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


    Cahill, Niamh and Kemp, Andrew C. and Horton, Benjamin P. and Parnell, Andrew C. (2016) A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change. Climate of the Past, 12 (2). pp. 525-542. ISSN 1814-9332

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    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 (δ 13C) 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 (δ 13C) 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.

    Item Type: Article
    Keywords: Bayesian hierarchical model; reconstructing relative sea level; raw data; rates of change;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Item ID: 14574
    Identification Number: https://doi.org/10.5194/cp-12-525-2016
    Depositing User: Niamh Cahill
    Date Deposited: 29 Jun 2021 16:03
    Journal or Publication Title: Climate of the Past
    Publisher: European Geosciences Union (EGU)
    Refereed: Yes
    URI:

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