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Near-future changes in storm surges along the Atlantic Iberian coast

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Abstract

Decadal predictions bridge the gap between the short-term weather/seasonal forecasts and the long-term climate projections. They target the reproduction of large-scale weather patterns at multi-year time scales by both recognizing the long memory of some components of the climate system, and explicitly including the evolution of greenhouse gas concentrations as an external forcing. This study illustrates the use of decadal predictions to determine the near-future storminess at regional scales. Specifically, the evolution of extreme storm surges and sea levels along the Atlantic Iberian coast is assessed. Present (1980–2016) and near-future (2021–2024) storm surges are simulated over the northeast Atlantic, forced by atmospheric reanalyses (ERA-Interim) and decadal predictions (MiKlip), respectively. Results are then statistically analyzed to investigate the short-term effects of climate change and climate variability on extreme surges and extreme sea levels. Surges will increase mostly in early winter, while tides are largest in late winter. As a result, the impact of the increase in storminess on the extreme sea levels and coastal flooding will be modest, and the growth in extreme sea levels will be dominated by the contribution of mean sea level rise.

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References

  • Andrade C, Pires O, Taborda R, Freitas MC (2007) Projecting future changes in wave climate and coastal response in Portugal by the end of the 21st century. J Coast Res 50:263–267

    Google Scholar 

  • Antunes C (2016) Subida do Nível Médio do Mar em Cascais, revisão da taxa actual. Actas das 4.as Jornadas de Engenharia Hidrográfica, Instituto Hidrográfico (in Portuguese). ISBN - 978-989-705-097-8

  • Bertin X, Bruneau N, Breilh J-F, Fortunato AB, Karpytchev M (2012) Importance of wave age and resonance in storm surges: the case Xynthia, Bay of Biscay. Ocean Model 42(1):16–30. https://doi.org/10.1016/j.ocemod.2011.11.001

    Article  Google Scholar 

  • Bordbar MH, Martin T, Latif M, Park W (2017) Role of internal variability in recent decadal to multidecadal tropical Pacific climate changes. Geophys Res Lett 44:4246–4255. https://doi.org/10.1002/2016GL072355

    Article  Google Scholar 

  • Camus P, Losada IJ, Izaguirre C, Espejo A, Menéndez M, Pérez J (2017) Statistical wave climate projections for coastal impact assessments. Earth’s Future 5(9):918–933. https://doi.org/10.1002/2017EF000609

    Article  Google Scholar 

  • Carrère L, Lyard F, Cancet M, Guillot A, Roblou L (2012) FES2012: A new global tidal model taking advantage of nearly 20 years of altimetry. In: Proceedings of meeting 20 years of altimetry

  • Charles E, Idier D, Delecluse P, Déqué M, Le Cozannet G (2012) Climate change impact on waves in the Bay of Biscay, France. Ocean Dyn 62(6):831–848. https://doi.org/10.1007/s1023601205348

    Article  Google Scholar 

  • Chaumillon E, Bertin X, Fortunato AB, Bajo M, Schneider J-L, Dezileau L, Walsh JP, Michelot A, Chauveau E, Créach A, Hénaff A, Sauzeau T, Waeles B, Gervais B, Jan G, Baumann J, Breilh J-F, Pedreros R (2017) Storm-induced marine flooding: lessons from a multidisciplinary approach. Earth Sci Rev 165:151–184. https://doi.org/10.1016/j.earscirev.2016.12.005

    Article  Google Scholar 

  • Dangendorf S, Marcos M, Wöppelmann G, Conrad CP, Frederikse T, Riva R (2017) Reassessment of 20th century global mean sea level rise. Proc Natl Acad Sci 114(23):5946–5951. https://doi.org/10.1073/pnas.1616007114

    Article  Google Scholar 

  • Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J-J, Park B-K, Peubey C, de Rosnay P, Tavolato C, Thépaut J-N, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. https://doi.org/10.1002/qj.828

    Article  Google Scholar 

  • Fortunato AB, Rodrigues M, Dias JM, Lopes C, Oliveira A (2013) Generating inundation maps for a coastal lagoon: a case study in the Ria de Aveiro (Portugal). Ocean Eng 64(1):60–71. https://doi.org/10.1016/j.oceaneng.2013.02.020

    Article  Google Scholar 

  • Fortunato AB, Li K, Bertin X, Rodrigues M, Miguez BM (2016) Determination of extreme sea levels along the Iberian Atlantic coast. Ocean Eng 111(1):471–482. https://doi.org/10.1016/j.oceaneng.2015.11.031

