Articles | Volume 21, issue 3
https://doi.org/10.5194/os-21-897-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-21-897-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 May 2025
Research article |
| 07 May 2025
| 07 May 2025
Marine heatwaves in the Mediterranean Sea: a convolutional neural network study for extreme event prediction
Antonios Parasyris
CORRESPONDING AUTHOR
Foundation for Research and Technology – Hellas, Institute of Applied and Computational Mathematics, 70013 Heraklion, Greece
Vassiliki Metheniti
Foundation for Research and Technology – Hellas, Institute of Applied and Computational Mathematics, 70013 Heraklion, Greece
Nikolaos Kampanis
Foundation for Research and Technology – Hellas, Institute of Applied and Computational Mathematics, 70013 Heraklion, Greece
Sofia Darmaraki
Foundation for Research and Technology – Hellas, Institute of Applied and Computational Mathematics, 70013 Heraklion, Greece
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This work provides a comprehensive overview of the current marine observation capabilities in the Mediterranean Sea, making this information accessible to scientists, stakeholders and environmental policymakers. It supports initiatives such as the Copernicus Marine Service, European Marine Observation and Data Network, the European Ocean Pact and relevant European Union directives. It’s intended to be regularly updated, helping to identify strengths and gaps in Mediterranean observation systems.
Manuel Vargas-Yáñez, Orens Pasqueron de Fommervault, Emanuela Clementi, Christian Ferrarin, Svitlana Liubartseva, Diego Álvarez-Berastegui, Baptiste Mourre, Matjaz Licer, George Alexandrakis, Salvatore Aronica, Rosa Balbín, Joaquín Ballabrera-Poy, Sana Ben Ismail, Nathaniel Bensoussan, Ismail Bessa, Alicia Blanco, Anthony Bosse, Evi Bourma, François Bourrin, Carlo Brandini, Fulvio Capodici, Clara Casals-Baixas, Branko Cermelj, Giuseppe Ciraolo, Pascal Conan, Laurent Coppola, Lorenzo Corgnati, Joaquín del Río, Emilie Diamond Riquier, Lara Díaz-Barroso, Bartolomeo Doronzo, Xavier Durrieu de Madron, Milad Fakhri, Constantin Frangoulis, Claudia Fratianni, Emilio García-Ladona, Jesús García-Lafuente, M. Carmen García-Martínez, Joaquín Garrabou, Josep Gasol, Adam Gauci, Frédéric Gazeau, Gerald Gregori, Abed El Rahman Hassoun, Jordi Isern-Fontanet, Dominique Lefevre, Nadia Lo Bue, Guiomar López, Pablo Lorente, Deny Malengros, Ramon Massana, Sinan Mavruk, Stefano Miserocchi, Behzad Mostajir, Laure Mousseau, Francina Moya, Antonio Novellino, Arianna Orasi, Emanuele Organelli, Vincent Ouisse, Tal Ozer, Antonios Parasyris, Begoña Pérez-Gómez, Susana Pérez-Rubio, Leonidas Perivoliotis, George Petihakis, Anne Petrenko, Sebastien Pinel, Xavier Pitarch, Angela Pomaro, Lucía Quirós-Collazos, Emma Reyes, Simone Sammartino, Anna Sánchez-Vidal, Lucía Sanz-Pinilla, Katrin Schroeder, Stefano Taddei, Elena Tel, Pierre Testor, Marco Torri, Daniela Turk, Laura Ursella, Dimitris Velaoras, Francesca Vidussi, Ivica Vilibić, Enrico Zambianchi, Nikolaos D. Zarokanellos, George Zodiatis, and Vanessa Cardin
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This work provides a comprehensive overview of the current marine observation capabilities in the Mediterranean Sea, making this information accessible to scientists, stakeholders and environmental policymakers. It supports initiatives such as the Copernicus Marine Service, European Marine Observation and Data Network, the European Ocean Pact and relevant European Union directives. It’s intended to be regularly updated, helping to identify strengths and gaps in Mediterranean observation systems.
Sofia Darmaraki, Marco Reale, Robin Waldman, and Vasiliki Metheniti
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Extreme cold temperature events in the ocean can strongly affect marine life, yet little is known about how they will change in the future. We studied past and future occurrence of these events in the Mediterranean Sea, using a regional, high resolution climate model.We find that they will become rarer, shorter,weaker and will shift northward, with higher global warming levels. These changes may affect sensitive ecosystems, biodiversity and fisheries and socioeconomic systems relaying on them.
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State Planet, 6-osr9, 10, https://doi.org/10.5194/sp-6-osr9-10-2025, https://doi.org/10.5194/sp-6-osr9-10-2025, 2025
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The Mediterranean Sea experiences a basin-wide increase in sea surface temperature (SST) and extreme SST occurrences. Stronger warming trends are found in the eastern basin, where a decrease in SST variability is also observed. Our findings on the origin of marine heatwave (MHW) trends in the basin suggest that the mean SST warming drives the long-term trends for most MHW properties across the basin, except for mean MHW intensity, where interannual variability emerges as the dominant driver.
