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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 Peter
Parnell, Andrew C.
|Keywords:||Bayesian Transfer Function
|Issue Date:||2016||Source:||Cahill, N., Kemp, A. C., Horton, B. P., & Parnell, A. C. (2016). A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change. Climate of the Past, 12(2), 525-542. doi:10.5194/cp-12-525-2016||Series/Report no.:||Climate of the Past||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 = 0.003m2). 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.||URI:||https://hdl.handle.net/10356/88094
|ISSN:||1814-9324||DOI:||http://dx.doi.org/10.5194/cp-12-525-2016||Rights:||© 2016 The Authors (Published by Copernicus Publications on behalf of the European Geosciences Union (EGU)). This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EOS Journal Articles|
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