Articles | Volume 19, issue 6
https://doi.org/10.5194/os-19-1753-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-19-1753-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Dec 2023
Research article |
| 08 Dec 2023
| 08 Dec 2023
Uncertainties and discrepancies in the representation of recent storm surges in a non-tidal semi-enclosed basin: a hindcast ensemble for the Baltic Sea
Marvin Lorenz
CORRESPONDING AUTHOR
Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany
Ulf Gräwe
Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany
Related authors
Joshua Kiesel, Claudia Wolff, and Marvin Lorenz
Nat. Hazards Earth Syst. Sci., 24, 3841–3849, https://doi.org/10.5194/nhess-24-3841-2024, https://doi.org/10.5194/nhess-24-3841-2024, 2024
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In October 2023, one of the strongest storm surges on record hit the southwestern Baltic Sea coast, causing severe impacts in the German federal state of Schleswig-Holstein, including dike failures. Recent studies on coastal flooding from the same region align well with the October 2023 surge, with differences in peak water levels of less than 30 cm. This rare coincidence is used to assess current capabilities and limitations of coastal flood modelling and derive key areas for future research.
Marvin Lorenz, Katri Viigand, and Ulf Gräwe
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-198, https://doi.org/10.5194/nhess-2024-198, 2024
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This study divides the sea level components that contribute to extreme sea levels in the Baltic Sea into three parts: the filling state of the Baltic Sea, seiches and storm surges. In the western part of the Baltic Sea, storm surges are the main factor, while in the central and northern parts, the filling state plays a larger role. Using a numerical model, we show that wind and air pressure are the main drivers of these events, with Atlantic sea level also playing a small role.
Joshua Kiesel, Marvin Lorenz, Marcel König, Ulf Gräwe, and Athanasios T. Vafeidis
Nat. Hazards Earth Syst. Sci., 23, 2961–2985, https://doi.org/10.5194/nhess-23-2961-2023, https://doi.org/10.5194/nhess-23-2961-2023, 2023
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Among the Baltic Sea littoral states, Germany is anticipated to experience considerable damage as a result of increased coastal flooding due to sea-level rise (SLR). Here we apply a new modelling framework to simulate how flooding along the German Baltic Sea coast may change until 2100 if dikes are not upgraded. We find that the study region is highly exposed to flooding, and we emphasise the importance of current plans to update coastal protection in the future.
Marvin Lorenz, Knut Klingbeil, Parker MacCready, and Hans Burchard
Ocean Sci., 15, 601–614, https://doi.org/10.5194/os-15-601-2019, https://doi.org/10.5194/os-15-601-2019, 2019
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Estuaries are areas where riverine and oceanic waters meet and mix. The exchange flow of an estuary describes the water properties of the inflowing and outflowing water. These can be described by simple bulk values for volume fluxes and salinities. This work focuses on the numerics of one computational method for these values, the Total Exchange Flow. We show that only the so-called dividing salinity method is able to reliably calculate the correct values, even for complex situations.
Joshua Kiesel, Claudia Wolff, and Marvin Lorenz
Nat. Hazards Earth Syst. Sci., 24, 3841–3849, https://doi.org/10.5194/nhess-24-3841-2024, https://doi.org/10.5194/nhess-24-3841-2024, 2024
Short summary
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In October 2023, one of the strongest storm surges on record hit the southwestern Baltic Sea coast, causing severe impacts in the German federal state of Schleswig-Holstein, including dike failures. Recent studies on coastal flooding from the same region align well with the October 2023 surge, with differences in peak water levels of less than 30 cm. This rare coincidence is used to assess current capabilities and limitations of coastal flood modelling and derive key areas for future research.
Marvin Lorenz, Katri Viigand, and Ulf Gräwe
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-198, https://doi.org/10.5194/nhess-2024-198, 2024
Preprint under review for NHESS
Short summary
Short summary
This study divides the sea level components that contribute to extreme sea levels in the Baltic Sea into three parts: the filling state of the Baltic Sea, seiches and storm surges. In the western part of the Baltic Sea, storm surges are the main factor, while in the central and northern parts, the filling state plays a larger role. Using a numerical model, we show that wind and air pressure are the main drivers of these events, with Atlantic sea level also playing a small role.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2685, https://doi.org/10.5194/egusphere-2024-2685, 2024
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Forecasting river runoff, crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using Convolutional Long Short-Term Memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Joshua Kiesel, Marvin Lorenz, Marcel König, Ulf Gräwe, and Athanasios T. Vafeidis
Nat. Hazards Earth Syst. Sci., 23, 2961–2985, https://doi.org/10.5194/nhess-23-2961-2023, https://doi.org/10.5194/nhess-23-2961-2023, 2023
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Among the Baltic Sea littoral states, Germany is anticipated to experience considerable damage as a result of increased coastal flooding due to sea-level rise (SLR). Here we apply a new modelling framework to simulate how flooding along the German Baltic Sea coast may change until 2100 if dikes are not upgraded. We find that the study region is highly exposed to flooding, and we emphasise the importance of current plans to update coastal protection in the future.
