Drivers of Interannual Sea Level Variability on the Northwestern European Shelf

Abstract

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.

21st century marine climate projections for the NW European Shelf Seas based on a Perturbed Parameter Ensemble

“…Satellite SSH data We evaluate the NWSPPE SSH following the methodology of , where we refer the reader for evaluation of PDCtrl SSH. We compare the mean pattern of the modelled sea level to satellite altimetry Mean Dynamic Topography (MDT), which indicates the average strength of the geostrophic currents (Hermans et al, 2020a). The pattern of the interannual SSH variability is compared to a satellite altimetry sea level anomaly product.…”

“…However, COx improves the representation of coastal ocean processes, and so does give additional information on sea level change (e.g. Hermans et al, 2020a, b;, but this is only part of the Sea Level puzzle (Palmer et al, 2018). 500…”

Using Shelf‐Edge Transport Composition and Sensitivity Experiments to Understand Processes Driving Sea Level on the Northwest European Shelf

“…In this study we seek to begin filling this gap for monthly to-interannual variability. We follow the approach of Tinker et al (2020) and Hermans et al (2020) in conducting perturbation experiments but focus on higher frequency variability (monthly means) using a NEMO based NWES regional model with reanalysis forcing. To these studies we also add an analysis of the momentum equations around the shelfedge, which enables us to isolate both the dynamic processes and the DSL variability that results from different forcing.…”

Understanding the Natural Variability of Still Water Levels in the San Francisco Bay Over the Past 500 yr: Implications for Future Coastal Flood Risk

“…Coastal flooding depends on variability in total water levels (TWLs) resulting from processes that vary over a range of spatiotemporal scales (Anderson et al., 2019; Barnard et al., 2019; Leonard et al., 2014). These include (a) regional relative SLR, (b) storm surge, (c) wave setup and swash, (d) high‐frequency (i.e., diurnal cycles, Haigh et al., 2020), and low‐frequency (4.4 yr perigean and 18.6 yr nodal cycles; Peng et al., 2019; Rashid et al., 2021; Thompson et al., 2021) fluctuations in astronomical tides, and ( v ) non‐tidal variations in mean sea level (MSL) due to seasonal, interannual, and decadal fluctuations in atmospheric and oceanographic forcing (Anderson et al., 2019; Barnard et al., 2019; Calafat et al., 2013; Dangendorf et al., 2014; Hermans et al., 2020; Merrifield et al., 2012; Orton et al., 2016; Ray & Foster, 2016; Sweet & Park, 2014; Taherkhani et al., 2020; Thomson et al., 2021; Vitousek et al., 2017; Wahl & Chambers, 2016). These processes can either dampen or amplify TWLs based on the synchronization of their phases.…”