On Bayesian Modelling of the Uncertainties in Palaeoclimate Reconstruction

We outline a model and algorithm to perform inference on the palaeoclimate and palaeoclimate volatility from pollen proxy data. We use a novel multivariate non-linear non-Gaussian state space model consisting of an observation equation linking climate to proxy data and an evolution equation driving...

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Bibliographic Details
Published in:arXiv.org 2012-06
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
Bayesian analysis
Climate change
Evolution
Gaussian process
Pollen
State space models
State variable
Volatility
Online Access:Get full text
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Summary:We outline a model and algorithm to perform inference on the palaeoclimate and palaeoclimate volatility from pollen proxy data. We use a novel multivariate non-linear non-Gaussian state space model consisting of an observation equation linking climate to proxy data and an evolution equation driving climate change over time. The link from climate to proxy data is defined by a pre-calibrated forward model, as developed in Salter-Townshend and Haslett (2012) and Sweeney (2012). Climatic change is represented by a temporally-uncertain Normal-Inverse Gaussian Levy process, being able to capture large jumps in multivariate climate whilst remaining temporally consistent. The pre-calibrated nature of the forward model allows us to cut feedback between the observation and evolution equations and thus integrate out the state variable entirely whilst making minimal simplifying assumptions. A key part of this approach is the creation of mixtures of marginal data posteriors representing the information obtained about climate from each individual time point. Our approach allows for an extremely efficient MCMC algorithm, which we demonstrate with a pollen core from Sluggan Bog, County Antrim, Northern Ireland.
ISSN:2331-8422