Bronwyn E. Cahill, Piotr Kowalczuk, Lena Kritten, Ulf Gräwe, John Wilkin, and Jürgen Fischer
Biogeosciences, 20, 2743–2768, https://doi.org/10.5194/bg-20-2743-2023, https://doi.org/10.5194/bg-20-2743-2023, 2023
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We quantify the impact of optically significant water constituents on surface heating rates and thermal energy fluxes in the western Baltic Sea. During productive months in 2018 (April to September) we found that the combined effect of coloured
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Ocean Sci., 18, 1725–1739, https://doi.org/10.5194/os-18-1725-2022, https://doi.org/10.5194/os-18-1725-2022, 2022
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River engineering measures greatly changed tidal dynamics in the Weser estuary. We studied the effect on saltwater intrusion with numerical models. Our analysis shows that a deepening of the navigation channel causes saltwater to intrude further into the Weser estuary. This effect is mostly masked by the natural variability of river discharge. In our study, it proved essential to recalibrate individual hindcast models due to differences in sediments, bed forms, and underlying bathymetric data.
Matthias Gröger, Manja Placke, H. E. Markus Meier, Florian Börgel, Sandra-Esther Brunnabend, Cyril Dutheil, Ulf Gräwe, Magnus Hieronymus, Thomas Neumann, Hagen Radtke, Semjon Schimanke, Jian Su, and Germo Väli
Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, https://doi.org/10.5194/gmd-15-8613-2022, 2022
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Comparisons of oceanographic climate data from different models often suffer from different model setups, forcing fields, and output of variables. This paper provides a protocol to harmonize these elements to set up multidecadal simulations for the Baltic Sea, a marginal sea in Europe. First results are shown from six different model simulations from four different model platforms. Topical studies for upwelling, marine heat waves, and stratification are also assessed.
Jens Daniel Müller, Bernd Schneider, Ulf Gräwe, Peer Fietzek, Marcus Bo Wallin, Anna Rutgersson, Norbert Wasmund, Siegfried Krüger, and Gregor Rehder
Biogeosciences, 18, 4889–4917, https://doi.org/10.5194/bg-18-4889-2021, https://doi.org/10.5194/bg-18-4889-2021, 2021
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Based on profiling pCO2 measurements from a field campaign, we quantify the biomass production of a cyanobacteria bloom in the Baltic Sea, the export of which would foster deep water deoxygenation. We further demonstrate how this biomass production can be accurately reconstructed from long-term surface measurements made on cargo vessels in combination with modelled temperature profiles. This approach enables a better understanding of a severe concern for the Baltic’s good environmental status.
Erik Jacobs, Henry C. Bittig, Ulf Gräwe, Carolyn A. Graves, Michael Glockzin, Jens D. Müller, Bernd Schneider, and Gregor Rehder
Biogeosciences, 18, 2679–2709, https://doi.org/10.5194/bg-18-2679-2021, https://doi.org/10.5194/bg-18-2679-2021, 2021
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Ocean Sci., 16, 1491–1507, https://doi.org/10.5194/os-16-1491-2020, https://doi.org/10.5194/os-16-1491-2020, 2020
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This study investigates the impact of the uncertainty in atmospheric data of a storm event on the transport of microplastics and sediments. The model chain includes the WRF atmospheric model, the WAVEWATCH III® wave model, and the GETM regional ocean model as well as a sediment transport model based on the FABM framework. An ensemble approach based on stochastic perturbations of the WRF model is used. We found a strong impact of atmospheric uncertainty on the amount of transported material.
Hagen Radtke, Sandra-Esther Brunnabend, Ulf Gräwe, and H. E. Markus Meier
Clim. Past, 16, 1617–1642, https://doi.org/10.5194/cp-16-1617-2020, https://doi.org/10.5194/cp-16-1617-2020, 2020
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During the last century, salinity in the Baltic Sea showed a multidecadal oscillation with a period of 30 years. Using a numerical circulation model and wavelet coherence analysis, we demonstrate that this variation has at least two possible causes. One driver is river runoff which shows a 30-year variation. The second one is a variation in the frequency of strong inflows of saline water across Darss Sill which also contains a pronounced 30-year period.
