Interannual variability of mean sea level and its sensitivity to wind climate in an inter-tidal basin

Gerkema, Theo; Duran‐Matute, Matias

doi:10.5194/esd-8-1223-2017

Cited by 18 publications

(11 citation statements)

References 23 publications

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“…For this study, we use monthly-mean sea-level observations from 33 tide-gauge stations around the North Sea, the Norwegian coast, and the English Channel. The data have been obtained from the Permanent Service for Mean Sea Level (PSMSL) database (PSMSL, 2017;Holgate et al, 2013). We only use stations that are not flagged for possible problems and for which the data are provided in a revised local reference (RLR) to avoid stations with unstable datums.…”

Section: Methodsmentioning

confidence: 99%

Multi-decadal variability in seasonal mean sea level along the North Sea coast

Frederikse

Gerkema

2018

Ocean Sci.

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Section: Methodsmentioning

confidence: 99%

Multi-decadal variability in seasonal mean sea level along the North Sea coast

Frederikse

Gerkema

2018

Ocean Sci.

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“…The small effect of nonlinear feedbacks suggests that, to first order, the influences of different drivers of sea level variability on the shelf can be studied separately. This justifies the use of multiple (linear) regression methods in observation‐based studies (e.g., Calafat & Chambers, 2013; Dangendorf et al, 2013, 2014; Frederikse, Riva, Kleinherenbrink, et al, 2016; Gerkema & Duran‐Matute, 2017; Wahl et al, 2013).…”

Section: The Contributions Of Different Drivers To Interannual Sea Lementioning

confidence: 99%

“…Fluctuations of sea level on various time scales complicate detecting sea level trends from observations, and sea level variability will continue to be the dominant source of uncertainty of sea level change for the coming decades (Palmer et al, 2018). Removing explained parts of variability from sea level records can lead to more accurate estimates of trends and accelerations (e.g., Calafat & Chambers, 2013; Dangendorf et al, 2014; Gerkema & Duran‐Matute, 2017; Hogarth et al, 2020; Thompson, 1986; Wahl et al, 2013). Therefore, the processes driving interannual to multidecadal sea level variability on the NWES need to be understood.…”

Section: Introductionmentioning

confidence: 99%

Drivers of Interannual Sea Level Variability on the Northwestern European Shelf

et al. 2020

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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. 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.

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“…It should be noted that our coupled model does not account for winds and the gravitational circulation. According to recent studies, wind climate can significantly contribute to the long-term variability of regional water elevation (Arns et al, 2015;Gerkema and Duran-Matute, 2017). Density-driven flow can also dominate local transport pro-cesses (Geyer and MacCready, 2014;Burchard et al, 2018;Schulz and Gerkema, 2018).…”

Section: Discussionmentioning

confidence: 99%

Effects of sea-level rise on tides and sediment dynamics in a Dutch tidal bay

et al. 2020

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Sea-level rise (SLR) not only increases the threat of coastal flooding, but may also change tidal regimes in estuaries and coastal bays. To investigate such nearshore tidal responses to SLR, a hydrodynamic model of the European Shelf is downscaled to a model of a Dutch coastal bay (the Oosterschelde, i.e., Eastern Scheldt) and forced by SLR scenarios ranging from 0 to 2 m. This way, the effect of SLR on tidal dynamics in the adjacent North Sea is taken into account as well. The model setup does not include meteorological forcing, gravitational circulation, and changes in bottom topography. Our results indicate that SLR up to 2 m induces larger increases in tidal amplitude and stronger nonlinear tidal distortion in the bay compared to the adjacent shelf sea. Under SLR up to 2 m, the bay shifts from a mixed floodand ebb-dominant state to complete ebb dominance. We also find that tidal asymmetry affects an important component of sediment transport. Considering sand bed-load transport only, the changed tidal asymmetry may lead to enhanced export, with potential implications for shoreline management. In this case study, we find that local impacts of SLR can be highly spatially varying and nonlinear. The model coupling approach applied here is suggested as a useful tool for establishing local SLR projections in estuaries and coastal bays elsewhere. Future studies should include how SLR changes the bed morphology as well as the feedback effect on tides.

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Interannual variability of mean sea level and its sensitivity to wind climate in an inter-tidal basin

Cited by 18 publications

References 23 publications

Multi-decadal variability in seasonal mean sea level along the North Sea coast

Multi-decadal variability in seasonal mean sea level along the North Sea coast

Drivers of Interannual Sea Level Variability on the Northwestern European Shelf

Effects of sea-level rise on tides and sediment dynamics in a Dutch tidal bay

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