Evidence for increasing frequency of extreme coastal sea levels

TL;DR

This study compiles and analyzes 36 datasets of extreme sea levels along US coasts, demonstrating that models accounting for increasing frequency of extremes better fit the data than stationary models.

Contribution

It introduces a comprehensive dataset and analysis framework to evaluate how model structure influences estimates of extreme coastal sea levels under climate change.

Findings

Models incorporating changing extreme sea level frequency outperform stationary models.

Deep uncertainty in coastal hazard modeling is addressed through multiple climate variables.

Evidence suggests an increasing trend in extreme coastal sea levels.

Abstract

Projections of extreme sea levels (ESLs) are critical for managing coastal risks, but are made complicated by deep uncertainties. One key uncertainty is the choice of model structure used to estimate coastal hazards. Differences in model structural choices contribute to uncertainty in estimated coastal hazard, so it is important to characterize how model structural choice affects estimates of ESL. Here, we present a collection of 36 ESL data sets, from tide gauge stations along the United States East and Gulf Coasts. The data are processed using both annual block maxima and peaks-over-thresholds approaches for modeling distributions of extremes. We use these data sets to fit a suite of potentially nonstationary extreme value models by covarying the ESL statistics with multiple climate variables. We demonstrate how this data set enables inquiry into deep uncertainty surrounding coastal hazards. For all of the models and sites considered here, we find that accounting for changes in the frequency of coastal extreme sea levels provides a better fit than using a stationary extreme value model.