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Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility

Author

Listed:
  • Andrew C. Parnell
  • James Sweeney
  • Thinh K. Doan
  • Michael Salter-Townshend
  • Judy R. M. Allen
  • Brian Huntley
  • John Haslett

Abstract

type="main" xml:id="rssc12065-abs-0001"> We propose and fit a Bayesian model to infer palaeoclimate over several thousand years. The data that we use arise as ancient pollen counts taken from sediment cores together with radiocarbon dates which provide (uncertain) ages. When combined with a modern pollen–climate data set, we can calibrate ancient pollen into ancient climate. We use a normal–inverse Gaussian process prior to model the stochastic volatility of palaeoclimate over time, and we present a novel modularized Markov chain Monte Chain algorithm to enable fast computation. We illustrate our approach with a case-study from Sluggan Moss, Northern Ireland, and provide an R package, Bclim , for use at other sites.

Suggested Citation

  • Andrew C. Parnell & James Sweeney & Thinh K. Doan & Michael Salter-Townshend & Judy R. M. Allen & Brian Huntley & John Haslett, 2015. "Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(1), pages 115-138, January.
    • Handle: RePEc:bla:jorssc:v:64:y:2015:i:1:p:115-138
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    File URL: http://hdl.handle.net/10.1111/rssc.2014.64.issue-1
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    Cited by:

    1. Niamh Cahill & Jacky Croke & Micheline Campbell & Kate Hughes & John Vitkovsky & Jack Eaton Kilgallen & Andrew Parnell, 2023. "A Bayesian time series model for reconstructing hydroclimate from multiple proxies," Environmetrics, John Wiley & Sons, Ltd., vol. 34(4), June.

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