Statistical modeling of rates and trends in Holocene relative sea level

TL;DR

This paper reviews and compares statistical models for analyzing Holocene relative sea level data, emphasizing hierarchical frameworks that separate measurement uncertainties from process variability to better understand spatio-temporal sea level changes.

Contribution

It introduces a unified hierarchical statistical framework for modeling RSL data, facilitating comparison of different approaches and highlighting the importance of non-parametric methods.

Findings

Hierarchical models clarify the separation of uncertainties and process variability.

Comparison of modeling approaches reveals differences in RSL trend estimates.

Non-parametric methods are recommended for complex spatio-temporal RSL patterns.

Abstract

Characterizing the spatio-temporal variability of relative sea level (RSL) and estimating local, regional, and global RSL trends requires statistical analysis of RSL data. Formal statistical treatments, needed to account for the spatially and temporally sparse distribution of data and for geochronological and elevational uncertainties, have advanced considerably over the last decade. Time-series models have adopted more flexible and physically-informed specifications with more rigorous quantification of uncertainties. Spatio-temporal models have evolved from simple regional averaging to frameworks that more richly represent the correlation structure of RSL across space and time. More complex statistical approaches enable rigorous quantification of spatial and temporal variability, the combination of geographically disparate data, and the separation of the RSL field into various components associated with different driving processes. We review the range of statistical modeling and analysis choices used in the literature, reformulating them for ease of comparison in a common hierarchical statistical framework. The hierarchical framework separates each model into different levels, clearly partitioning measurement and inferential uncertainty from process variability. Placing models in a hierarchical framework enables us to highlight both the similarities and differences among modeling and analysis choices. We illustrate the implications of some modeling and analysis choices currently used in the literature by comparing the results of their application to common datasets within a hierarchical framework. In light of the complex patterns of spatial and temporal variability exhibited by RSL, we recommend non-parametric approaches for modeling temporal and spatio-temporal RSL.