Marvin Lorenz, Knut Klingbeil, Parker MacCready, and Hans Burchard
Ocean Sci., 15, 601–614, https://doi.org/10.5194/os-15-601-2019, https://doi.org/10.5194/os-15-601-2019, 2019
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Estuaries are areas where riverine and oceanic waters meet and mix. The exchange flow of an estuary describes the water properties of the inflowing and outflowing water. These can be described by simple bulk values for volume fluxes and salinities. This work focuses on the numerics of one computational method for these values, the Total Exchange Flow. We show that only the so-called dividing salinity method is able to reliably calculate the correct values, even for complex situations.
Beate Stawiarski, Stefan Otto, Volker Thiel, Ulf Gräwe, Natalie Loick-Wilde, Anna K. Wittenborn, Stefan Schloemer, Janine Wäge, Gregor Rehder, Matthias Labrenz, Norbert Wasmund, and Oliver Schmale
Biogeosciences, 16, 1–16, https://doi.org/10.5194/bg-16-1-2019, https://doi.org/10.5194/bg-16-1-2019, 2019
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Since eddies play a major role in the dynamics of oceanic flows, it is of great interest to gain information about their tracks, lifetimes and shapes. We develop an eddy tracking tool based on structures in the flow with collecting (attracting) or separating (repelling) properties. In test cases mimicking oceanic flows it yields eddy lifetimes close to the analytical ones. It even provides a detailed view of the dynamics that can be useful to gain more insight into eddy dynamics in oceanic flows.
M. Duran-Matute, T. Gerkema, G. J. de Boer, J. J. Nauw, and U. Gräwe
Ocean Sci., 10, 611–632, https://doi.org/10.5194/os-10-611-2014, https://doi.org/10.5194/os-10-611-2014, 2014
Related subject area
Approach: Numerical Models | Properties and processes: Sea level, tides, tsunamis and surges
The characteristics of tides and their effects on the general circulation of the Mediterranean Sea
Assessing the storm surge model performance: What error indicators can measure the skill?
Effects of sea level rise and tidal flat growth on tidal dynamics and geometry of the Elbe estuary
Technical note: Extending sea level time series for the analysis of extremes with statistical methods and neighbouring station data
Observations and modeling of tidally generated high-frequency velocity fluctuations downstream of a channel constriction
Bethany McDonagh, Emanuela Clementi, Anna Chiara Goglio, and Nadia Pinardi
Ocean Sci., 20, 1051–1066, https://doi.org/10.5194/os-20-1051-2024, https://doi.org/10.5194/os-20-1051-2024, 2024
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Tides in the Mediterranean Sea are typically of low amplitude, but twin experiments with and without tides demonstrate that tides affect the circulation directly at scales away from those of the tides. Analysis of the energy changes due to tides shows that they enhance existing oscillations, and internal tides interact with other internal waves. Tides also increase the mixed layer depth and enhance deep water formation in key regions. Internal tides are widespread in the Mediterranean Sea.
Rodrigo Campos-Caba, Lorenzo Mentaschi, Jacopo Alessandri, Paula Camus, Andrea Mazzino, Franceso Ferrari, Ivan Federico, Michalis Vousdoukas, and Massimo Tondello
EGUsphere, https://doi.org/10.5194/egusphere-2024-1415, https://doi.org/10.5194/egusphere-2024-1415, 2024
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Development of high-resolution simulations of storm surge in the Northern Adriatic Sea, employing different atmospheric forcing data and physical configurations. Traditional metrics like Pearson correlation and RMSE favor a simulation forced by a coarser database and employing a less sophisticated setup (2D, barotropic). Closer examination allows to identify a baroclinic (3D) model forced by a high-resolution dataset as better able to capture the variability and peak values of the storm surge.
Tara F. Mahavadi, Rita Seiffert, Jessica Kelln, and Peter Fröhle
Ocean Sci., 20, 369–388, https://doi.org/10.5194/os-20-369-2024, https://doi.org/10.5194/os-20-369-2024, 2024
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To analyse the influence of potential future mean sea level rise (SLR) and tidal flat elevation scenarios on the tidal dynamics in the Elbe estuary, we used a highly resolved hydrodynamic numerical model. The results show increasing tidal range in the Elbe estuary due to SLR alone. In combination with different tidal flat growth scenarios, they reveal strongly varying changes in tidal range. We discuss how changes in estuarine geometry can provide an explanation for the changes in tidal range.
