Type

Journal Article

Authors

Andrew C Parnell
John Haslett
Thinh K. Doan

Subjects

Mathematics

Topics
climate data ice cores bayesian inference joint modelling non linear bayesian method greenland time series

Joint inference of misaligned irregular time series with application to Greenland ice core data (2015)

Abstract Ice cores provide insight into the past climate over many millennia. Due to ice compaction, the raw data for any single core are irregular in time. Multiple cores have different irregularities; and when considered together, they are misaligned in time. After processing, such data are made available to researchers as regular time series: a data product. Typically, these cores are independently processed. This paper considers a fast Bayesian method for the joint processing of multiple irregular series. This is shown to be more efficient than the independent alternative. Furthermore, our explicit framework permits a reliable modelling of the impact of the multiple sources of uncertainty. The methodology is illustrated with the analysis of a pair of ice cores. Our data products, in the form of posterior marginals or joint distributions on an arbitrary temporal grid, are finite Gaussian mixtures. We can also produce process histories to study non-linear functionals of interest. More generally, the concept of joint analysis via hierarchical Gaussian process models can be widely extended, as the models used can be viewed within the larger context of continuous space–time processes.
Collections Ireland -> University College Dublin -> Earth Institute Research Collection
Ireland -> University College Dublin -> Mathematics and Statistics Research Collection
Ireland -> University College Dublin -> School of Mathematics and Statistics

Full list of authors on original publication

Andrew C Parnell, John Haslett, Thinh K. Doan

Experts in our system

1
Andrew Parnell
Maynooth University
Total Publications: 45