Rethinking Sea‐Level Projections Using Families and Timing Differences

Abstract

In the recent decade, many global and regional sea‐level rise (SLR) projections have been published, which raises questions for users. Here, we present a series of strategies to help users to see the forest for the trees, by reducing the number of choices to be made. First, we use the similarities in the methodologies and contributing sources of the projections to group 82 projections from 29 publications into only 8 families. Second, we focus on the timing of reaching several global mean SLR thresholds, and the uncertainty therein, rather than the projected value in the year 2100. Finally, we combine the information on timing and families to define three categories which can support decision making. For global mean SLR up to 0.50 m the differences in timing are small, regardless of climate scenario or family, clearly indicating a timing window for adaptation decisions. For larger global mean SLR (0.75–1.00 m), the climate scenario becomes more important for the uncertainties in timing, but the SLR threshold will be crossed within a limited window of time, supporting adaptation decision making on the medium to long term. Beyond 1.00 m the differences between the families and climate scenario strongly determine the uncertainties in timing, and more information is needed, for instance using early warning signals, before decisions for adaptation can be made. We make recommendations on how each of these three categories, combined with the lead time and lifespan of adaptation options, can inform decisions on adaptation strategies using adaptation pathways planning.

Global survey shows planners use widely varying sea-level rise projections for coastal adaptation

“…However, a single number is eventually needed in many contexts, especially by engineers designing coastal infrastructure. This number should arise out of careful consideration of a range of projections during the asset life cycle including high-end estimates for risk-averse decisions 50 and timing windows to exceed design thresholds 51 . We recognize that the use case 52 of our respondents would shed further light on the structures and selected projections.…”

Fusion of Probabilistic Projections of Sea-Level Rise

“…A fusion of relative sea-level change projections could be combined with extreme value statistics to project changes in coastal flooding (Hermans et al, 2023;Hieronymus & Kalén, 2022;Kopp et al, 2014;Vousdoukas et al, 2018). The fusion could be used to investigate the timing of when thresholds may be crossed (Hermans et al, 2023;Slangen et al, 2022). The fusion could be used to investigate economic impacts, such as impairment of housing markets due to inundation (Rodziewicz et al, 2022).…”

Sea Level Rise Learning Scenarios for Adaptive Decision‐Making Based on IPCC AR6

“…In the domain of sea level rise, learning scenarios have rarely been applied and the majority of adaptation decision analyses use static sea level rise scenarios (van der Pol & Hinkel, 2019). Even though recent publications emphasize the option to wait for further knowledge about rising sea levels under appropriate circumstances (Slangen et al., 2022), only very few learning scenarios for sea level rise exist. The few existing sea level rise learning scenarios are generated by random sampling of distribution parameters (Woodward et al., 2014), a stochastic process (Gersonius et al., 2013), updating distribution parameters (Linquiti & Vonortas, 2012), simple physical models (Webster et al., 2008) or ad‐hoc assumptions (van der Pol et al., 2013).…”