Riccardo Martellucci, Francesco Tiralongo, Sofia F. Darmaraki, Michela D'Alessandro, Giorgio Mancinelli, Emanuele Mancini, Roberto Simonini, Milena Menna, Annunziata Pirro, Diego Borme, Rocco Auriemma, Marco Graziano, and Elena Mauri
State Planet, 6-osr9, 9, https://doi.org/10.5194/sp-6-osr9-9-2025, https://doi.org/10.5194/sp-6-osr9-9-2025, 2025
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In 2023, global mean air temperatures reached unprecedented highs and the Mediterranean was hit by the longest marine heatwave in four decades. These conditions favoured the spread of invasive species affecting fisheries in the central Mediterranean. This study provides new insights into the cascading impacts of climate-driven extreme events on marine ecosystems and fisheries and suggests actionable strategies for dealing with invasive species in a changing climate.
Cited articles
Abdelmajeed, A. Y. and Juszczak, R.: Challenges and Limitations of Remote Sensing Applications in Northern Peatlands: Present and Future Prospects, https://doi.org/10.3390/rs16030591, 2024.
Anding, D. and Kauth, R.: Estimation of sea surface temperature from space, Remote Sens. Environ., 1, 217–220, https://doi.org/10.1016/S0034-4257(70)80002-5, 1970.
Balaji, V.: Climbing down Charney's ladder: machine learning and the post-Dennard era of computational climate science, Philos. T. Roy. Soc. A, 379, 20200085, https://doi.org/10.1098/rsta.2020.0085, 2021.
Benincasa, R., Liguori, G., Pinardi, N., and von Storch, H.: Internal and forced ocean variability in the Mediterranean Sea, Ocean Sci., 20, 1003–1012, https://doi.org/10.5194/os-20-1003-2024, 2024.
Berrisford, P., Dee, D., Fielding, K., Fuentes, M., Kallberg, P., Kobayashi, S., and Uppala, S. S.: The ERA-interim archive, ERA Report Series, https://www.ecmwf.int/en/elibrary/73681-era-interim-archive (last access: May 2025), 2009.
Berthou, S., Renshaw, R., Smyth, T., Tinker, J., Grist, J. P., Wihsgott, J. U., Jones, S., Inall, M., Nolan, G., Berx, B., Arnold, A., Blunn, L. P., Castillo, J. M., Cotterill, D., Daly, E., Dow, G., Gómez, B., Fraser-Leonhardt, V., Hirschi, J. J. M., Lewis, H. W., Mahmood, S., and Worsfold, M.: Exceptional atmospheric conditions in June 2023 generated a northwest European marine heatwave which contributed to breaking land temperature records, Commun. Earth Environ., 5, 287, https://doi.org/10.1038/s43247-024-01413-8, 2024.
Bertsimas, D. and Boussioux, L.: Ensemble modeling for time series forecasting: an adaptive robust optimization approach, arXiv [preprint], https://doi.org/10.48550/arXiv.2304.04308, 2023.
Bethoux, J. P., Gentili, B., Morin, P., Nicolas, E., Pierre, C., and Ruiz-Pino, D.: The Mediterranean Sea: a miniature ocean for climatic and environmental studies and a key for the climatic functioning of the North Atlantic, Prog. Oceanogr., 44, 131–146, https://doi.org/10.1016/S0079-6611(99)00023-3, 1999.
Beuvier, J., Béranger, K., Lebeaupin Brossier, C., Somot, S., Sevault, F., Drillet, Y., Bourdallé-Badie, R., Ferry, N., and Lyard, F.: Spreading of the Western Mediterranean Deep Water after winter 2005: Time scales and deep cyclone transport, J. Geophys. Res.-Oceans, 117, C07022, https://doi.org/10.1029/2011JC007679, 2012.
Bonino, G., Masina, S., Galimberti, G., and Moretti, M.: Southern Europe and western Asian marine heatwaves (SEWA-MHWs): a dataset based on macroevents, Earth Syst. Sci. Data, 15, 1269–1285, https://doi.org/10.5194/essd-15-1269-2023, 2023.
Bonino, G., Galimberti, G., Masina, S., McAdam, R., and Clementi, E.: Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea, Ocean Sci., 20, 417–432, https://doi.org/10.5194/os-20-417-2024, 2024.
Chattopadhyay, A., Nabizadeh, E., and Hassanzadeh, P.: Analog Forecasting of Extreme-Causing Weather Patterns Using Deep Learning, J. Adv. Model Earth Sy., 12, e2019MS001958, https://doi.org/10.1029/2019MS001958, 2020.
