IDEAS home Printed from https://ideas.repec.org/a/spr/climat/v144y2017i2d10.1007_s10584-017-2048-3.html
   My bibliography  Save this article

Assessing climate change impacts on extreme weather events: the case for an alternative (Bayesian) approach

Author

Listed:
  • Michael E. Mann

    (Penn State University)

  • Elisabeth A. Lloyd

    (Indiana University)

  • Naomi Oreskes

    (Harvard University)

Abstract

The conventional approach to detecting and attributing climate change impacts on extreme weather events is generally based on frequentist statistical inference wherein a null hypothesis of no influence is assumed, and the alternative hypothesis of an influence is accepted only when the null hypothesis can be rejected at a sufficiently high (e.g., 95% or “p = 0.05”) level of confidence. Using a simple conceptual model for the occurrence of extreme weather events, we show that if the objective is to minimize forecast error, an alternative approach wherein likelihoods of impact are continually updated as data become available is preferable. Using a simple “proof-of-concept,” we show that such an approach will, under rather general assumptions, yield more accurate forecasts. We also argue that such an approach will better serve society, in providing a more effective means to alert decision-makers to potential and unfolding harms and avoid opportunity costs. In short, a Bayesian approach is preferable, both empirically and ethically.

Suggested Citation

  • Michael E. Mann & Elisabeth A. Lloyd & Naomi Oreskes, 2017. "Assessing climate change impacts on extreme weather events: the case for an alternative (Bayesian) approach," Climatic Change, Springer, vol. 144(2), pages 131-142, September.
  • Handle: RePEc:spr:climat:v:144:y:2017:i:2:d:10.1007_s10584-017-2048-3
    DOI: 10.1007/s10584-017-2048-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10584-017-2048-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10584-017-2048-3?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Regina Nuzzo, 2014. "Scientific method: Statistical errors," Nature, Nature, vol. 506(7487), pages 150-152, February.
    2. Cooley, Daniel & Nychka, Douglas & Naveau, Philippe, 2007. "Bayesian Spatial Modeling of Extreme Precipitation Return Levels," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 824-840, September.
    3. Kevin Trenberth, 2012. "Framing the way to relate climate extremes to climate change," Climatic Change, Springer, vol. 115(2), pages 283-290, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Henri F. Drake & Geoffrey Henderson, 2022. "A defense of usable climate mitigation science: how science can contribute to social movements," Climatic Change, Springer, vol. 172(1), pages 1-18, May.
    2. Peter A. Stott & David J. Karoly & Francis W. Zwiers, 2017. "Is the choice of statistical paradigm critical in extreme event attribution studies?," Climatic Change, Springer, vol. 144(2), pages 143-150, September.
    3. Aglaé Jézéquel & Vivian Dépoues & Hélène Guillemot & Mélodie Trolliet & Jean-Paul Vanderlinden & Pascal Yiou, 2018. "Behind the veil of extreme event attribution," Climatic Change, Springer, vol. 149(3), pages 367-383, August.
    4. Elisabeth A. Lloyd & Naomi Oreskes & Sonia I. Seneviratne & Edward J. Larson, 2021. "Climate scientists set the bar of proof too high," Climatic Change, Springer, vol. 165(3), pages 1-10, April.
    5. Henrik Thorén & Johannes Persson & Lennart Olsson, 2021. "A pluralist approach to epistemic dilemmas in event attribution science," Climatic Change, Springer, vol. 169(1), pages 1-17, November.
    6. Tobias Pfrommer & Timo Goeschl & Alexander Proelss & Martin Carrier & Johannes Lenhard & Henrike Martin & Ulrike Niemeier & Hauke Schmidt, 2019. "Establishing causation in climate litigation: admissibility and reliability," Climatic Change, Springer, vol. 152(1), pages 67-84, January.

