Global warming in response to accumulation of human‐induced greenhouse gases inside the atmosphere has already caused several visible consequences, among them increase of the Earth's mean temperature and ocean heat content, melting of glaciers, and loss of ice from the Greenland and Antarctica ice sheets. Ocean warming and land ice melt in turn are causing sea level to rise. Sea level rise and its impacts on coastal zones have become a question of growing interest in the scientific community, as well as in the media and public. In this review paper, we summarize the most up‐to‐date knowledge about sea level rise and its causes, highlighting the regional variability that superimposes the global mean rise. We also present sea level projections for the 21st century under different warming scenarios. We next address the issue of the sea level rise impacts. We question whether there is already observational evidence of coastal impacts of sea level rise and highlight the fact that results differ from one location to another. This suggests that the response of coastal systems to sea level rise is highly dependent on local natural and human settings. We finally show that in spite of remaining uncertainties about future sea levels and related impacts, it becomes possible to provide preliminary assessment of regional impacts of sea level rise.

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.

Sea level rise (SLR) is a long‐lasting consequence of climate change because global anthropogenic warming takes centuries to millennia to equilibrate for the deep ocean and ice sheets. SLR projections based on climate models support policy analysis, risk assessment and adaptation planning today, despite their large uncertainties. The central range of the SLR distribution is estimated by process‐based models. However, risk‐averse practitioners often require information about plausible future conditions that lie in the tails of the SLR distribution, which are poorly defined by existing models. Here, a community effort combining scientists and practitioners builds on a framework of discussing physical evidence to quantify high‐end global SLR for practitioners. The approach is complementary to the IPCC AR6 report and provides further physically plausible high‐end scenarios. High‐end estimates for the different SLR components are developed for two climate scenarios at two timescales. For global warming of +2°C in 2100 (RCP2.6/SSP1‐2.6) relative to pre‐industrial values our high‐end global SLR estimates are up to 0.9 m in 2100 and 2.5 m in 2300. Similarly, for a (RCP8.5/SSP5‐8.5), we estimate up to 1.6 m in 2100 and up to 10.4 m in 2300. The large and growing differences between the scenarios beyond 2100 emphasize the long‐term benefits of mitigation. However, even a modest 2°C warming may cause multi‐meter SLR on centennial time scales with profound consequences for coastal areas. Earlier high‐end assessments focused on instability mechanisms in Antarctica, while here we emphasize the importance of the timing of ice shelf collapse around Antarctica. This is highly uncertain due to low understanding of the driving processes. Hence both process understanding and emission scenario control high‐end SLR.

Significance: This work provides a robust estimate of water-level (WL) changes in the Ganges–Brahmaputra–Meghna delta, driven by continental freshwater dynamics, vertical land motion, and sea-level rise. Through an unprecedented set of 101 gauges, we reconstruct WL variations since the 1970s and show that the WL across the delta increased slightly faster, ∼3 mm/y, than the global mean sea-level rise (∼2 mm/y). By combining satellite altimetry and WL reconstructions, we estimate that maximum expected rates of delta subsidence since the 1990s range from 1 to 7 mm/y. By 2100, even under a greenhouse gas emission mitigation scenario (RCP4.5), the subsidence could double the projected sea-level rise, making it reach 85 to 140 cm across the delta.

The dependence between extreme storm surges and wind waves is assessed statistically along the global coasts using the outputs of two numerical models consistently forced with the same atmospheric fields. We show that 55% of the world coastlines face compound storm surge wave extremes. Hence, for a given level of probability, neglecting these dependencies leads to underestimating extreme coastal water levels. Dependencies are dominant in midlatitudes and are likely underestimated in the tropics due to limited representation of tropical cyclones. Furthermore, we show that in half of the areas with dependence, the estimated probability of occurrence of coastal extreme water levels increases significantly when it is accounted for. Translated in terms of return periods, this means that along 30% of global coastlines, extreme water levels expected at most once in a century without considering dependence between storm surges and waves become a 1 in 50‐year event.

This study provides a literature‐based comparative assessment of uncertainties and biases in global to world‐regional scale assessments of current and future coastal flood risks, considering mean and extreme sea‐level hazards, the propagation of these into the floodplain, people and coastal assets exposed, and their vulnerability. Globally, by far the largest bias is introduced by not considering human adaptation, which can lead to an overestimation of coastal flood risk in 2100 by up to factor 1300. But even when considering adaptation, uncertainties in how coastal societies will adapt to sea‐level rise dominate with a factor of up to 27 all other uncertainties. Other large uncertainties that have been quantified globally are associated with socio‐economic development (factors 2.3–5.8), digital elevation data (factors 1.2–3.8), ice sheet models (factor 1.6–3.8) and greenhouse gas emissions (factors 1.6–2.1). Local uncertainties that stand out but have not been quantified globally, relate to depth‐damage functions, defense failure mechanisms, surge and wave heights in areas affected by tropical cyclones (in particular for large return periods), as well as nearshore interactions between mean sea‐levels, storm surges, tides and waves. Advancing the state‐of‐the‐art requires analyzing and reporting more comprehensively on underlying uncertainties, including those in data, methods and adaptation scenarios. Epistemic uncertainties in digital elevation, coastal protection levels and depth‐damage functions would be best reduced through open community‐based efforts, in which many scholars work together in collecting and validating these data.