Generating sea-level information for coastal adaptation: a risk management perspective

<p><span>Despite the widespread need to use sea-level rise information in coastal adaptation decision making, the production of this information rarely starts from a decision making perspective. This constitutes a major gap, because the specific sea-level information needed for adaptation depends on the type of decision a coastal decision maker is facing. Recent work developed in the context of the World <span lang="en-GB">C</span><span lang="en-GB">limate</span><span lang="en-GB">R</span><span lang="en-GB">esearch</span>Program (WCRP) Grand Challenge “Regional Sea-Level Change and Coastal Impacts” and the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) of the Intergovernmental Panel on Climate Change (IPCC) has started to address this gap by drawing upon the decision analysis literature. This paper presents this work identifying what kind of mean sea-level rise (SLR) information is needed for local coastal adaptation decisions. A special emphasis is placed on the contributions of the melting of the ice sheets of Greenland and Antarctica to global mean SLR, as <span lang="en-GB">t</span><span lang="en-GB">hese processes </span>may contribute significantly to future SLR and, at the same time, are most uncertain. First, different types of coastal adaptation decisions are characterized in terms of decision horizons and users' <span lang="en-GB">u</span><span lang="en-GB">ncertainty</span>tolerance. Next, suitable decision analysis approaches and sea-level information required for these are identified. Finally it is discussed if and how these information needs can be met given the state-of-the-art of sea-level science. It is found 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) and 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 adaptation purposes.</span></p>