A high-end sea level rise probabilistic projection including rapid Antarctic ice sheet mass loss

Bars, Dewi Le; Drijfhout, Sybren; Vries, Hylke de

doi:10.1088/1748-9326/aa6512

Cited by 142 publications

(152 citation statements)

References 27 publications

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“…The ranges are derived by combing lowest (i.e., RCP2.6) and highest (i.e., RCP8.5) cumulative probability distributions of studies with the same confidence levels into p‐boxes (shaded areas). The left panel shows the construction of a p‐box combining Kopp et al () and Le Bars et al (), both based on the DeConto and Pollard () and hence considered to be very low confidence studies. The right‐hand panel adds to this very low confidence p‐box (green area), a low confidence p‐box based on Kopp et al () and Jackson and Jevrejeva () (red area), and a medium confidence p‐box based on the likely ranges of Intergovernmental Panel on Climate Change Fifth Assessment Report (Church et al, ; blue area), assuming they follow a partial Gaussian distribution.…”

Section: Meeting the Identified Needsmentioning

confidence: 99%

“…Finally, the following three sets of low‐ to high‐end sea level rise scenarios for robust decision making are attained by choosing more extreme percentiles and lower confidence levels the lower a user's uncertainty tolerance is (i) the 17th and 83rd percentiles of the medium confidence p‐box for the user with a high level of uncertainty tolerance, (ii) the 5th and 95th percentiles of the low confidence p‐box for the user with a medium level of uncertainty tolerance, and (iii) the 5th and 95th percentiles of the very low confidence p‐box for the user with a low level of uncertainty tolerance. In the left panel, g* and g** refer to two interpretations of the Coupled Model Intercomparison Project Phase 5 ensemble uncertainty (see Le Bars et al, ).…”

Section: Meeting the Identified Needsmentioning

confidence: 99%

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Meeting User Needs for Sea Level Rise Information: A Decision Analysis Perspective

et al. 2019

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Despite widespread efforts to implement climate services, there is almost no literature that systematically analyzes users' needs. This paper addresses this gap by applying a decision analysis perspective to identify what kind of mean sea level rise (SLR) information is needed for local coastal adaptation decisions. We first characterize these decisions, then identify suitable decision analysis approaches and the sea level information required, and finally discuss if and how these information needs can be met given the state of the art of sea level science. We find that four types of information are needed: (i) probabilistic predictions for short‐term decisions when users are uncertainty tolerant; (ii) high‐end and low‐end SLR scenarios chosen for different levels of uncertainty tolerance; (iii) upper bounds of SLR for users with a low uncertainty tolerance; and (iv) learning scenarios derived from estimating what knowledge will plausibly emerge about SLR over time. Probabilistic predictions can only be attained for the near term (i.e., 2030–2050) before SLR significantly diverges between low and high emission scenarios, for locations for which modes of climate variability are well understood and the vertical land movement contribution to local sea levels is small. Meaningful SLR upper bounds cannot be defined unambiguously from a physical perspective. Low‐ to high‐end scenarios for different levels of uncertainty tolerance and learning scenarios can be produced, but this involves both expert and user judgments. The decision analysis procedure elaborated here can be applied to other types of climate information that are required for mitigation and adaptation purposes.

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Section: Meeting the Identified Needsmentioning

confidence: 99%

Section: Meeting the Identified Needsmentioning

confidence: 99%

Meeting User Needs for Sea Level Rise Information: A Decision Analysis Perspective

et al. 2019

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“…The ratio of correlation is based on an expert judgement analysis (Bamber and Aspinall, 2013). Le Bars et al (2017) concluded, based on symmetric contributions, that the combined PDFs becomes wider if the contributions are assumed to be correlated, suggesting that the shape of the distribution is also imported. .…”

Section: Discussionmentioning

confidence: 99%

Impact of asymmetric uncertainties in ice sheet dynamics on regional sea level projections

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et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Currently a paradigm shift is made from global averaged to spatially variable sea level change (SLC) projections. Traditionally, the contribution from ice sheet mass loss to SLC is considered to be symmetrically distributed. However, several assessments suggest that the probability distribution of dynamical ice sheet mass loss is asymmetrically distributed towards higher SLC values. Here we show how asymmetric probability distributions of dynamical ice sheet mass loss impact the high-end uncertainties of regional SLC projections across the globe. For this purpose we use distributions of dynamical ice sheet mass loss presented by Church et al. (2013), De Vries and Van de Wal (2015) and Ritz et al. (2015). The global average median can be 0.18 m higher compared to symmetric distributions based on IPCC-AR5, but the change in the global average 95th percentile SLC is considerably larger with a shift of 0.32 m. Locally the 90th, 95th and 97.5th SLC percentiles exceed +1.4, +1.6 and +1.8 m. The high-end percentiles of SLC projections are highly sensitive to the precise shape of the probability distributions of dynamical ice sheet mass loss. The shift towards higher values is of importance for coastal safety strategies as they are based on the high-end percentiles of projections.

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“…Once the shelves are removed, MISI can occur in some regions, leading to rapid retreat and ice loss. (2016), in combination with other processes that contribute to sea level rise, provides the basis of many recent probabilistic sea level rise estimates with high values for the upper end of the probability distribution function (e.g., Kopp et al, 2014;Le Bars et al, 2017). Trusel et al (2015) suggested that for the majority of ice shelves, melt rates are too small to generate enough surface melt before the end of the century under RCP4.5 conditions but that for RCP8.5 conditions a few ice shelves approach or exceed the necessary threshold.…”

Section: Why Is Consensus On High-end Scenarios Difficult To Achieve?mentioning

confidence: 99%

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

et al. 2019

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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.

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A high-end sea level rise probabilistic projection including rapid Antarctic ice sheet mass loss

Cited by 142 publications

References 27 publications

Meeting User Needs for Sea Level Rise Information: A Decision Analysis Perspective

Meeting User Needs for Sea Level Rise Information: A Decision Analysis Perspective

Impact of asymmetric uncertainties in ice sheet dynamics on regional sea level projections

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

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