Sea-level change is one of the most concerning climate change and global warming consequences, especially impacting coastal societies and environments. The spatial and temporal variability of sea level is neither linear nor globally uniform, especially in semi-enclosed basins such as the Mediterranean Sea, which is considered a hot spot regarding expected impacts related to climate change. This study investigates sea-level trends and their variability over the Mediterranean Sea from 1993 to 2019. We use gridded sea-level anomaly products from satellite altimetry for the total observed sea level, whereas ocean temperature and salinity profiles from reanalysis were used to compute the thermosteric and halosteric effects, respectively, and the steric component of the sea level. We perform a statistical change point detection to assess the spatial and temporal significance of each trend change. The linear trend provides a clear indication of the non-steric effects as the dominant drivers over the entire period at the Mediterranean Sea scale, except for the Levantine and Aegean sub-basins, where the steric component explains the majority of the sea-level trend. The main changes in sea-level trends are detected around 1997, 2006, 2010, and 2016, associated with Northern Ionian Gyre reversal episodes, which changed the thermohaline properties and water mass redistribution over the sub-basins.

Sea-level science has seen many recent developments in observations and modelling of the different contributions and the total mean sea-level change. In this overview, we discuss (1) the evolution in IPCC projections, (2) how the projections compare to observations and (3) the outlook for further improving projections. We start by discussing how the model projections of 21 st century sea-level change have changed from the IPCC AR5 report (2013) to SROCC (2019) and AR6 (2021), highlighting similarities and differences in the methodologies and comparing the global mean and regional projections. This shows that there is good agreement in the median values, but also highlights some

Sea level on the northwestern European shelf (NWES) varies substantially from year to year. Removing explained parts of interannual sea level variability from observations helps to improve estimates of long-term sea level trends. To this end, the contributions of different drivers to interannual sea level variability need to be understood and quantified. We quantified these contributions for the entire NWES by performing sensitivity experiments with a high-resolution configuration of the Regional Ocean Modeling System (ROMS). The lateral and atmospheric boundary conditions were derived from reanalyses. We compared our model results with satellite altimetry data and used our sensitivity experiments to show that nonlinear feedbacks cause only minor interannual sea level variability on the shelf. This indicates that our experiments can be used to separate the effects of different drivers. We find that wind dominates the variability of annual mean sea level in the southern and eastern North Sea (up to 4.7-cm standard deviation), whereas the inverse barometer effect dominates elsewhere on the NWES (up to 1.7-cm standard deviation). In contrast, forcing at the lateral ocean boundaries results in small and coherent variability on the shelf (0.5-cm standard deviation). Variability driven by buoyancy fluxes ranges from 0.5-to 1.3-cm standard deviation. The results of our sensitivity experiments explain the (anti)correlation between interannual sea level variability at different locations on the NWES and can be used to estimate sea level rise from observations in this region with higher accuracy. Plain Language Summary Sea level on the continental shelf northwest of Europe is rising in the long term but is also varying strongly from year to year. This makes it difficult to determine the rate of sea level rise from observations. To improve long-term trends computed from sea level observations, the causes of short-term sea level variability need to be understood. Therefore, we test the influences of different components of the atmosphere and ocean on year-to-year sea level variability, using a numerical ocean model for northwestern Europe. We find that the varying strength and direction of winds causes large variability of sea level in the southern and eastern North Sea. In other places on the continental shelf, sea level variability is mainly influenced by the variability of atmospheric pressure. We also find that the ocean outside our model domain drives small and uniform sea level variability on the continental shelf and that there is a moderate influence of solar radiation, precipitation, and evaporation. Our results help to understand how sea level varies at different locations in northwestern Europe and to obtain better estimates of the rate of long-term sea level rise.

Projections of relative sea-level change (RSLC) are commonly reported at an annual mean basis. The seasonality of RSLC is often not considered, even though it may modulate the impacts of annual mean RSLC. Here, we study seasonal differences in 21st-century ocean dynamic sea-level change (DSLC, 2081-2100 minus 1995-2014) on the Northwestern European Shelf (NWES) and their drivers, using an ensemble of 33 CMIP6 models complemented with experiments performed with a regional ocean model. For the high-end emissions scenario SSP5-8.5, we find substantial seasonal differences in ensemble mean DSLC, especially in the southeastern North Sea. For example, at Esbjerg (Denmark), winter mean DSLC is on average 8.4 cm higher than summer mean DSLC. Along all coasts on the NWES, DSLC is higher in winter and spring than in summer and autumn. For the low-end emissions scenario SSP1-2.6, these seasonal differences are smaller. Our experiments indicate that the changes in winter and summer sea-level anomalies are mainly driven by regional changes in wind-stress anomalies, which are generally southwesterly and east-northeasterly over the NWES, respectively. In spring and autumn, regional wind-stress changes play a smaller role. We also show that CMIP6 models not resolving currents through the English Channel cannot accurately simulate the effect of seasonal wind-stress changes on he NWES. Our results imply that using projections of annual mean RSLC may underestimate the projected changes in extreme coastal sea levels in spring and winter. Additionally, changes in the seasonal sea-level cycle may affect groundwater dynamics and the inundation characteristics of intertidal ecosystems.

