Framework for High‐End Estimates of Sea Level Rise for Stakeholder Applications

Abstract

An approach to analyze high-end sea level rise is presented to provide a conceptual framework for high-end estimates as a function of time scale, thereby linking robust sea level science with stakeholder needs. Instead of developing and agreeing on a set of high-end sea level rise numbers or using an expert consultation, our effort is focused on the essential task of providing a generic conceptual framework for such discussions and demonstrating its feasibility to address this problem. In contrast, information about high-end sea level rise projections was derived previously either from a likely range emerging from the highest view of emissions in the Intergovernmental Panel on Climate Change assessment (currently the Representative Concentration Pathway 8.5 scenario) or from independent ad hoc studies and expert solicitations. Ideally, users need high-end sea level information representing the upper tail of a single joint sea level frequency distribution, which considers all plausible yet unknown emission scenarios as well as involved physical mechanisms and natural variability of sea level, but this is not possible. In the absence of such information we propose a framework that would infer the required information from explicit conditional statements (lines of evidence) in combination with upper (plausible) physical bounds. This approach acknowledges the growing uncertainty in respective estimates with increasing time scale. It also allows consideration of the various levels of risk aversion of the diverse stakeholders who make coastal policy and adaptation decisions, while maintaining scientific rigor.

Likely and High-End Impacts of Regional Sea-Level Rise on the Shoreline Change of European Sandy Coasts Under a High Greenhouse Gas Emissions Scenario

“…High-ends are defined as plausible-although unlikely-high-impact sea-level scenarios [28]. There is still no unique approach in the sea-level literature on how to quantify high-ends today.…”

“…There is still no unique approach in the sea-level literature on how to quantify high-ends today. In fact, different lines of evidence can be used to define potential future contributions and to combine them [28]. High-end scenarios are often designed in a probabilistic frame based on: (i) Representative Concentration Pathway (RCP) scenarios of the IPCC, (ii) assumptions with regard to physical processes to be considered (e.g., drivers of Antarctic ice-sheet melting) and (iii) particular subsets of models [72].…”

“…In contrast, in the case of high-risk aversion, the uncertainty tolerance is low and, therefore, robust decision-making could be required [26]. Probabilistic projections cannot be used within robust decision-making approaches because ensuring robustness implies testing adaptation options against any plausible scenarios, whereas the tail of sea-level projections is highly uncertain [22,23,28,29]. Instead, one should consider high-end projections or scenarios, which explore plausible-although unlikely-high impact sea-level scenarios beyond the likely range [28].…”

“…Probabilistic projections cannot be used within robust decision-making approaches because ensuring robustness implies testing adaptation options against any plausible scenarios, whereas the tail of sea-level projections is highly uncertain [22,23,28,29]. Instead, one should consider high-end projections or scenarios, which explore plausible-although unlikely-high impact sea-level scenarios beyond the likely range [28]. In particular, high-end sea-level scenarios are particularly useful to plan the full range of coastal adaptation responses [26,27,30,31].Cascading effects from sea-level rise to coastal impacts are generally quantified with the aim of computing the most likely impacts [32,33].…”

The Role of Climate Sensitivity in Upper‐Tail Sea Level Rise Projections

“…The combination of reduced‐complexity climate and semiempirical sea level models can provide useful insights into probabilistic sea level projections, as it allows for a careful sampling of the parametric and other uncertainties. Recent applications include estimating likelihoods of SLR projections (Vega‐Westhoff et al, 2019; Wong et al, 2017) and informing coastal decision planning (Sriver et al, 2018; Stammer et al, 2019; Wong & Keller, 2017).…”