Articles | Volume 21, issue 2
https://doi.org/10.5194/os-21-767-2025
© Author(s) 2025. 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-21-767-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Ocean wave spectrum bias correction through energy conservation for climate change impacts
Andrea Lira Loarca
CORRESPONDING AUTHOR
Department of Civil, Chemical and Environmental Engineering, University of Genoa, Via Montallegro 1, 16145, Genoa, Italy
Giovanni Besio
Department of Civil, Chemical and Environmental Engineering, University of Genoa, Via Montallegro 1, 16145, Genoa, Italy
Related authors
Francesco Ferrari, Carmen Zarzuelo, Alejandro López-Ruiz, and Andrea Lira-Loarca
Earth Syst. Sci. Data, 17, 4881–4900, https://doi.org/10.5194/essd-17-4881-2025, https://doi.org/10.5194/essd-17-4881-2025, 2025
Short summary
Short summary
A high-resolution, freely available dataset is provided for Deception Island, Antarctica, covering the years 2005 to 2020. It is based on the Weather Research and Forecasting (WRF) atmospheric model and the Delft3D hydrodynamic model. The dataset includes detailed information on weather and ocean conditions, helping to improve understanding of Antarctic coastal changes and their links to climate change.
Eva Sebok, Hans Jørgen Henriksen, Ernesto Pastén-Zapata, Peter Berg, Guillaume Thirel, Anthony Lemoine, Andrea Lira-Loarca, Christiana Photiadou, Rafael Pimentel, Paul Royer-Gaspard, Erik Kjellström, Jens Hesselbjerg Christensen, Jean Philippe Vidal, Philippe Lucas-Picher, Markus G. Donat, Giovanni Besio, María José Polo, Simon Stisen, Yvan Caballero, Ilias G. Pechlivanidis, Lars Troldborg, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci., 26, 5605–5625, https://doi.org/10.5194/hess-26-5605-2022, https://doi.org/10.5194/hess-26-5605-2022, 2022
Short summary
Short summary
Hydrological models projecting the impact of changing climate carry a lot of uncertainty. Thus, these models usually have a multitude of simulations using different future climate data. This study used the subjective opinion of experts to assess which climate and hydrological models are the most likely to correctly predict climate impacts, thereby easing the computational burden. The experts could select more likely hydrological models, while the climate models were deemed equally probable.
Francesco Ferrari, Carmen Zarzuelo, Alejandro López-Ruiz, and Andrea Lira-Loarca
Earth Syst. Sci. Data, 17, 4881–4900, https://doi.org/10.5194/essd-17-4881-2025, https://doi.org/10.5194/essd-17-4881-2025, 2025
Short summary
Short summary
A high-resolution, freely available dataset is provided for Deception Island, Antarctica, covering the years 2005 to 2020. It is based on the Weather Research and Forecasting (WRF) atmospheric model and the Delft3D hydrodynamic model. The dataset includes detailed information on weather and ocean conditions, helping to improve understanding of Antarctic coastal changes and their links to climate change.
Eva Sebok, Hans Jørgen Henriksen, Ernesto Pastén-Zapata, Peter Berg, Guillaume Thirel, Anthony Lemoine, Andrea Lira-Loarca, Christiana Photiadou, Rafael Pimentel, Paul Royer-Gaspard, Erik Kjellström, Jens Hesselbjerg Christensen, Jean Philippe Vidal, Philippe Lucas-Picher, Markus G. Donat, Giovanni Besio, María José Polo, Simon Stisen, Yvan Caballero, Ilias G. Pechlivanidis, Lars Troldborg, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci., 26, 5605–5625, https://doi.org/10.5194/hess-26-5605-2022, https://doi.org/10.5194/hess-26-5605-2022, 2022
Short summary
Short summary
Hydrological models projecting the impact of changing climate carry a lot of uncertainty. Thus, these models usually have a multitude of simulations using different future climate data. This study used the subjective opinion of experts to assess which climate and hydrological models are the most likely to correctly predict climate impacts, thereby easing the computational burden. The experts could select more likely hydrological models, while the climate models were deemed equally probable.
