Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility

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...

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Bibliographic Details
Published in:Journal of the Royal Statistical Society 2015-01, Vol.64 (1), p.115-138
Main Authors: Parnell, Andrew C., Sweeney, James, Doan, Thinh K., Salter-Townshend, Michael, Allen, Judy R. M., Huntley, Brian, Haslett, John
Format: Article
Language:English
Subjects:
Algorithms
Applied statistics
Approximation
Bayesian analysis
Bayesian method
Chains
Climate
Climate change
Climate proxies
Datasets
Hierarchical time series
Inference
Interpolation
inverse Gaussian process
Modular Bayes
Monte Carlo simulation
Normal
Northern Ireland
Palaeoclimate reconstruction
Paleoclimatology
Pollen
Radiocarbon dating
Statistical analysis
Stochastic processes
Stochasticity
Studies
Temporal uncertainty
Time series models
Uncertainty
United Kingdom
Volatility
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Summary: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.
ISSN:0035-9254
1467-9876
DOI:10.1111/rssc.12065