CAREER: Synthesizing Structural Uncertainty of Sea-level Rise Projections to Improve Application in Decision Making

Rowan University, Glassboro NJ

Investigators

Abstract

Coastal populations and economic assets have increased steadily in recent decades and are likely to continue to do so. At the same time, coastal communities are facing increasing threats from climate related sea level rise (SLR). Thus, there is a rapidly growing need for both global and local SLR projections with uncertainties that reflect our scientific knowledge. This CAREER award will meet the challenge by using the wealth of information contained in four decades of SLR science to improve the scientific community’s understanding of sources of uncertainty within SLR projections. The project will analyze previous SLR science to improve the scientific community’s understanding of sources of uncertainty within SLR projections at global and local scales. The project will support development of a publicly accessible interactive website (a “SLR Dashboard”) that will serve as a central hub for comprehensive SLR projection databases, allowing users immediate access to key SLR projection data, visualizations, and statistical tools. By improving our understanding of the uncertainty that currently exists within projections of future SLR, this work will enhance our ability to develop adaptation and resiliency strategies in coastal communities around the globe. Undergraduate students at Rowan University will be engaged in development and analysis of the databases and will share their work during events with public audiences. The project includes plans to develop children’s book with literary and educational experts that will teach broad audiences about rising sea levels in an engaging and accessible format. Four decades and ~10 cm of global mean SLR since the first global sea level projections were made, projections of future SLR remain deeply uncertain. Although a major source of uncertainty relates to the amount and timing of melt from the Antarctic Ice Sheet, additional uncertainty arises from problem formulation—choices in methodology used to project future SLR, and emissions scenarios considered. While the science behind SLR projections is constantly improving, differences amongst the large numbers of available SLR projections can make it difficult for decision makers to interpret the science and decide which projections they should use. By analyzing existing projections to better understand sources of structural uncertainty, we can reduce the impact of problem formulation upon SLR projection uncertainty over various time scales, improving the practical application of SLR projections in a wide variety of settings. The scientific goals of this project are to 1) further develop publicly-available databases that describe both local and global SLR projections, 2) use machine learning and statistical analyses to quantify how uncertainty manifests and evolves in SLR projections at various spatial and temporal scales, and 3) evaluate the sensitivity of various SLR projection methodologies to climate drivers to improve our understanding of the uncertainty within current projections of SLR. The project will also support the training and education of undergraduates in data analysis and sea level rise policy issues. The project will support development of a children's book about SLR. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.