnerability assessment on agricultural water supply risk for adaptive management of climate change in South Korea," Agricultural Water Management, Elsevier, vol. 152(C), pages 173-187.

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