Modeling sea-level change using errors-in-variables integrated Gaussian processes
Niamh Cahill, Andrew C. Kemp, Benjamin P. Horton, Andrew C. Parnell

TL;DR
This paper introduces a Bayesian Gaussian process model that accounts for multiple uncertainties, including age errors, to analyze historical and recent sea-level change data, revealing unprecedented recent rise rates.
Contribution
It presents a novel errors-in-variables Gaussian process framework for modeling sea-level change with comprehensive uncertainty quantification.
Findings
Sea-level rise rate increased from 1.13 mm/yr in 1880 to 1.92 mm/yr in 2009
2000 AD rise rate in North Carolina is 2.44 mm/yr, unprecedented in 2000 years
Model effectively captures continuous sea-level change with uncertainty considerations.
Abstract
We perform Bayesian inference on historical and late Holocene (last 2000 years) rates of sea-level change. The input data to our model are tide-gauge measurements and proxy reconstructions from cores of coastal sediment. These data are complicated by multiple sources of uncertainty, some of which arise as part of the data collection exercise. Notably, the proxy reconstructions include temporal uncertainty from dating of the sediment core using techniques such as radiocarbon. The model we propose places a Gaussian process prior on the rate of sea-level change, which is then integrated and set in an errors-in-variables framework to take account of age uncertainty. The resulting model captures the continuous and dynamic evolution of sea-level change with full consideration of all sources of uncertainty. We demonstrate the performance of our model using two real (and previously published)…
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Taxonomy
TopicsReservoir Engineering and Simulation Methods