    Article  Google Scholar 

  • Fortunato AB, Freire P, Bertin X, Rodrigues M, Ferreira J, Liberato ML (2017) A numerical study of the February 15, 1941 storm in the Tagus estuary. Cont Shelf Res 144:50–64. https://doi.org/10.1016/j.csr.2017.06.023

    Article  Google Scholar 

  • Freire P, Tavares AO, Sá L, Oliveira A, Fortunato AB, Santos PP, Rilo A, Gomes JL, Rogeiro J, Pablo R, Pinto PJ (2016) A local-scale approach to estuarine flood risk management. Nat Hazards 84(3):1705–1739. https://doi.org/10.1007/s11069-016-2510-y

    Article  Google Scholar 

  • Freitas JG, Dias JA (2013) 1941 windstorm effects on the Portuguese Coast. What lessons for the future? J Coast Res 65:714–719

    Article  Google Scholar 

  • Garnier E, Ciavola P, Spencer T, Ferreira Ó, Armaroli C, McIvor A (2018) Historical analysis of storm events: case studies in France, England, Portugal and Italy. Coast Eng 134:10–23. https://doi.org/10.1016/j.coastaleng.2017.06.014

    Article  Google Scholar 

  • Giorgetta MA, Jungclaus J, Reick CH, Legutke S, Bader J, Böttinger M, Brovkin V, Crueger T, Esch M, Fieg K, Glushak K, Gayler V, Haak H, Hollweg H-D, Ilyina T, Kinne S, Kornblueh L, Matei D, Mauritsen T, Mikolajewicz U, Mueller W, Notz D, Pithan F, Raddatz T, Rast S, Redler R, Roeckner E, Schmidt H, Schnur R, Segschneider J, Six KD, Stockhause M, Timmreck C, Wegner J, Widmann H, Wieners KH, Claussen M, Marotzke J, Stevens B (2013) Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the coupled model intercomparison project phase 5. J Adv Model Earth Syst 5:572–597. https://doi.org/10.1002/jame.20038

    Article  Google Scholar 

  • Guérin T, Bertin X, Coulombier T, de Bakker A (2018) Impacts of wave-induced circulation in the surf zone on wave setup. Ocean Model 123:86–97. https://doi.org/10.1016/j.ocemod.2018.01.006

    Article  Google Scholar 

  • Guerreiro M, Fortunato AB, Freire P, Rilo A, Taborda R, Freitas MC, Andrade C, Silva T, Rodrigues M, Bertin X, Azevedo A (2015) Evolution of the hydrodynamics of the Tagus estuary (Portugal) in the 21st century. J Integr Coast Zone Manag 15(1):65–80. https://doi.org/10.5894/rgci515

    Article  Google Scholar 

  • Horsburgh KJ, Wilson C (2007) Tide-surge interaction and its role in the distribution of surge residuals in the North Sea. J Geophys Res. https://doi.org/10.1029/2006JC004033

    Article  Google Scholar 

  • Hosking JRM (1985) Algorithm AS2015: maximum-likelihood estimation of the parameters of the Generalized Extreme-Value distribution. J Roy Stat Soc: Ser C (Appl Stat) 34(3):301–310

    Google Scholar 

  • Idier D, Paris F, Le Cozannet G, Boulahya F, Dumas F (2017) Sea-level rise impacts on the tides of the European Shelf. Cont Shelf Res 137:56–71. https://doi.org/10.1016/j.csr.2017.01.007

    Article  Google Scholar 

  • Jacob D, Petersen J, Eggert B, Alias A, Christensen OB, Bouwer LM, Braun A, Colette A, Déqué M, Georgievski G, Georgopoulou E, Gobiet A, Menut L, Nikulin G, Haensler A, Hempelmann N, Jones C, Keuler K, Kovats S, Kröner N, Kotlarski S, Kriegsmann A, Martin E, van Meijgaard E, Moseley C, Pfeifer S, Preuschmann S, Radermacher C, Radtke K, Rechid D, Rounsevell M, Samuelsson P, Somot S, Soussana J-F, Teichmann C, Valentini R, Vautard R, Weber B, Yiou P (2014) EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change 14:563–578. https://doi.org/10.1007/s10113-013-0499-2

    Article  Google Scholar 

  • Kadow C, Illing S, Kunst O, Rust HW, Pohlmann H, Müller WA, Cubasch U (2016) Evaluation of forecasts by accuracy and spread in the MiKlip decadal climate prediction system. Meteorol Z 25(6):631–643

