Illustrative Analysis of Probabilistic Sea Level Rise Hazard

Date

2020-02

Journal Title

Journal ISSN

Volume Title

Publisher

American Meteorological Society

Abstract

Sea level rise results from several contributing physical processes, including ocean thermal expansion and glacier and ice sheet mass loss. Future projections of sea level remain highly uncertain due to several sources of aleatory and epistemic uncertainty. Quantifying different sources of sea level rise involves considering possible pathways of future radiative forcing and integrating models of different sea level rise processes. The probabilistic hazard analysis strategy has been proposed for combining sea level rise prediction models and climate forcing scenarios to examine sea level rise prediction uncertainty and the sources of this uncertainty. In this study we carry out an illustrative probabilistic sea level rise hazard analysis using ensembles of sea level rise predictions and emissions scenarios from the literature. This illustrative analysis allows us to estimate the probability that sea level rise will exceed a specified threshold at a given location and time and highlights how sea level rise uncertainty is sensitive to scenario inputs and sea level rise projection modeling choices. Probabilistic hazard is depicted for Earth using sea level rise hazard maps. We also demonstrate how hazard deaggregation can help us quantify the relative contributions of sea level rise sources, prediction models, and climate forcing scenarios to sea level rise hazard. The ice sheet contribution to sea level rise has a large impact on probabilistic projection of sea level rise due to the disagreements between current ice sheet models related to differences in modeling ice sheet instability.

Description

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Keywords

Sea Level, Sensitivity Studies, Bayesian Methods, Probability Forecasts/Models/Distribution, Ensembles

Citation

Thomas, M.A. & Lin, T. (2020). Illustrative analysis of probabilistic sea level rise hazard. Journal of Climate, 33(4), 1523-1234. https://doi.org/10.1175/JCLI-D-19-0320.1

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