Kévin Dubois, Morten Andreas Dahl Larsen, Martin Drews, Erik Nilsson, and Anna Rutgersson
Ocean Sci., 20, 21–30, https://doi.org/10.5194/os-20-21-2024, https://doi.org/10.5194/os-20-21-2024, 2024
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Coastal floods occur due to extreme sea levels (ESLs) which are difficult to predict because of their rarity. Long records of accurate sea levels at the local scale increase ESL predictability. Here, we apply a machine learning technique to extend sea level observation data in the past based on a neighbouring tide gauge. We compared the results with a linear model. We conclude that both models give reasonable results with a better accuracy towards the extremes for the machine learning model.
Håvard Espenes, Pål Erik Isachsen, and Ole Anders Nøst
Ocean Sci., 19, 1633–1648, https://doi.org/10.5194/os-19-1633-2023, https://doi.org/10.5194/os-19-1633-2023, 2023
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We show that tidally generated eddies generated near the constriction of a channel can drive a strong and fluctuating flow field far downstream of the channel constriction itself. The velocity signal has been observed in other studies, but this is the first study linking it to a physical process. Eddies such as those we found are generated because of complex coastal geometry, suggesting that, for example, land-reclamation projects in channels may enhance current shear over a large area.
Cited articles
Andersson, H. C.: Influence of long-term regional and large-scale atmospheric circulation on the Baltic Sea level, Tellus A, 54, 76–88, https://doi.org/10.3402/tellusa.v54i1.12125, 2002. a
Andrée, E., Su, J., Dahl Larsen, M. A., Drews, M., Stendel, M., and Skovgaard Madsen, K.: The role of preconditioning for extreme storm surges in the western Baltic Sea, Nat. Hazards Earth Syst. Sci., 23, 1817–1834, https://doi.org/10.5194/nhess-23-1817-2023, 2023. a
Arns, A., Wahl, T., Haigh, I., Jensen, J., and Pattiaratchi, C.: Estimating extreme water level probabilities: A comparison of the direct methods and recommendations for best practise, Coast. Eng., 81, 51–66, https://doi.org/10.1016/j.coastaleng.2013.07.003, 2013. a, b
Arns, A., Wahl, T., Wolff, C., Vafeidis, A. T., Haigh, I. D., Woodworth, P., Niehüser, S., and Jensen, J.: Non-linear interaction modulates global extreme sea levels, coastal flood exposure, and impacts, Nat. Commun., 11, 1–9, https://doi.org/10.1038/s41467-020-15752-5, 2020. a, b, c
Bamber, J. L., Oppenheimer, M., Kopp, R. E., Aspinall, W. P., and Cooke, R. M.: Ice sheet contributions to future sea-level rise from structured expert judgment, P. Natl. Acad. Sci. USA, 116, 11195–11200, https://doi.org/10.1073/pnas.1817205116, 2019. a
Barbosa, S. M.: Quantile trends in Baltic sea level, Geophys. Res. Lett., 35, https://doi.org/10.1029/2008gl035182, 2008. a
Bierstedt, S. E., Hünicke, B., and Zorita, E.: Variability of wind direction statistics of mean and extreme wind events over the Baltic Sea region, Tellus A, 67, 29073, https://doi.org/10.3402/tellusa.v67.29073, 2015. a
Bork, I. and Müller-Navarra, S. H.: Modellierung von extremen Sturmhochwassern an der deutschen Ostseeküste, Die Küste, 75 MUSTOK, 71–130, 2009. a
Burchard, H. and Bolding, K.: GETM, A General Estuarine Transport Model: Scientific Documentation, Tech. Rep. EUR 20253, EN, Eur. Comm., 2002. a
Christensen, O. B., Kjellström, E., Dieterich, C., Gröger, M., and Meier, H. E. M.: Atmospheric regional climate projections for the Baltic Sea region until 2100, Earth Syst. Dynam., 13, 133–157, https://doi.org/10.5194/esd-13-133-2022, 2022. a
Coles, S., Bawa, J., Trenner, L., and Dorazio, P.: An introduction to statistical modeling of extreme values, Springer, ISBN 1-85233-459-2, https://doi.org/10.1007/978-1-4471-3675-0, 2001. a, b, c, d