    Article  Google Scholar 

  • Kruschke T, Rust HW, Kadow C, Leckebusch GC, Ulbrich U (2014) Evaluating decadal predictions of northern hemispheric cyclone frequencies. Tellus A Dyn Meteorol Oceanogr 66(1–15):22830. https://doi.org/10.3402/tellusa.v66.22830

    Article  Google Scholar 

  • Kruschke T, Rust HW, Kadow C, Muller WA, Pohlmann H, Leckebusch GC, Ulbrich U (2016) Probabilistic evaluation of decadal prediction skill regarding Northern hemisphere winter storms. Meteorol Zeitschrift 25(6):721–738

    Article  Google Scholar 

  • Marcos M, Woodworth PL (2017) Spatiotemporal changes in extreme sea levels along the coasts of the North Atlantic and the Gulf of Mexico. J Geophys Res Oceans. https://doi.org/10.1002/2017JC013065

    Article  Google Scholar 

  • Marotzke J, Müller WA, Vamborg FS, Becker P, Cubasch U, Feldmann H, Kaspar F, Kottmeier C, Marini C, Polkova I, Prömmel K, Rust HW, Stammer D, Ulbrich U, Kadow C, Köhl A, Kröger J, Kruschke T, Pinto JG, Pohlmann H, Reyers M, Schröder M, Sienz F, Timmreck C, Ziese M (2016) MiKlip: a national research project on decadal climate prediction. Bull Am Meteorol Soc 97:2379–2394. https://doi.org/10.1175/BAMS-D-15-00184.1

    Article  Google Scholar 

  • Mueller WA, Baehr J, Haak H, Jungclaus JH, Kröger J, Matei D, Notz D, Pohlmann H, Storch JS, Marotzke J (2012) Forecast skill of multi-year seasonal means in the decadal prediction system of the Max Planck Institute for Meteorology. Geophys Res Lett. https://doi.org/10.1029/2012GL053326

    Article  Google Scholar 

  • Muir-Wood R (2011) The 1941 February 15th Windstorm in the Iberian Peninsula. Mapfre. http://www.mapfre.com/mapfrere/docs/html/revistas/trebol/n56/docs/Articulo1en.pdf

  • Nauels A, Meinshausen M, Mengel M, Lorbacher K, Wigley TML (2017) Synthesizing long-term sea level rise projections—the MAGICC sea level model v2.0. Geosci Model Dev 10:2495–2524. https://doi.org/10.5194/gmd-10-2495-2017

    Article  Google Scholar 

  • Perez J, Menendez M, Camus P, Mendez FJ, Losada IJ (2015) Statistical multi-model climate projections of surface ocean waves in Europe. Ocean Model 96:161–170. https://doi.org/10.1016/j.ocemod.2015.06.001

    Article  Google Scholar 

  • Pickering MD, Horsburgh KJ, Blundell JR, Hirschi JJ-M, Nicholls RJ, Verlaan M, Wells NC (2017) The impact of future sea-level rise on the global tides. Cont Shelf Res 142:50–68. https://doi.org/10.1016/j.csr.2017.02.004

    Article  Google Scholar 

  • Pohlmann H, Mueller WA, Kulkarni K, Kameswarrao M, Matei D, Vamborg FSE, Kadow C, Illing S, Marotzke J (2013) Improved forecast skill in the tropics in the new MiKlip decadal climate predictions. Geophys Res Lett 40(21):5798–5802. https://doi.org/10.1002/2013GL058051

    Article  Google Scholar 

  • Pugh D, Woodworth PL (2014) Sea-level science: understanding tides, surges, tsunamis and mean sea-level changes. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Rockel B, Will A, Hense A (2008) The regional climate model COSMO-CLM (CCLM). Meteorol Z 17(4):347–348. https://doi.org/10.1127/0941-2948/2008/0309

    Article  Google Scholar 

  • Santamaría-Gómez A, Gravelle M, Dangendorf S, Marcos M, Spada G, Wöppelmann G (2017) Uncertainty of the 20th century sea-level rise due to vertical land motion errors. Earth Planet Sci Lett 473:24–32. https://doi.org/10.1016/j.epsl.2017.05.038

    Article  Google Scholar 

  • Smith DM, Cusack S, Colman AW, Folland CK, Harris GR, Murphy JM (2007) Improved surface temperature prediction for the coming decade from a global climate model. Science 317(5839):796–799. https://doi.org/10.1126/science.1139540

