Meeting User Needs for Sea Level Rise Information: A Decision Analysis Perspective. Issue 3 (30th March 2019) - 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.; van de Wal, Roderik
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. Plain Language Summary: Information on future sea‐level rise (SLR) is needed for diverse coastal adaptation decisions such as deciding on how much sand to apply for counteracting beach erosion, designing the height and strength of coastal protection infrastructure, and planing future developments in the coastal zone. Different kinds of decisions thereby require different kinds of SLR information and not all kinds of information required can be delivered by the state‐of‐the‐art of sea‐level rise science. This paper addresses this problem from the points of view of both decision science and sea‐level rise science. We find that three kinds of SLR information can be produced to inform coastal decision making. First, probabilistic predictions of mean SLR can be produced for short term decisions (i.e., 2030‐2050) and some locations. Second, high‐end sea‐level rise scenarios chosen for different levels of uncertainty tolerance of decision makers can be developed by SLR experts assigning confidence levels to available SLR studies. Third, learning scenarios estimating what will be known about SLR at given points in the future can further improve decision making. The procedure elaborated in this paper can be applied to other types of climate information such as temperature or precipitation. Key Points: Different kinds of contexts require different kinds of sea level rise information to support coastal adaptation decision making Uncertainty intolerant users require high‐end and low‐end sea level rise scenarios produced for different levels of uncertainty intolerance Long‐term decisions can be improved through learning scenarios estimating what will be learned about sea level rise in the future