Recent studies disagree about the contribution of variations in temperature and salinity of the oceans-steric change-to the observed sea-level change. This article explores two sources of uncertainty to both global mean and regional steric sea-level trends. First, we analyze the influence of different temperature and salinity data sets on the estimated steric sea-level change. Next, we investigate the impact of different stochastic noise models on the estimation of trends and their uncertainties. By varying both the data sets and noise models, the global mean steric sea-level trend and uncertainty can vary from 0.69 to 2.40 and 0.02 to 1.56 mm/year, respectively, for 1993-2017. This range is even larger on regional scales, reaching up to 30 mm/year. Our results show that a first-order autoregressive model is the most appropriate choice to describe the residual behavior of the ensemble mean of all data sets for the global mean steric sea-level change over the last 25 years, which consequently leads to the most representative uncertainty. Using the ensemble mean and the first-order autoregressive noise model, we find a global mean steric sea-level change of 1.36 ± 0.10 mm/year for 1993-2017 and 1.08 ± 0.07 mm/year for 2005-2015. Regionally, a combination of different noise models is the best descriptor of the steric sea-level change and its uncertainty. The spatial coherence in the noise model preference indicates clusters that may be best suited to investigate the regional sea-level budget. Plain Language Summary Ocean temperature and salinity variations lead to changes in sea level, known as steric sea-level change. Steric variations are important contributors to sea-level change and reflect how the oceans have been responding to global warming. For this reason, several recent studies have quantified the contribution of steric variations to global and regional sea-level change. However, the reported rates largely differ between studies. In this paper, we look at how the use of different temperature and salinity data sets can be one of the causes of the different estimates of steric sea-level change published so far. We also investigate how different methods (noise models) used to obtain the rate of change can be another source of different results. We find that the rate of change can vary up to 2 mm/year for the global mean as a result of different data sets and methods used. Regionally, differences can reach up to several tens of millimeters per year. We show that the noise models should always be carefully chosen for each region, so that the rate of change is accurately estimated.

The interaction between the incoming winds with high mountainous islands produces a wind-sheltered area in the leeward side, known as the atmospheric wake. In addition to weaker winds, the wake is also characterized by a clearing of clouds, resulting in intense solar radiation reaching the sea surface. As a consequence, a warm oceanic wake forms on the leeward side. This phenomenon detectable from space can extend 100 km offshore of Madeira, where the sea surface temperature can be 4⁰C higher than the surrounding oceanic waters. This study considers in-situ, remote sensing, and ocean circulation model data, to investigate the effects of the warm wake in the vertical structure of the upper ocean. To characterize the convective layer (25-70m) developing within the oceanic wake, 200 vertical profiles of temperature, salinity and turbulence were considered, together with the computation of the Density Ratio and Turner-angle. In comparison to the open-ocean water column, wake waters are strongly stratified with respect to temperature although highly unstable. The vertical profiles of salinity show distinct water parcels that sink and/or rise as a response to the intense heat fluxes. During the night, the ocean surface cools, leading to the stretching of the mixed layer which was replicated by the ocean circulation model. In exposed, non-wake regions however, particularly in the southeast and north coast of the island, the stretching of the mixed layer is not detectable.

Abstract. Ocean mass change is one of the main drivers of present-day sea-level change (SLC). Also known as barystatic SLC, ocean mass change is caused by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage. While many studies have quantified the present-day barystatic contribution to global mean SLC, fewer works have looked into regional changes. This study provides an analysis of regional patterns of contemporary mass redistribution associated with barystatic SLC since 1993 (the satellite altimetry era), with a focus on the uncertainty budget. We consider three types of uncertainties: intrinsic (the uncertainty from the data/model itself), temporal (related to the temporal variability in the time series) and spatial–structural (related to the spatial distribution of the mass change sources). Regional patterns (fingerprints) of barystatic SLC are computed from a range of estimates of the individual freshwater sources and used to analyze the different types of uncertainty. Combining all contributions, we find that regional sea-level trends range from −0.4 to 3.3 mm yr−1 for 2003–2016 and from −0.3 to 2.6 mm yr−1 for 1993–2016, considering the 5–95th percentile range across all grid points and depending on the choice of dataset. When all types of uncertainties from all contributions are combined, the total barystatic uncertainties regionally range from 0.6 to 1.3 mm yr−1 for 2003–2016 and from 0.4 to 0.8 mm yr−1 for 1993–2016, also depending on the dataset choice. We find that the temporal uncertainty dominates the budget, responsible on average for 65 % of the total uncertainty, followed by the spatial–structural and intrinsic uncertainties, which contribute on average 16 % and 18 %, respectively. The main source of uncertainty is the temporal uncertainty from the land water storage contribution, which is responsible for 35 %–60 % of the total uncertainty, depending on the region of interest. Another important contribution comes from the spatial–structural uncertainty from Antarctica and land water storage, which shows that different locations of mass change can lead to trend deviations larger than 20 %. As the barystatic SLC contribution and its uncertainty vary significantly from region to region, better insights into regional SLC are important for local management and adaptation planning.

Abstract. 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.