    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. Jyotirmoy Sarkar, 2018. "Will P†Value Triumph over Abuses and Attacks?," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 7(4), pages 66-71, July.
    2. Arthur Matsuo Yamashita Rios de Sousa & Hideki Takayasu & Misako Takayasu, 2017. "Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-18, May.
    3. Guanzhou Wei & Xiao Liu & Russell Barton, 2024. "An extended PDE‐based statistical spatio‐temporal model that suppresses the Gibbs phenomenon," Environmetrics, John Wiley & Sons, Ltd., vol. 35(2), March.
    4. Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 203-225, March.
    5. Maurizio Canavari & Andreas C. Drichoutis & Jayson L. Lusk & Rodolfo M. Nayga, Jr., 2018. "How to run an experimental auction: A review of recent advances," Working Papers 2018-5, Agricultural University of Athens, Department Of Agricultural Economics.
    6. Stephenson Alec G. & Tawn Jonathan A., 2013. "Determining the Best Track Performances of All Time Using a Conceptual Population Model for Athletics Records," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 67-76, March.
    7. Felipe Campelo & Fernanda Takahashi, 2019. "Sample size estimation for power and accuracy in the experimental comparison of algorithms," Journal of Heuristics, Springer, vol. 25(2), pages 305-338, April.
    8. Martin E Héroux & Janet L Taylor & Simon C Gandevia, 2015. "The Use and Abuse of Transcranial Magnetic Stimulation to Modulate Corticospinal Excitability in Humans," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-10, December.
    9. Vaibhav Chaturvedi & Priyadarshi Shukla, 2014. "Role of energy efficiency in climate change mitigation policy for India: assessment of co-benefits and opportunities within an integrated assessment modeling framework," Climatic Change, Springer, vol. 123(3), pages 597-609, April.
    10. Roger Beecham & Nick Williams & Alexis Comber, 2020. "Regionally-structured explanations behind area-level populism: An update to recent ecological analyses," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    11. Silius M. Vandeskog & Sara Martino & Daniela Castro-Camilo & Håvard Rue, 2022. "Modelling Sub-daily Precipitation Extremes with the Blended Generalised Extreme Value Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 598-621, December.
    12. Gholamreza Roshan & Stefan W. Grab & Mohammad Saeed Najafi, 2020. "The role of physical geographic parameters affecting past (1980–2010) and future (2020–2049) thermal stress in Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(1), pages 365-399, May.
    13. Manuel G. Scotto & Susana M. Barbosa & Andr�s M. Alonso, 2011. "Extreme value and cluster analysis of European daily temperature series," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(12), pages 2793-2804, March.
    14. Rishikesh Yadav & Raphaël Huser & Thomas Opitz, 2021. "Spatial hierarchical modeling of threshold exceedances using rate mixtures," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    15. Shuxin Guo & Qiang Liu, 2024. "Data-generating process and time-series asset pricing," Papers 2405.10920, arXiv.org.
    16. Linyin Cheng & Amir AghaKouchak & Eric Gilleland & Richard Katz, 2014. "Non-stationary extreme value analysis in a changing climate," Climatic Change, Springer, vol. 127(2), pages 353-369, November.
    17. Jonathan Jalbert & Christian Genest & Luc Perreault, 2022. "Interpolation of Precipitation Extremes on a Large Domain Toward IDF Curve Construction at Unmonitored Locations," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 461-486, September.
    18. Tserennadmid Nadia Mijiddorj & Justine Shanti Alexander & Gustaf Samelius & Charudutt Mishra & Bazartseren Boldgiv, 2020. "Traditional livelihoods under a changing climate: herder perceptions of climate change and its consequences in South Gobi, Mongolia," Climatic Change, Springer, vol. 162(3), pages 1065-1079, October.
    19. Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 203-225, March.
    20. Juan Li & Hanzhang Xu & Wei Pan & Bei Wu, 2017. "Association between tooth loss and cognitive decline: A 13-year longitudinal study of Chinese older adults," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-12, February.

    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:spr:climat:v:144:y:2017:i:2:d:10.1007_s10584-017-2048-3. 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.springer.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.