Mean sea-level rise (MSLR) will induce shoreline recession, increasing the stress on coastal systems of high socio-economic and environmental values. Localized mega-nourishments are meant to alleviate erosion problems by diffusing alongshore over decades and thus feeding adjacent beaches. The 21-st century morphological evolution of the Delfland coast, where the Sand Engine mega-nourishment was built in 2011, was simulated with the Q2Dmorfo model to assess the Sand Engine capacity to protect the area against the effects of MSLR. The calibrated and validated model was forced with historical wave and sea-level data and MSLR projections until 2100 corresponding to different Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5). Results show that the Sand Engine diffusive trend will continue in forthcoming decades, with the feeding effect to adjacent beaches being less noticeable from 2050 onward. Superimposed to this alongshore diffusion, MSLR causes the shoreline to recede because of both passive-flooding and a net offshore sediment transport produced by wave reshaping and gravity. The existing feeding asymmetry enforces more sediment transport to the NE than to the SW, causing the former to remain stable whilst the SW shoreline retreats significantly, especially from 2050 onward. Sediment from the Sand Engine does not reach the beaches located more than 6 km to the SW, with a strong shoreline and profile recession in that area, as well as dune erosion. The uncertainties in the results are dominated by those related to the free model parameters up to 2050 whilst uncertainties in MSLR projections prevail from 2050 to 2100.@en

This study analyses the distribution of ocean wind waves in response to extratropi- cal cyclones over the western Mediterranean Sea. To this end we use an ERA40-based database of atmospheric cyclones and a 3-hourly wind wave hind- cast with high horizontal resolution (1/6  ) based on an ERA40 downscaled forcing for the region of study. The imprint of winds on surface waves is evaluated through composites of modelled significant wave height, surface wind and wave peak period collocated under the storms. Results highlight an asymmetric pattern that depends on the translational speed and size of the cyclonic perturbations. Uncer- tainties of the composites are at most 10% at 95% confidence interval, with an aver- age maximum perturbation of significant wave height near 2 m for those cyclones moving faster than 10 m/s @en

This work analyzes the coastal impacts of the combined effect of extreme waves and sea level extremes, including surges and projected mean sea level rise in Bocagrande, Cartagena (Colombia). Extreme waves are assessed from a wave reanalysis that are propagated from deep waters to the beach considering the hydrodynamic processes and taking into account the interaction between waves and the coastal elevation within the study area. First, we consider present sea level, storm surges and waves affecting the area. Next, we analyze the effect of sea level rise according to a moderate (RCP4.5) climate change scenario for the 21st century (years 2025, 2050, 2075, and 2100). The most pessimistic scenario (year 2100) yields a percentage of flooded area of 97.2%, thus revealing the major threat that represents sea level rise for coastal areas in the Caribbean Sea. @en

The rate at which global mean sea level (GMSL) rose during the 20th century is uncertain, with little consensus between various reconstructions that indicate rates of rise ranging from 1.3 to 2 mm•y-1. Here we present a 20th-century GMSL reconstruction computed using an area-weighting technique for averaging tide gauge records that both incorporates up-to-date observations of vertical land motion (VLM) and corrections for local geoid changes resulting from ice melting and terrestrial freshwater storage and allows for the identification of possible differences compared with earlier attempts. Our reconstructed GMSL trend of 1.1 ± 0.3 mm•y-1 (1s) before 1990 falls below previous estimates, whereas our estimate of 3.1 ± 1.4 mm•y-1 from 1993 to 2012 is consistent with independent estimates from satellite altimetry, leading to overall acceleration larger than previously suggested. This feature is geographically dominated by the Indian Ocean-Southern Pacific region, marking a transition from lower-than-average rates before 1990 toward unprecedented high rates in recent decades. We demonstrate that VLM corrections, area weighting, and our use of a common reference datum for tide gauges may explain the lower rates compared with earlier GMSL estimates in approximately equal proportion. The trends and multidecadal variability of our GMSL curve also compare well to the sum of individual contributions obtained from historical outputs of the Coupled Model Intercomparison Project Phase 5. This, in turn, increases our confidence in process-based projections presented in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.@en

Attribution of sea-level change to its different drivers is typically done using a sea-level budget approach. While the global mean sea-level budget is considered closed, closing the budget on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional budget has been mainly analysed on a basin-wide scale. Here we investigate the sea-level budget at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (self-organizing map, SOM) and a network detection approach (δ-MAPS). The extracted domains provide more spatial detail within the ocean basins and indicate how sea-level variability is connected among different regions. Using these domains we can close, within 1σ uncertainty, the sub-basin regional sea-level budget from 1993–2016 in 100 % and 76 % of the SOM and δ-MAPS regions, respectively. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass exchange between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g. the Gulf Stream region) the dynamic mass redistribution is significant. Regions where the budget cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. The use of the budget approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers.@en