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.
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.
the Creative Commons Attribution 3.0 License.
A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change
Niamh Cahill
CORRESPONDING AUTHOR
School of Mathematics and Statistics, CASL, Earth Institute, University College Dublin, Ireland
Dept. of Biostatistics and Epidemiology, School of Public Health, University of Massachusetts Amherst, USA
Andrew C. Kemp
Dept. of Earth and Ocean Sciences, Tufts University, USA
Benjamin P. Horton
Department of Marine & Coastal Sciences and Institute of Earth, Ocean, & Atmospheric Sciences, Rutgers University, USA
The Earth Observatory of Singapore and the Asian School of the Environment, Nanyang Technological University, Singapore
Andrew C. Parnell
School of Mathematics and Statistics, CASL, Earth Institute, University College Dublin, Ireland
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- Reproducibility and variability of earthquake subsidence estimates from saltmarshes of a Cascadia estuary J. Padgett et al. 10.1002/jqs.3446
- Revising Estimates of Spatially Variable Subsidence during the A.D. 1700 Cascadia Earthquake Using a Bayesian Foraminiferal Transfer Function A. Kemp et al. 10.1785/0120170269
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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.
We propose a Bayesian model for the reconstruction and analysis of former sea levels. The model...