Copernicus Climate Change Service: Climate Data Store: UERRA regional reanalysis for Europe on height levels from 1961 to 2019, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.44ec8078, 2019. a
Dieterich, C., Gröger, M., Arneborg, L., and Andersson, H. C.: Extreme sea levels in the Baltic Sea under climate change scenarios – Part 1: Model validation and sensitivity, Ocean Sci., 15, 1399–1418, https://doi.org/10.5194/os-15-1399-2019, 2019. a
Eelsalu, M., Soomere, T., Pindsoo, K., and Lagemaa, P.: Ensemble approach for projections of return periods of extreme water levels in Estonian waters, Cont. Shelf Res., 91, 201–210, https://doi.org/10.1016/j.csr.2014.09.012, 2014. a
Errico, R. M.: What is an adjoint model?, B. Am. Meteorol. Soc., 78, 2577–2592, https://doi.org/10.1175/1520-0477(1997)078<2577:wiaam>2.0.co;2, 1997. a
Feuchter, D., Jörg, C., Rosenhagen, G., Auchmann, R., Romppainen-Martius, O., and Brönnimann, S.: The 1872 Baltic Sea storm surge, Geograph. Bern., 89, 91–98, https://doi.org/10.4480/GB2013.G89.10, 2013. a
Fox-Kemper, B., Hewitt, H., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S., Edwards, T., Golledge, N., Hemer, M., Kopp, R., Krinner, G., Mix, A., Notz, D., Nowicki, S., Nurhati, I., Ruiz, L., Sallée, J.-B., Slangen, A., and Yu, Y.: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, https://doi.org/10.1017/9781009157896.011, 2021. a
Geyer, B.: High-resolution atmospheric reconstruction for Europe 1948–2012: coastDat2, Earth Syst. Sci. Data, 6, 147–164, https://doi.org/10.5194/essd-6-147-2014, 2014. a, b
Geyer, B., Ludwig, T., and von Storch, H.: Limits of reproducibility and hydrodynamic noise in atmospheric regional modelling, Commun. Earth Environ., 2, 17, https://doi.org/10.1038/s43247-020-00085-4, 2021. a
Gräwe, U., Naumann, M., Mohrholz, V., and Burchard, H.: Anatomizing one of the largest saltwater inflows into the Baltic Sea in December 2014, J. Geophys. Res.-Oceans, 120, 7676–7697, https://doi.org/10.1002/2015JC011269, 2015. a
Gräwe, U., Klingbeil, K., Kelln, J., and Dangendorf, S.: Decomposing mean sea level rise in a semi-enclosed basin, the Baltic Sea, J. Climate, 32, 3089–3108, https://doi.org/10.1175/jcli-d-18-0174.1, 2019. a, b, c
Grinsted, A.: Projected Change – Sea Level, 253–263, Springer International Publishing, Cham, ISBN 978-3-319-16006-1, https://doi.org/10.1007/978-3-319-16006-1_14, 2015. a
Gröger, M., Placke, M., Meier, H. E. M., Börgel, F., Brunnabend, S.-E., Dutheil, C., Gräwe, U., Hieronymus, M., Neumann, T., Radtke, H., Schimanke, S., Su, J., and Väli, G.: The Baltic Sea Model Intercomparison Project (BMIP) – a platform for model development, evaluation, and uncertainty assessment, Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, 2022. a
Haigh, I. D., Marcos, M., Talke, S. A., Woodworth, P. L., Hunter, J. R., Hague, B. S., Arns, A., Bradshaw, E., and Thompson, P.: GESLA Version 3: A major update to the global higher-frequency sea-level dataset, Geosci. Data J., https://doi.org/10.31223/x5mp65, 2022. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., et al.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a
Hieronymus, M., Dieterich, C., Andersson, H., and Hordoir, R.: The effects of mean sea level rise and strengthened winds on extreme sea levels in the Baltic Sea, Theor. Appl., 8, 366–371, https://doi.org/10.1016/j.taml.2018.06.008, 2018. a
Hofstede, J. and Hamann, M.: The 1872 catastrophic storm surge at the Baltic Sea coast of Schleswig-Holstein; lessons learned?, Die Küste, https://doi.org/10.18171/1.092101, 2022. a
Hünicke, B. and Zorita, E.: Influence of temperature and precipitation on decadal Baltic Sea level variations in the 20th century, Tellus A, 58, 141–153, https://doi.org/10.1111/j.1600-0870.2006.00157.x, 2006. a