Cited articles
Ardhuin, F., Rogers, E., Babanin, A. V., Filipot, J.-F., Magne, R., Roland, A., van der Westhuysen, A., Queffeulou, P., Lefevre, J.-M., Aouf, L., and Collard, F.: Semiempirical Dissipation Source Functions for Ocean Waves. Part I: Definition, Calibration, and Validation, J. Phys. Oceanogr., 40, 1917–1941, 2010. a
Barbariol, F., Davison, S., Falcieri, F. M., Ferretti, R., Ricchi, A., Sclavo, M., and Benetazzo, A.: Wind Waves in the Mediterranean Sea: An ERA5 Reanalysis Wind-Based Climatology, Frontiers in Marine Science, 8, 760614, https://doi.org/10.3389/fmars.2021.760614, 2021. a
Besio, G., Mentaschi, L., and Mazzino, A.: Wave energy resource assessment in the Mediterranean Sea on the basis of a 35-year hindcast, Energy, 94, 50–63, https://doi.org/10.1016/j.energy.2015.10.044, 2016. a, b
Christensen, J. H., Boberg, F., Christensen, O. B., and Lucas-Picher, P.: On the need for bias correction of regional climate change projections of temperature and precipitation, Geophys. Res. Lett., 35, L20709, https://doi.org/10.1029/2008GL035694, 2008. a
Christensen, O., Drews, M., Christensen, J., Dethloff, K., Hebestadt, I., Ketelsen, K., and Rinke, A.: The HIRHAM regional climate model version 5 (beta), DMI Technical Report 06-17, http://www.dmi.dk/dmi/tr06-17 (last access: 11 April 2025), 2007. a
Costoya, X., Rocha, A., and Carvalho, D.: Using bias-correction to improve future projections of offshore wind energy resource: A case study on the Iberian Peninsula, Appl. Energ., 262, 114562, https://doi.org/10.1016/j.apenergy.2020.114562, 2020. a
De Leo, F., Besio, G., and Mentaschi, L.: Trends and variability of ocean waves under RCP8.5 emission scenario in the Mediterranean Sea, Ocean Dynam., 71, 97–117, 2021. a
Di Biagio, V., Cossarini, G., Salon, S., and Solidoro, C.: Extreme event waves in marine ecosystems: an application to Mediterranean Sea surface chlorophyll, Biogeosciences, 17, 5967–5988, https://doi.org/10.5194/bg-17-5967-2020, 2020. a
Echevarria, E. R., Hemer, M. A., and Holbrook, N. J.: Seasonal Variability of the Global Spectral Wind Wave Climate, J. Geophys. Res.-Oceans, 124, 2924–2939, https://doi.org/10.1029/2018JC014620, 2019. a
Guedes, R., Durrant, T., Johnson, D., Perez, J., de Bruin, R., Harrington, J., Rapizo, H., and Bak, S.: Wavespectra: Python library for ocean wave spectra, Zenodo [code], https://doi.org/10.5281/zenodo.5171588, 2021. a, b
IPCC: Summary for Policymakers, chap. SPM, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.1017/9781009157964.001, 2019. a
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., 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., and Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, 2014. a
Lazzari, P., Solidoro, C., Ibello, V., Salon, S., Teruzzi, A., Béranger, K., Colella, S., and Crise, A.: Seasonal and inter-annual variability of plankton chlorophyll and primary production in the Mediterranean Sea: a modelling approach, Biogeosciences, 9, 217–233, https://doi.org/10.5194/bg-9-217-2012, 2012. a
Lemos, G., Menendez, M., Semedo, A., Camus, P., Hemer, M., Dobrynin, M., and Miranda, P. M.: On the need of bias correction methods for wave climate projections, Global Planet. Change, 186, 103109, https://doi.org/10.1016/j.gloplacha.2019.103109, 2020a. a, b
Lemos, G., Semedo, A., Dobrynin, M., Menendez, M., and Miranda, P. M. A.: Bias-Corrected CMIP5-Derived Single-Forcing Future Wind-Wave Climate Projections toward the End of the Twenty-First Century, J. Appl. Meteorol. Clim., 59, 1393–1414, https://doi.org/10.1175/JAMC-D-19-0297.1, 2020b. a, b, c, d, e
Leutwyler, D., Lüthi, D., Ban, N., Fuhrer, O., and Schär, C.: Evaluation of the convection-resolving climate modeling approach on continental scales, J. Geophys. Res.-Atmos., 122, 5237–5258, https://doi.org/10.1002/2016JD026013, 2017. a
Lira-Loarca, A., Cobos, M., Besio, G., and Baquerizo, A.: Projected wave climate temporal variability due to climate change, Stoch. Env. Res. Risk A., 35, 1741–1757, https://doi.org/10.1007/s00477-020-01946-2, 2021a. a, b
Lira-Loarca, A., Ferrari, F., Mazzino, A., and Besio, G.: Future wind and wave energy resources and exploitability in the Mediterranean Sea by 2100, Appl. Energ., 302, 117492, https://doi.org/10.1016/j.apenergy.2021.117492, 2021b. a, b
Lira-Loarca, A. and Besio, G.: MeteOcean 2D wave spectra statistics in the Mediterranean Sea: multi-model ensemble of GCM-RCMs projections by 2100, SEANOE [data set], https://doi.org/10.17882/96905, 2023. a
Lobeto, H., Menendez, M., and Losada, I. J.: Projections of Directional Spectra Help to Unravel the Future Behavior of Wind Waves, Frontiers in Marine Science, 8, 558, https://doi.org/10.3389/fmars.2021.655490, 2021a. a, b