    Article  Google Scholar 

  • Thompson PR, Mitchum GT, Vonesch C, Li J (2013) Variability of winter storminess in the eastern United States during the twentieth century from tide gauges. J Clim 26:9713–9726. https://doi.org/10.1175/JCLI-D-12-00561.1

    Article  Google Scholar 

  • Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Weather Rev 117:1779–1799. https://doi.org/10.1175/1520-0493

    Article  Google Scholar 

  • van den Brink HW, Konnen GP, Opsteegh JD, van Oldenborgh GJ, Burgers G (2005) Estimating return periods of extreme events from ECMWF seasonal forecast ensembles. Int J Climatol 25:1345–1354

    Article  Google Scholar 

  • Van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque J-F, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: an overview. Clim Change 109:5. https://doi.org/10.1007/s10584-011-0148-z

    Article  Google Scholar 

  • Vousdoukas MI, Voukouvalas E, Annunziato A, Giardino A, Feyen L (2016) Projections of extreme storm surge levels along Europe. Clim Dyn 47(9–10):3171–3190. https://doi.org/10.1007/s00382-016-3019-5

    Article  Google Scholar 

  • Vousdoukas MI, Mentaschi L, Voukouvalas E, Verlaan M, Feyen L (2017) Extreme sea levels on the rise along Europe’s coasts. Earths Future 5(3):304–323. https://doi.org/10.1002/2016EF000505

    Article  Google Scholar 

  • Wahl T, Haigh ID, Nicholls RJ, Arns A, Dangendorf S, Hinkel J, Slangen ABA (2017) Understanding extreme sea levels for broad-scale coastal impact and adaptation analysis. Nat Commun 8:16075. https://doi.org/10.1038/ncomms16075

    Article  Google Scholar 

  • Zhang Y, Baptista AM (2008) SELFE: a semi-implicit Eulerian-Lagrangian finite element model for cross-scale ocean circulation. Ocean Model 21(3–4):71–96. https://doi.org/10.1016/j.ocemod.2007.11.005

    Article  Google Scholar 

  • Zhang YJ, Ye F, Stanev EV, Grashorn S (2016) Seamless cross-scale modeling with SCHISM. Ocean Model 102:64–81. https://doi.org/10.1016/J.OCEMOD.2016.05.002

    Article  Google Scholar 

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Acknowledgements

This work was partially funded by the European Commission through the H2020 project BINGO (Grant Agreement Number 641739). MR was also partially funded by a postdoctoral grant from FCT—Fundação para a Ciência e a Tecnologia (SFRH/BPD/87512/2012). This work made use of results produced with the support of the Portuguese National Grid Initiative; more information in https://wiki.ncg.ingrid.pt. The climate simulations were performed at the German Climate Computing Centre (DKRZ). We thank Prof. Joseph Zhang for the model SCHISM, Dr. Alberto Azevedo for help in processing data, Drs. Elsa Alves and Manuel Oliveira for useful discussions and two anonymous reviewers for constructive criticism.

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Appendix

Appendix

The statistical analyses presented in the paper used several predictions for each year. The predictions vary in the way the atmospheric model was initialized (10 different ensemble members) and, in some cases, the lead year (i.e., the number of years since the beginning of the simulation). The question arises as to what extent different daily predictions for the same year exhibit any type of similarity or correlation. The answer to this question is important to establish whether or not the time series represent independent realizations of the atmospheric state.

It was hypothesized that the different predictions for the same year were uncorrelated, except for the seasonal signal. This hypothesis was verified for the mean sea level pressure at Cascais, using the predictions for 1980–2011. For each year, we used 40 predictions, corresponding to 10 ensemble members and 4 lead years (lead years 7 through 10).

The verification proceeded as follows. First, daily and monthly averages of mean sea level pressures at Cascais were computed for the 1280 time series (10 ensemble members, 32 years and 4 lead years). Then, the climatology (means of the monthly means) was determined and subtracted from the daily time series to obtain the daily residuals. Finally, the correlation for each pair of daily residuals series for the same year was determined. Results show that there is no correlation between predictions (Fig. 9), thereby validating their use as independent time series.

Fig. 9
figure 9

Correlation analysis between predictions for the same year. Each circle represents the correlation coefficient between two time series of predicted residuals for the same year at Cascais. The solid line shows their mean, and the shaded area shows the 95% confidence interval

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Fortunato, A.B., Meredith, E.P., Rodrigues, M. et al. Near-future changes in storm surges along the Atlantic Iberian coast. Nat Hazards 98, 1003–1020 (2019). https://doi.org/10.1007/s11069-018-3375-z

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