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.

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.

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.