Lobeto, H., Menendez, M., and Losada, I. J.: Future behavior of wind wave extremes due to climate change, Sci. Rep., 11, 1–12, 2021b. a
Logan, T., Bourgault, P., Smith, T. J., Huard, D., Biner, S., Labonté, M.-P., Rondeau-Genesse, G., Fyke, J., Aoun, A., Roy, P., Ehbrecht, C., Caron, D., Stephens, A., Whelan, C., and Low, J.-F.: Ouranosinc/xclim: v0.28.1, Zenodo [code], https://doi.org/10.5281/zenodo.5146351, 2021. a, b
Mentaschi, L., Besio, G., Cassola, F., and Mazzino, A.: Developing and validating a forecast/hindcast system for the Mediterranean Sea, J. Coastal Res., 65, 1551–1556, https://doi.org/10.2112/SI65-262.1, 2013a. a
Mentaschi, L., Besio, G., Cassola, F., and Mazzino, A.: Problems in RMSE-based wave model validations, Ocean Model., 72, 53–58, 2013b. a
Mentaschi, L., Besio, G., Cassola, F., and Mazzino, A.: Performance evaluation of Wavewatch III in the Mediterranean Sea, Ocean Model., 90, 82–94, https://doi.org/10.1016/j.ocemod.2015.04.003, 2015. a
Morim, J., Hemer, M., Cartwright, N., Strauss, D., and Andutta, F.: On the concordance of 21st century wind-wave climate projections, Global Planet. Change, 167, 160–171, https://doi.org/10.1016/j.gloplacha.2018.05.005, 2018. a
Mortlock, T. R. and Goodwin, I. D.: Directional wave climate and power variability along the Southeast Australian shelf, Cont. Shelf Res., 98, 36–53, https://doi.org/10.1016/j.csr.2015.02.007, 2015. a
Oppenheimer, M., Glavovic, B. C., Hinkel, J., van de Wal, R., Magnan, A. K., Abd-Elgawad, A., Cai, R., Cifuentes-Jara, M., Deconto, R. M., Ghosh, T., Hay, J., Isla, F., Marzeion, B., Meyssignac, B., and Sebesvari, Z.: Sea Level Rise and Implications for Low Lying Islands, Coasts and Communities, chap. 4, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.1017/9781009157964.006, 2019. a
Outten, S. and Sobolowski, S.: Extreme wind projections over Europe from the Euro-CORDEX regional climate models, Weather and Climate Extremes, 33, 100363, https://doi.org/10.1016/j.wace.2021.100363, 2021. a
Portilla-Yandún, J., Salazar, A., and Cavaleri, L.: Climate patterns derived from ocean wave spectra, Geophys. Res. Lett., 43, 11736–11743, https://doi.org/10.1002/2016GL071419, 2016. a
Rascle, N. and Ardhuin, F.: A global wave parameter database for geophysical applications. Part 2: Model validation with improved source term parameterization, Ocean Model., 70, 174–188, 2013. a
Shimura, T. and Mori, N.: High-resolution wave climate hindcast around Japan and its spectral representation, Coast. Eng., 151, 1–9, https://doi.org/10.1016/j.coastaleng.2019.04.013, 2019. a
Strandberg, G., Bärring, L., Hansson, U., Jansson, C., Jones, C., Kjellström, E., Kolax, M., Kupiainen, M., Nikulin, G., Samuelsson, P., Ullerstig, A., and Wang, S.: CORDEX scenarios for Europe from the Rossby Centre, regional climate model RCA4, Tech. Rep. 116, SMHI, https://urn.kb.se/resolve?urn=urn:nbn:se:smhi:diva-2839 (last access: 11 April 2025), 2014. a
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods, J. Hydrol., 456-457, 12–29, https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012. a, b
The WAVEWATCH III:® Development Group: User manual and documentation WAVEWATCH III® v6.07, Tech. rep., https://github.com/NOAA-EMC/WW3/wiki/Manual (last access: 11 April 2025), 2019. a
Villas Bôas, A. B., Gille, S. T., Mazloff, M. R., and Cornuelle, B. D.: Characterization of the Deep Water Surface Wave Variability in the California Current Region, J. Geophys. Res.-Oceans, 122, 8753–8769, https://doi.org/10.1002/2017JC013280, 2017. a
Will, A., Akhtar, N., Brauch, J., Breil, M., Davin, E., Ho-Hagemann, H. T. M., Maisonnave, E., Thürkow, M., and Weiher, S.: The COSMO-CLM 4.8 regional climate model coupled to regional ocean, land surface and global earth system models using OASIS3-MCT: description and performance, Geosci. Model Dev., 10, 1549–1586, https://doi.org/10.5194/gmd-10-1549-2017, 2017. a
Short summary
A new method improves the accuracy of climate models by adjusting wave spectrum simulations in the Mediterranean Sea. It corrects biases and accounts for changes in wave patterns due to climate change, such as shifts in direction and frequency. This technique was applied to multiple climate models, assessing future wave conditions for mid-century and end-of-century scenarios. The results underline the importance of precise corrections for better predicting how waves may evolve as the climate changes.
A new method improves the accuracy of climate models by adjusting wave spectrum simulations in...