A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change
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-...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
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2018
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| Online Access: | https://hdl.handle.net/10356/88094 http://hdl.handle.net/10220/46894 |
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| author | Cahill, Niamh Kemp, Andrew C. Horton, Benjamin Peter Parnell, Andrew C. |
| author2 | Asian School of the Environment |
| author_facet | Asian School of the Environment Cahill, Niamh Kemp, Andrew C. Horton, Benjamin Peter Parnell, Andrew C. |
| author_sort | Cahill, Niamh |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:42:44Z |
| format | Journal Article |
| id | ntu-10356/88094 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:42:44Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/880942020-09-26T21:27:20Z A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change Cahill, Niamh Kemp, Andrew C. Horton, Benjamin Peter Parnell, Andrew C. Asian School of the Environment Earth Observatory of Singapore Bayesian Transfer Function Calibration DRNTU::Science::Geology 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. Published version 2018-12-10T05:30:32Z 2019-12-06T16:55:51Z 2018-12-10T05:30:32Z 2019-12-06T16:55:51Z 2016 Journal Article 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 1814-9324 https://hdl.handle.net/10356/88094 http://hdl.handle.net/10220/46894 10.5194/cp-12-525-2016 en Climate of the Past © 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. 18 p. application/pdf |
| spellingShingle | Bayesian Transfer Function Calibration DRNTU::Science::Geology Cahill, Niamh Kemp, Andrew C. Horton, Benjamin Peter Parnell, Andrew C. A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change |
| title | A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change |
| title_full | A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change |
| title_fullStr | A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change |
| title_full_unstemmed | A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change |
| title_short | A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change |
| title_sort | bayesian hierarchical model for reconstructing relative sea level from raw data to rates of change |
| topic | Bayesian Transfer Function Calibration DRNTU::Science::Geology |
| url | https://hdl.handle.net/10356/88094 http://hdl.handle.net/10220/46894 |
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