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Climate change affects the frequency and intensity of storms worldwide (IPCC, 2021). Observation-based studies have shown that surge water levels become more extreme and that surges occur more frequently (e.g. Menéndez & Woodworth, 2010) and model simulations indicate that this will go on/accelerate in the future (e.g. Flather & Williams, 2000; Tebaldi et al., 2012). Studies on such high-frequency water level variability mainly rely on observations from tide gauges that are restricted to coastal and shelf regions. However, recent studies have shown that by careful processing of satellite altimetry data it is possible to capture most of the storm surge water level variability that is observed at tide gauges (e.g. Andersen et al., 2013; Antony et al., 2014; Madsen et al., 2015). Therefore, in the presented study, the full length of satellite altimetry records (> 25 years) has been exploited to study temporal changes in surge water level variability. With doing so, insight is gained in the large-scale changes in surge water levels, alongside the primarily coastal changes that were observed at tide gauges. This could improve modelling of surge water levels at open sea and potentially improve forecasting of extreme water level events. By combining data from multiple missions, a maximum coverage of high-water level events is pursued. In addition, the combination of altimetry data processed by RADS (Scharroo et al., 2013) with coastal data from XTrack (Birol et al., 2021) ensures close to full coverage of the oceans. Moreover, the usage of coastal data from XTrack allows for comparison of surge water level variability derived from satellite altimetry and derived from tide gauges. This is used to define confidence levels of surge frequency and related water level estimates as derived from the satellite data. References Andersen, O. B., Cheng, Y., Deng, X., Steward, M., & Gharineiat, Z. (2015). Using satellite altimetry and tide gauges for storm surge warning. Proceedings of the International Association of Hydrological Sciences, 365, 28-34. Antony, C., Testut, L., & Unnikrishnan, A. S. (2014). Observing storm surges in the Bay of Bengal from satellite altimetry. Estuarine, Coastal and Shelf Science, 151, 131-140. Birol, F., Léger, F., Passaro, M., Cazenave, A., Niño, F., Calafat, F. M., ... & Benveniste, J. (2021). The X-TRACK/ALES multi-mission processing system: New advances in altimetry towards the coast. Advances in Space Research, 67(8), 2398-2415. Flather, R. A., & Williams, J. (2000). Climate change effects on storm surges: methodologies and results. Climate scenarios for water-related and coastal impacts, (3), 66-72. IPCC. 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press. Madsen, K. S., Høyer, J. L., Fu, W., & Donlon, C. (2015). Blending of satellite and tide gauge sea level observations and its assimilation in a storm surge model of the N orth S ea and B altic S ea. Journal of Geophysical Research: Oceans, 120(9), 6405-6418. Menéndez, M., & Woodworth, P. L. (2010). Changes in extreme high water levels based on a quasi‐global tide‐gauge data set. Journal of Geophysical Research: Oceans, 115(C10). Scharroo, R., Leuliette, E., Lillibridge, J., Byrne, D., Naeije, M., & Mitchum, G. (2013). RADS: Consistent multi-mission products. In 20 Years of Progress in Radar Altimetry (Vol. 710). Tebaldi, C., Strauss, B. H., & Zervas, C. E. (2012). Modelling sea level rise impacts on storm surges along US coasts. Environmental Research Letters, 7(1), 014032.

Satellite observations can help in the retrieval of constituents in shallow waters. Noise contamination, however, makes smaller constituents irretrievable and large sources of error. Throughout shallow areas, the constituent’s relevancy changes. For example, near an amphidromic point where M2 relevance drops, so does the potential of satellite contribution for improving its accuracy. Moreover, shallow waters are generally influenced by many constituents (>100). Accurately retrieving all these constituents with satellite radar altimeter data alone is not possible. Series length requirements imposed by the Rayleigh criteria to separate constituents are still unavailable. Removing unwanted signals from satellite observations improves least-squares-based harmonic estimates, given an inversion matrix with the same condition number. This variance reduction is the core of the remove compute restore approach commonly used. First, residual harmonic sets are computed with the difference between observations and model background estimates through conventional or weighted least-squares. Then, the residual harmonics are added to the background model estimates. Here we implemented a method that extends the typical approach by including model background estimate and error covariance in the least-squares step. This inclusion helps to weigh between constituents well represented in the model and those that must be updated. To test the method, we designed a semi-synthetic experiment. First, we used tide gauge data to generate a satellite equivalent dataset and compared estimations between the two methods listed above and the model estimate. Next, we applied the method to compute tidal estimates along satellite radar altimeter tracks (T/P Jason) in the 2D Dutch Coastal Shelf Model (DCSMv6) domain. Results from the synthetic experiment show that the second method produces consistently better estimates reducing RSS consistently through temporal cross-validation. In addition, it provides an effective way of keeping as many constituents estimates as the model series can resolve, adding the benefits of satellite observations. Finally, results from the North Sea implementation show the new estimates increase the variance reduction of satellite residuals across the whole domain relative to background tidal estimates. The range of improvements varies between 0 and 3cm, which is significant given already very accurate model background estimates. The benefited areas include the English Channel, the Irish Sea, the English North-Sea Coast, the Bay of Biscay, the German Bight, and the North Atlantic region close to the upper boundary of the model domain.

Title : Volume : Issue : Pagination : 149