Articles | Volume 12, issue 2
https://doi.org/10.5194/cp-12-525-2016
https://doi.org/10.5194/cp-12-525-2016
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

Viewed

Total article views: 4,848 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,888 1,709 251 4,848 201 246
  • HTML: 2,888
  • PDF: 1,709
  • XML: 251
  • Total: 4,848
  • BibTeX: 201
  • EndNote: 246
Views and downloads (calculated since 16 Oct 2015)
Cumulative views and downloads (calculated since 16 Oct 2015)
Latest update: 21 Dec 2025
Download
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.
Share