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Meeting user needs for sea level rise information: a decision analysis perspective

Meeting user needs for sea level rise information: a decision analysis perspective
Meeting user needs for sea level rise information: a decision analysis perspective

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.

climate service, coastal adaptation, robust decision making, sea-level rise information
2328-4277
320-337
Hinkel, Jochen
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Church, John A.
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Gregory, Jonathan M.
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Lambert, Erwin
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Le Cozannet, Gonéri
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Lowe, Jason
728c4904-22ef-448f-80d9-f319f5b513f0
McInnes, Kathleen L.
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Nicholls, Robert J.
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van der Pol, Thomas D.
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van de Wal, Roderik
447ffa62-61ee-46dc-a6fb-b37e78f7c44b
Hinkel, Jochen
9c7e8026-955c-42cd-9179-6113efbf1339
Church, John A.
808e97d4-860d-44c4-ab43-c71bd176d30b
Gregory, Jonathan M.
a33d2bf6-f135-491a-a765-52fdad67639a
Lambert, Erwin
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Le Cozannet, Gonéri
ac33ad15-3dbd-4166-a498-986cc747ab4f
Lowe, Jason
728c4904-22ef-448f-80d9-f319f5b513f0
McInnes, Kathleen L.
234ff52b-2dde-4723-8239-c08637217628
Nicholls, Robert J.
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van der Pol, Thomas D.
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van de Wal, Roderik
447ffa62-61ee-46dc-a6fb-b37e78f7c44b

Hinkel, Jochen, Church, John A., Gregory, Jonathan M., Lambert, Erwin, Le Cozannet, Gonéri, Lowe, Jason, McInnes, Kathleen L., Nicholls, Robert J., van der Pol, Thomas D. and van de Wal, Roderik (2019) Meeting user needs for sea level rise information: a decision analysis perspective. Earth's Future, 7 (3), 320-337. (doi:10.1029/2018EF001071).

Record type: Article

Abstract

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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Hinkel et al 2019 Earth's Future - Version of Record
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Accepted/In Press date: 26 February 2019
e-pub ahead of print date: 3 March 2019
Published date: March 2019
Keywords: climate service, coastal adaptation, robust decision making, sea-level rise information

Identifiers

Local EPrints ID: 430116
URI: https://eprints.soton.ac.uk/id/eprint/430116
ISSN: 2328-4277
PURE UUID: c63aca57-470d-407e-aa5e-07c8006e34a8
ORCID for Robert J. Nicholls: ORCID iD orcid.org/0000-0002-9715-1109

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Date deposited: 12 Apr 2019 16:30
Last modified: 15 Oct 2019 00:45

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Contributors

Author: Jochen Hinkel
Author: John A. Church
Author: Jonathan M. Gregory
Author: Erwin Lambert
Author: Gonéri Le Cozannet
Author: Jason Lowe
Author: Kathleen L. McInnes
Author: Thomas D. van der Pol
Author: Roderik van de Wal

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