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    Statistical challenges in estimating past climate changes


    Sweeney, James and Salter-Townshend, Michael and Edwards, Tamsin and Buck, Caitlin E. and Parnell, Andrew (2018) Statistical challenges in estimating past climate changes. WIREs Computational Statistics, 10 (e1437). ISSN 1939-0068

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    Abstract

    We review the statistical methods currently in use to estimate past changes in climate. These methods encompass the full gamut of statistical modeling approaches, ranging from simple regression up to nonparametric spatiotemporal Bayesian models. Often the full inferential challenge is broken down into many submodels each of which may involve multiple stochastic components, and occasionally mechanistic or process-based models too. We argue that many of the traditional approaches are simplistic in their structure, handling, and presentation of uncertainty, and that newer models (which incorporate mechanistic aspects alongside statistical models) provide an exciting research agenda for the next decade. We hope that policy-makers and those charged with predicting future climate change will increasingly use probabilistic paleoclimate reconstructions to calibrate their forecasts, learn about key natural climatological parameters, and make appropriate decisions concerning future climate change. Remarkably few statisticians have involved themselves with paleoclimate reconstruction, and we hope that this article inspires more to take up the challenge.

    Item Type: Article
    Additional Information: Cite as: Sweeney, J, Salter‐Townshend, M, Edwards, T, Buck, CE, Parnell, AC. Statistical challenges in estimating past climate changes. WIREs Comput Stat. 2018; 10:e1437. https://doi.org/10.1002/wics.1437
    Keywords: Bayesian methods and theory; computational Bayesian methods; Paleoclimate reconstruction; statistical modelling of climate;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Faculty of Science and Engineering > Mathematics and Statistics
    Item ID: 13276
    Identification Number: https://doi.org/10.1002/wics.1437
    Depositing User: Andrew Parnell
    Date Deposited: 24 Sep 2020 15:10
    Journal or Publication Title: WIREs Computational Statistics
    Publisher: Wiley
    Refereed: Yes
    URI:

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