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

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Title: A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change
Authors: Cahill, Niamh
Kemp, Andrew C.
Horton, Benjamin P.
Parnell, Andrew C.
Permanent link: http://hdl.handle.net/10197/7965
Date: 29-Feb-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.
Funding Details: European Commission - European Regional Development Fund
Science Foundation Ireland
Type of material: Journal Article
Publisher: European Geosciences Union
Copyright (published version): 2016 the Authors
Keywords: Paleoenvironmental reconstruction;Relative sea-level;Bayesian hierarchical model;New Jersey Common Era
DOI: 10.5194/cp-12-525-2016
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
CASL Research Collection
Earth Institute Research Collection

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