Kara, A. B., Rochford, P. A., and Hurlburt, H. E.: Efficient and accurate bulk parameterizations of air–sea fluxes for use in general circulation models, J. Atmos. Ocean. Tech., 17, 1421–1438, https://doi.org/10.1175/1520-0426(2000)017<1421:eaabpo>2.0.co;2, 2000. a, b, c, d
Karabil, S., Zorita, E., and Hünicke, B.: Contribution of atmospheric circulation to recent off-shore sea-level variations in the Baltic Sea and the North Sea, Earth Syst. Dynam., 9, 69–90, https://doi.org/10.5194/esd-9-69-2018, 2018. a
Kärnä, T., Ljungemyr, P., Falahat, S., Ringgaard, I., Axell, L., Korabel, V., Murawski, J., Maljutenko, I., Lindenthal, A., Jandt-Scheelke, S., Verjovkina, S., Lorkowski, I., Lagemaa, P., She, J., Tuomi, L., Nord, A., and Huess, V.: Nemo-Nordic 2.0: operational marine forecast model for the Baltic Sea, Geosci. Model Dev., 14, 5731–5749, https://doi.org/10.5194/gmd-14-5731-2021, 2021. a
Kärnä, T., Wallwork, J. G., and Kramer, S. C.: Efficient optimization of a regional water elevation model with an automatically generated adjoint, J. Adv. Model. Earth Syst., 15, e2022MS003169. https://doi.org/10.1029/2022MS003169, 2023. a
Kiesel, J., Lorenz, M., König, M., Gräwe, U., and Vafeidis, A. T.: Regional assessment of extreme sea levels and associated coastal flooding along the German Baltic Sea coast, Nat. Hazards Earth Syst. Sci., 23, 2961–2985, https://doi.org/10.5194/nhess-23-2961-2023, 2023. a, b
Klingbeil, K., Lemarié, F., Debreu, L., and Burchard, H.: The numerics of hydrostatic structured-grid coastal ocean models: state of the art and future perspectives, Ocean Modell., 125, 80–105, https://doi.org/10.1016/j.ocemod.2018.01.007, 2018. a
Kudryavtseva, N., Pindsoo, K., and Soomere, T.: Non-stationary Modeling of Trends in Extreme Water Level Changes Along the Baltic Sea Coast, J. Coast. Res., 85, 586–590, https://doi.org/10.2112/SI85-118.1, 2018. a
Kudryavtseva, N., Soomere, T., and Männikus, R.: Non-stationary analysis of water level extremes in Latvian waters, Baltic Sea, during 1961–2018, Nat. Hazards Earth Syst. Sci., 21, 1279–1296, https://doi.org/10.5194/nhess-21-1279-2021, 2021. a, b, c
Large, W. and Yeager, S.: The global climatology of an interannually varying air–sea flux data set, Clim. Dynam., 33, 341–364, https://doi.org/10.1007/s00382-008-0441-3, 2009. a
Lehmann, A., Getzlaff, K., and Harlaß, J.: Detailed assessment of climate variability in the Baltic Sea area for the period 1958 to 2009, Clim. Res., 46, 185–196, https://doi.org/10.3354/cr00876, 2011. a
Lorenz, M. and Gräwe, U.: Results of the study “Uncertainties and discrepancies in the representation of recent storm surges in a non-tidal semi-enclosed basin: a hind-cast ensemble for the Baltic Sea” in Ocean Science (1.0) Zenodo [code, data set], https://doi.org/10.5281/zenodo.8340649, 2023a. a
Lorenz, M. and Gräwe, U.: Numerical hindcast ensemble for extreme sea levels in the Baltic Sea for 1979–2018, Leibniz Institute for Baltic Sea Research Warnemünde [data set], https://iowmeta.io-warnemuende.de/geonetwork/srv/eng/catalog.search#/metadata/IO W-IOWMETA-BalticSeaExtremeSeaLevelHindcastEnsemble-197 9-2018 (last access: 13 September 2023), 2023b. a
Madsen, K. S., Høyer, J. L., Fu, W., and Donlon, C.: Blending of satellite and tide gauge sea level observations and its assimilation in a storm surge model of the North Sea and Baltic Sea, J. Geophys. Res.-Oceans, 120, 6405–6418, https://doi.org/10.1002/2015JC011070, 2015. a
Männikus, R., Soomere, T., and Kudryavtseva, N.: Identification of mechanisms that drive water level extremes from in situ measurements in the Gulf of Riga during 1961-2017, Cont. Shelf Res., 182, 22–36, https://doi.org/10.1016/j.csr.2019.05.014, 2019. a
