As a result of climate change, sea level is changing all over the world at unprecedented rates. Sea-level change can have significant impacts on coastal communities, infrastructure and global economy, as most of the major cities are located near to or at the coast. Rising sea levels can lead to, for instance, more severe and more frequent flooding, increasing coastal erosion and salt water intrusion. In addition, sea-level change can also influence coastal ecosystems, by altering the habitats of many plant and animals species. Therefore, it is crucial that we understand what is causing sea-level change and at what rate sea levels are changing. Global mean sea level has been rising at a rate of about 3.4 millimetres per year over the last 30 years. Regionally, however, sea level can be changing at a much higher or lower rate. That is because local processes, such as ocean dynamics and gravitational effects associated with continental ice mass changes, cause regional deviations from the global average. But what is causing sea level to change at a specific location? Is sea level changing because the oceans are warming, and thus expanding? Or because the ice from glaciers and ice sheets are melting? The attribution of sea-level change to these and other drivers can be done using a sea-level budget approach. Sea-level budget studies can be used to constrain missing or poorly known contributions and to validate climate models. While the global mean sea-level budget is considered closed within uncertainties, closing the budget on a regional to local scale is still challenging. In this thesis, I focused on the question: Can we close the regional sea-level budget in the satellite altimetry era on a sub-basin scale consistently for the entire world? For this, we need not only high quality observations of sea-level change and each component, but also of the uncertainties within each process. Therefore, in Chapter 2 and 3, I explored the main drivers of regional sea-level change, focusing on the uncertainty characterization of each component. I then looked at which spatial scale is optimal for analysing the regional sea-level budget, and compared the sum of the drivers with the total observed change in these regions in Chapter 4.@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

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.@en

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 twenty-first-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 the 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.

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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.

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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.

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