Fusion of Probabilistic Projections of Sea-Level Rise

Benjamin S. Grandey; Justin Dauwels; Zhi Yang Koh; Benjamin P. Horton; Lock Yue Chew

doi:10.1029/2024EF005295

Fusion of Probabilistic Projections of Sea-Level Rise

Benjamin S. Grandey^*
, Justin Dauwels
, Zhi Yang Koh
, Benjamin P. Horton
, Lock Yue Chew

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

6 Citations (Scopus)

2 Downloads (CityUHK Scholars)

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. © 2024. The Author(s).

Original language	English
Article number	e2024EF005295
Journal	Earth's Future
Volume	12
Issue number	12
Online published	11 Dec 2024
DOIs	https://doi.org/10.1029/2024EF005295
Publication status	Published - Dec 2024
Externally published	Yes

Funding

This Research/Project is supported by the National Research Foundation, Singapore, and National Environment Agency, Singapore under the National Sea Level Programme Funding Initiative (Award USS\u2010IF\u20102020\u20103). BPH was also supported by Singapore Ministry of Education (MOE) Academic Research Fund Tier 3 Project MOE\u20102019\u2010T3\u20101\u2010004. We thank the projection authors for developing and making the sea level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea Level Change Team for developing and hosting the IPCC AR6 Sea Level Projection Tool. BSG thanks Robert Kopp for providing helpful advice about the IPCC AR6 projections data. BSG thanks Dhrubajyoti Samanta, Jennifer Weeks, Trina Ng, Nidheesh Gangadharan, and Steven Sherwood for helpful discussions. This work comprises Earth Observatory of Singapore contribution number 629.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Research Keywords

epistemic uncertainty
expert judgment
ice sheet
p-box
probabilistic climate projections
sea level

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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title = "Fusion of Probabilistic Projections of Sea-Level Rise",

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. {\textcopyright} 2024. The Author(s).",

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author = "Grandey, \{Benjamin S.\} and Justin Dauwels and Koh, \{Zhi Yang\} and Horton, \{Benjamin P.\} and Chew, \{Lock Yue\}",

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T1 - Fusion of Probabilistic Projections of Sea-Level Rise

AU - Grandey, Benjamin S.

AU - Dauwels, Justin

AU - Koh, Zhi Yang

AU - Horton, Benjamin P.

AU - Chew, Lock Yue

PY - 2024/12

Y1 - 2024/12

N2 - 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. © 2024. The Author(s).

AB - 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. © 2024. The Author(s).

KW - epistemic uncertainty

KW - expert judgment

KW - ice sheet

KW - p-box

KW - probabilistic climate projections

KW - sea level

UR - https://www.scopus.com/pages/publications/85211487055

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85211487055&origin=recordpage

U2 - 10.1029/2024EF005295

DO - 10.1029/2024EF005295

M3 - RGC 21 - Publication in refereed journal

SN - 2328-4277

VL - 12

JO - Earth's Future

JF - Earth's Future

IS - 12

M1 - e2024EF005295

ER -

Fusion of Probabilistic Projections of Sea-Level Rise

Abstract

Funding

UN SDGs

Research Keywords

Publisher's Copyright Statement

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