Männikus, R., Soomere, T., and Viška, M.: Variations in the mean, seasonal and extreme water level on the Latvian coast, the eastern Baltic Sea, during 1961–2018, Estuar. Coast. Shelf Sci., 245, 106827, https://doi.org/10.1016/j.ecss.2020.106827, 2020. a
Meier, H. E. M., Dieterich, C., Gröger, M., Dutheil, C., Börgel, F., Safonova, K., Christensen, O. B., and Kjellström, E.: Oceanographic regional climate projections for the Baltic Sea until 2100, Earth Syst. Dynam., 13, 159–199, https://doi.org/10.5194/esd-13-159-2022, 2022a. a, b
Meier, H. E. M., Kniebusch, M., Dieterich, C., Gröger, M., Zorita, E., Elmgren, R., Myrberg, K., Ahola, M. P., Bartosova, A., Bonsdorff, E., Börgel, F., Capell, R., Carlén, I., Carlund, T., Carstensen, J., Christensen, O. B., Dierschke, V., Frauen, C., Frederiksen, M., Gaget, E., Galatius, A., Haapala, J. J., Halkka, A., Hugelius, G., Hünicke, B., Jaagus, J., Jüssi, M., Käyhkö, J., Kirchner, N., Kjellström, E., Kulinski, K., Lehmann, A., Lindström, G., May, W., Miller, P. A., Mohrholz, V., Müller-Karulis, B., Pavón-Jordán, D., Quante, M., Reckermann, M., Rutgersson, A., Savchuk, O. P., Stendel, M., Tuomi, L., Viitasalo, M., Weisse, R., and Zhang, W.: Climate change in the Baltic Sea region: a summary, Earth Syst. Dynam., 13, 457–593, https://doi.org/10.5194/esd-13-457-2022, 2022b. a, b
Meier, H. M., Broman, B., and Kjellström, E.: Simulated sea level in past and future climates of the Baltic Sea, Clim. Res., 27, 59–75, https://doi.org/10.3354/cr027059, 2004. a
Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C., and Ward, P. J.: A global reanalysis of storm surges and extreme sea levels, Nat. Commun., 7, 1–12, https://doi.org/10.1038/ncomms11969, 2016. a
Muis, S., Aerts, J., Antolínez, J. A. Á., Dullaart, J., Duong, T. M., Erikson, L., Haarmsa, R., Apecechea, M. I., Mengel, M., Le Bars, D., et al.: Global projections of storm surges using high-resolution CMIP6 climate models: validation, projected changes, and methodological challenges, Authorea Preprints, https://doi.org/10.1002/essoar.10511919.1, 2022. a
Peltier, W. R.: Global glacial isostasy and the surface of the ice-age Earth: the ICE-5G (VM2) model and GRACE, Annu. Rev. Earth Pl. Sc., 32, 111–149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004. a
Peng, S., Li, Y., and Xie, L.: Adjusting the wind stress drag coefficient in storm surge forecasting using an adjoint technique, J. Atmos. Ocean. Technol., 30, 590–608, https://doi.org/10.1175/jtech-d-12-00034.1, 2013. a
Pietrzak, J.: The use of TVD limiters for forward-in-time upstream-biased advection schemes in ocean modeling, Mon. Weather Rev., 126, 812–830, https://doi.org/10.1175/1520-0493(1998)126<0812:tuotlf>2.0.co;2, 1998. a
Reinert, M., Pineau-Guillou, L., Raillard, N., and Chapron, B.: Seasonal shift in storm surges at Brest revealed by extreme value analysis, J. Geophys. Res.-Oceans, 126, e2021JC017794, https://doi.org/10.1029/2021jc017794, 2021. a
Rosenhagen, G. and Bork, I.: Rekonstruktion der Sturmwetterlage vom 13. November 1872, Die Küste, 75 MUSTOK, 51–70, 2009. a
Rutgersson, A., Kjellström, E., Haapala, J., Stendel, M., Danilovich, I., Drews, M., Jylhä, K., Kujala, P., Larsén, X. G., Halsnæs, K., Lehtonen, I., Luomaranta, A., Nilsson, E., Olsson, T., Särkkä, J., Tuomi, L., and Wasmund, N.: Natural hazards and extreme events in the Baltic Sea region, Earth Syst. Dynam., 13, 251–301, https://doi.org/10.5194/esd-13-251-2022, 2022. a
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., et al.: The NCEP climate forecast system reanalysis, B. Am. Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010bams3001.1, 2010. a
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H.-y., Iredell, M., et al.: The NCEP climate forecast system version 2, J. Climate, 27, 2185–2208, https://doi.org/10.1175/jcli-d-12-00823.1, 2014. a
