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

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

doi:https://doi.org/10.5194/cp-12-525-2016

Articles | Volume 12, issue 2

https://doi.org/10.5194/cp-12-525-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/cp-12-525-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 12, issue 2

Research article

|

29 Feb 2016

Research article |

| 29 Feb 2016

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

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

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Final revised paper (published on 29 Feb 2016)
Preprint (discussion started on 16 Oct 2015)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC C2405: 'Review', Anonymous Referee #1, 19 Nov 2015
- AC C2898: 'Response 1', Niamh Cahill, 14 Jan 2016
RC C2424: 'Referee comment', Robin Edwards, 20 Nov 2015
- AC C2904: 'Response 2', Niamh Cahill, 14 Jan 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to minor revisions (review by Editor) (17 Jan 2016) by Eduardo Zorita

AR by Niamh Cahill on behalf of the Authors (24 Jan 2016) Author's response

ED: Publish as is (02 Feb 2016) by Eduardo Zorita

AR by Niamh Cahill on behalf of the Authors (08 Feb 2016)

Short summary

We propose a Bayesian model for the reconstruction and analysis of former sea levels. The model provides a single, unifying framework for reconstructing and analyzing sea level through time with fully quantified uncertainty. We illustrate our approach using a case study of Common Era (last 2000 years) sea levels from New Jersey.