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Sea-level rise magnifies flood hazards, raising the question when adaptation measures need to be taken. Here, we quantify when the recurrence of extreme water level events will double due to projected sea-level rise. Reproducing the most common method based on extreme water levels observed with tide gauges, at least one third of the coastal locations are to expect a doubling of extremes within a decade. However, tide gauges are commonly placed in wave-sheltered harbours where the contribution of waves to water levels is much smaller than at nearby wave-exposed coastlines such as beaches and dikes. In this study, we quantify doubling times at a variety of idealised shorelines based on modelled tides, storm surges and waves. We apply an extreme value analysis that accounts for the joint probability of extreme storm surges and extreme waves. Our results indicate that doubling times at wave-exposed shorelines are longer than those in wave-sheltered harbours, allowing for more time to adapt to magnified flood hazards. The median doubling times of average water levels including parameterised wave set-up are 1.2 to 5 times longer than those of still water levels as observed with tide gauges. For instantaneous water levels including wave run-up, doubling times are an additional 30% to 100% longer. We conclude that tide gauge-based analyses underestimate adaptation times by underestimating the contribution of waves to extreme water levels, and provide a quantitative framework to guide adaptation policy at wave-exposed shorelines.

Changes in mean sea level (MSL) are a major, but not the unique, cause of changes in high-percentile sea levels (HSL), e.g. the annual 99.9th quantile of sea level (among other factors, climate variability may also have huge influence). To unravel the respective influence of each contributor, we propose to use structural time series models considering six major climate indices (CI) (Artic Oscillation, North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Southern Oscillation Index, Niño 1 + 2 and Niño 3.4) as well as a reconstruction of MSL. The method is applied to eight century-long tide gauges across the world (Brest (France), Newlyn (UK), Cuxhaven (Germany), Stockholm (Sweden), Gedser (Danemark), Halifax (Canada), San Francisco (US), and Honolulu (US)). The treatment within a Bayesian setting enables to derive an importance indicator, which measures how often the considered driver is included in the model. The application to the eight tide gauges outlines that MSL signal is a strong driver (except for Gedser), but is not unique. In particular, the influence of Artic Oscillation index at Cuxhaven, Stockholm and Halifax, and of Niño Sea Surface Temperature index 1 + 2 at San Francisco appear to be very strong as well. A similar analysis was conducted by restricting the time period of interest to the 1st part of the 20th century. Over this period, we show that the MSL dominance is lower, whereas an ensemble of CI contribute to a large part to HSL time evolution as well. The proposed setting is flexible and could be applied to incorporate any alternative predictive time series such as river discharge, tidal constituents or vertical ground motions where relevant.

Despite progresses in climate change science, projections of future sea-level rise remain highly uncertain, especially due to large unknowns in the melting processes affecting the ice-sheets in Greenland and Antarctica. Based on climate-models outcomes and the expertise of scientists concerned with these issues, the IPCC provided constraints to the quantiles of sea-level projections. Moreover, additional physical limits to future sea-level rise have been established, although approximately. However, many probability functions can comply with this imprecise knowledge. In this contribution, we provide a framework based on extra-probabilistic theories (namely the possibility theory) to model the uncertainties in sea-level rise projections by 2100 under the RCP 8.5 scenario. The results provide a concise representation of uncertainties in future sea-level rise and of their intrinsically imprecise nature, including a maximum bound of the total uncertainty. Today, coastal impact studies are increasingly moving away from deterministic sea-level projections, which underestimate the expectancy of damages and adaptation needs compared to probabilistic laws. However, we show that the probability functions used so-far have only explored a rather conservative subset of sea-level projections compliant with the IPCC. As a consequence, coastal impact studies relying on these probabilistic sea-level projections are expected to underestimate the possibility of large damages and adaptation needs.

Figures 3 and 4 of the article ‘Bounding probabilistic sea-level projections within the framework of the possibility theory’ display a minimum value for sea level rise of 15 cm by 2100 with respect to the 1986–2005 mean for the RCP 8.5. The value of 15 cm is consistent with sea level rise rates dropping back to velocities observed during the 20th century according to recent studies, but not to the current sea level rise velocity of 3.4 mm yr ^−1 , as incorrectly stated in the article. This error has no impact on the rest of the article, including its arguments and conclusions, but it is potentially confusing for scientists willing to reproduce the left side of figures 3 and 4. We apologise for any inconvenience caused.