Fusion of Probabilistic Projections of Sea-Level Rise
Benjamin S. Grandey (Nanyang Technological University)
J.H.G. Dauwels (TU Delft - Signal Processing Systems)
Zhi Yang Koh (Nanyang Technological University)
Benjamin P. Horton (Asian School of the Environment, Nanyang Technological University)
Lock Yue Chew (Nanyang Technological University)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
A probabilistic projection of sea-level rise uses a probability distribution to represent scientific uncertainty. However, alternative probabilistic projections of sea-level rise differ markedly, revealing ambiguity, which poses a challenge to scientific assessment and decision-making. To address the challenge of ambiguity, we propose a new approach to quantify a best estimate of the scientific uncertainty associated with sea-level rise. Our proposed fusion combines the complementary strengths of the ice sheet models and expert elicitations that were used in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC). Under a low-emissions scenario, the fusion's very likely range (5th–95th percentiles) of global mean sea-level rise is 0.3–1.0 m by 2100. Under a high-emissions scenario, the very likely range is 0.5–1.9 m. The 95th percentile projection of 1.9 m can inform a high-end storyline, supporting decision-making for activities with low uncertainty tolerance. By quantifying a best estimate of scientific uncertainty, the fusion caters to diverse users.
help_outline