A noisy-input generalised additive model for relative sea-level change along the Atlantic coast of North America

TL;DR

This paper introduces a Bayesian spatial-temporal model that accurately estimates regional sea-level changes over the past 3000 years along North America's Atlantic coast, accounting for measurement errors and local variations.

Contribution

It presents a novel noisy-input generalized additive model that integrates proxy and instrumental data with measurement uncertainties for sea-level analysis.

Findings

Decomposition of sea-level change over 3000 years.

Probabilistic estimates of regional and local sea-level drivers.

Effective handling of proxy data uncertainties.

Abstract

We propose a Bayesian, noisy-input, spatial-temporal generalised additive model to examine regional relative sea-level (RSL) changes over time. The model provides probabilistic estimates of component drivers of regional RSL change via the combination of a univariate spline capturing a common regional signal over time, random slopes and intercepts capturing site-specific (local), long-term linear trends and a spatial-temporal spline capturing residual, non-linear, local variations. Proxy and instrumental records of RSL and corresponding measurement errors inform the model and a noisy-input method accounts for proxy temporal uncertainties. Results focus on the decomposition of RSL over the past 3000 years along the Atlantic coast of North America.