Smith, S. D., Anderson, R. J., Oost, W. A., Kraan, C., Maat, N., De Cosmo, J., Katsaros, K. B., Davidson, K. L., Bumke, K., Hasse, L., et al.: Sea surface wind stress and drag coefficients: The HEXOS results, Bound.-Lay. Meteorol., 60, 109–142, https://doi.org/10.1007/bf00122064, 1992. a
Soomere, T., Eelsalu, M., Kurkin, A., and Rybin, A.: Separation of the Baltic Sea water level into daily and multi-weekly components, Cont. Shelf Res., 103, 23–32, https://doi.org/10.1016/j.csr.2015.04.018, 2015. a
Suursaar, Ü. and Sooäär, J.: Decadal variations in mean and extreme sea level values along the Estonian coast of the Baltic Sea, Tellus A, 59, 249–260, https://doi.org/10.1111/j.1600-0870.2006.00220.x, 2007. a, b, c
Tadesse, M. G. and Wahl, T.: A database of global storm surge reconstructions, Sci. Data, 8, 1–10, https://doi.org/10.1038/s41597-021-00906-x, 2021. a
Tadesse, M. G., Wahl, T., Rashid, M. M., Dangendorf, S., Rodríguez-Enríquez, A., and Talke, S. A.: Long-term trends in storm surge climate derived from an ensemble of global surge reconstructions, Sci. Rep., 12, 1–18, https://doi.org/10.1002/essoar.10511038.1, 2022. a
Thyng, K. M., Greene, C. A., Hetland, R. D., Zimmerle, H. M., and DiMarco, S. F.: True colors of oceanography: Guidelines for effective and accurate colormap selection, Oceanography, 29, 9–13, https://doi.org/10.5670/oceanog.2016.66, 2016. a
Vousdoukas, M. I., Mentaschi, L., Voukouvalas, E., Verlaan, M., and Feyen, L.: Extreme sea levels on the rise along Europe's coasts, Earth's Future, 5, 304–323, https://doi.org/10.1002/2016ef000505, 2017. a
Wahl, T., Haigh, I. D., Nicholls, R. J., Arns, A., Dangendorf, S., Hinkel, J., and Slangen, A. B.: Understanding extreme sea levels for broad-scale coastal impact and adaptation analysis, Nat. Commun., 8, 1–12, https://doi.org/10.1038/ncomms16075, 2017. a
Weisse, R. and Weidemann, H.: Baltic Sea extreme sea levels 1948-2011: Contributions from atmospheric forcing, Procedia IUTAM, 25, 65–69, https://doi.org/10.1016/j.piutam.2017.09.010, 2017. a
Weisse, R., Dailidienė, I., Hünicke, B., Kahma, K., Madsen, K., Omstedt, A., Parnell, K., Schöne, T., Soomere, T., Zhang, W., and Zorita, E.: Sea level dynamics and coastal erosion in the Baltic Sea region, Earth Syst. Dynam., 12, 871–898, https://doi.org/10.5194/esd-12-871-2021, 2021. a, b, c
Wolski, T. and Wiśniewski, B.: Characteristics and Long-Term Variability of Occurrences of Storm Surges in the Baltic Sea, Atmosphere, 12, 1679, https://doi.org/10.3390/atmos12121679, 2021. a
Wolski, T. and Wiśniewski, B.: Characteristics of seasonal changes of the Baltic Sea extreme sea levels, Oceanologia, 65, 151–170, https://doi.org/10.1016/j.oceano.2022.02.006, 2023. a
Wolski, T., Wiśniewski, B., Giza, A., Kowalewska-Kalkowska, H., Boman, H., Grabbi-Kaiv, S., Hammarklint, T., Holfort, J., and Lydeikaitė, Ž.: Extreme sea levels at selected stations on the Baltic Sea coast, Oceanologia, 56, 259–290, https://doi.org/10.5697/oc.56-2.259, 2014. a
Woodworth, P. L., Hunter, J. R., Marcos, M., Caldwell, P., Menéndez, M., and Haigh, I.: Towards a global higher-frequency sea level dataset, Geosci. Data J., 3, 50–59, https://doi.org/10.1002/gdj3.42, 2016. a
Short summary
We study the variability of extreme sea levels in a 13 member hindcast ensemble for the Baltic Sea. The ensemble mean shows good agreement with observations regarding return levels and trends. However, we find great variability and uncertainty within the ensemble. We argue that the variability of storms in the atmospheric data directly translates into the variability of the return levels. These results highlight the need for large regional ensembles to minimise uncertainties.
We study the variability of extreme sea levels in a 13 member hindcast ensemble for the Baltic...
