Journal Article


Andrew C. Parnell
Benjamin P. Horton
Andrew C. Kemp
Niamh Cahill



raw relative sea level change new jersey common era sea change paleoenvironmental reconstruction relative sea level bayesian hierarchical model data model

A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change (2016)

Abstract We present a Bayesian hierarchical model for reconstructing the continuous and dynamic evolution of relative sea-level (RSL) change with quantified uncertainty. The reconstruction is produced from biological (foraminifera) and geochemical (δ13C) sea-level indicators preserved in dated cores of salt-marsh sediment. Our model is comprised of three modules: (1) a new Bayesian transfer (B-TF) function for the calibration of biological indicators into tidal elevation, which is flexible enough to formally accommodate additional proxies; (2) an existing chronology developed using the Bchron age–depth model, and (3) an existing Errors-In-Variables integrated Gaussian process (EIV-IGP) model for estimating rates of sea-level change. Our approach is illustrated using a case study of Common Era sea-level variability from New Jersey, USA We develop a new B-TF using foraminifera, with and without the additional (δ13C) proxy and compare our results to those from a widely used weighted-averaging transfer function (WA-TF). The formal incorporation of a second proxy into the B-TF model results in smaller vertical uncertainties and improved accuracy for reconstructed RSL. The vertical uncertainty from the multi-proxy B-TF is  ∼  28 % smaller on average compared to the WA-TF. When evaluated against historic tide-gauge measurements, the multi-proxy B-TF most accurately reconstructs the RSL changes observed in the instrumental record (mean square error  =  0.003 m2). The Bayesian hierarchical model provides a single, unifying framework for reconstructing and analyzing sea-level change through time. This approach is suitable for reconstructing other paleoenvironmental variables (e.g., temperature) using biological proxies.
Collections Ireland -> University College Dublin -> CASL Research Collection
Ireland -> University College Dublin -> Earth Institute Research Collection
Ireland -> University College Dublin -> Complex and Adaptive Systems Laboratory
Ireland -> University College Dublin -> Mathematics and Statistics Research Collection

Full list of authors on original publication

Andrew C. Parnell, Benjamin P. Horton, Andrew C. Kemp, Niamh Cahill

Experts in our system

Andrew Parnell
Maynooth University
Total Publications: 45
Benjamin P. Horton
Maynooth University
Total Publications: 11
Andrew C. Kemp
Maynooth University
Total Publications: 7
Niamh Cahill
Maynooth University
Total Publications: 16