Chen, L., Han, B., Wang, X., Zhao, J., Yang, W., and Yang, Z.: Machine Learning Methods in Weather and Climate Applications: A Survey, Appl. Sci. 13, 12019, https://doi.org/10.3390/app132112019, 2023.
Coppini, G., Clementi, E., Cossarini, G., Salon, S., Korres, G., Ravdas, M., Lecci, R., Pistoia, J., Goglio, A. C., Drudi, M., Grandi, A., Aydogdu, A., Escudier, R., Cipollone, A., Lyubartsev, V., Mariani, A., Cretì, S., Palermo, F., Scuro, M., Masina, S., Pinardi, N., Navarra, A., Delrosso, D., Teruzzi, A., Di Biagio, V., Bolzon, G., Feudale, L., Coidessa, G., Amadio, C., Brosich, A., Miró, A., Alvarez, E., Lazzari, P., Solidoro, C., Oikonomou, C., and Zacharioudaki, A.: The Mediterranean Forecasting System – Part 1: Evolution and performance, Ocean Sci., 19, 1483–1516, https://doi.org/10.5194/os-19-1483-2023, 2023.
Darmaraki, S., Somot, S., Sevault, F., and Nabat, P.: Past Variability of Mediterranean Sea Marine Heatwaves, Geophys. Res. Lett., 46, 9813–9823, https://doi.org/10.1029/2019GL082933, 2019a.
Darmaraki, S., Somot, S., Sevault, F., Nabat, P., Cabos Narvaez, W. D., Cavicchia, L., Djurdjevic, V., Li, L., Sannino, G., and Sein, D. V.: Future evolution of Marine Heatwaves in the Mediterranean Sea, Clim. Dynam., 53, 1371–1392, https://doi.org/10.1007/s00382-019-04661-z, 2019b.
Darmaraki, S., Denaxa, D., Theodorou, I., Livanou, E., Rigatou, D., Raitsos E, D., Stavrakidis-Zachou, O., Dimarchopoulou, D., Bonino, G., McAdam, R., Organelli, E., Pitsouni, A., and Parasyris, A.: Marine Heatwaves in the Mediterranean Sea: A Literature Review, Mediterr. Mar. Sci., 25, 586–620, https://doi.org/10.12681/mms.38392, 2024.
Desai, S. and Strachan, A.: Parsimonious neural networks learn interpretable physical laws, Sci. Rep., 11, 12761, https://doi.org/10.1038/s41598-021-92278-w, 2021.
Doury, A., Somot, S., Gadat, S., Ribes, A., and Corre, L.: Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach, Clim. Dynam., 60, 1751–1779, https://doi.org/10.1007/s00382-022-06343-9, 2023.
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006.
Fdez-Riverola, F., Corchado, J. M., and Torres, J. M.: An Automated Hybrid CBR System for Forecasting, Advances in Case-Based Reasoning, Berlin, Heidelberg, 519–533, https://doi.org/10.1007/3-540-46119-1_38, 2002.
Garcia-Gorriz, E. and Garcia-Sanchez, J.: Prediction of sea surface temperatures in the western Mediterranean Sea by neural networks using satellite observations, Geophys. Res. Lett., 34, L11603, https://doi.org/10.1029/2007GL029888, 2007.
Garrabou, J., Gómez-Gras, D., Medrano, A., Cerrano, C., Ponti, M., Schlegel, R., Bensoussan, N., Turicchia, E., Sini, M., Gerovasileiou, V., Teixido, N., Mirasole, A., Tamburello, L., Cebrian, E., Rilov, G., Ledoux, J. B., Souissi, J. B., Khamassi, F., Ghanem, R., Benabdi, M., Grimes, S., Ocaña, O., Bazairi, H., Hereu, B., Linares, C., Kersting, D. K., la Rovira, G., Ortega, J., Casals, D., Pagès-Escolà, M., Margarit, N., Capdevila, P., Verdura, J., Ramos, A., Izquierdo, A., Barbera, C., Rubio-Portillo, E., Anton, I., López-Sendino, P., Díaz, D., Vázquez-Luis, M., Duarte, C., Marbà, N., Aspillaga, E., Espinosa, F., Grech, D., Guala, I., Azzurro, E., Farina, S., Cristina Gambi, M., Chimienti, G., Montefalcone, M., Azzola, A., Mantas, T. P., Fraschetti, S., Ceccherelli, G., Kipson, S., Bakran-Petricioli, T., Petricioli, D., Jimenez, C., Katsanevakis, S., Kizilkaya, I. T., Kizilkaya, Z., Sartoretto, S., Elodie, R., Ruitton, S., Comeau, S., Gattuso, J. P., and Harmelin, J. G.: Marine heatwaves drive recurrent mass mortalities in the Mediterranean Sea, Glob. Change Biol., 28, 5708–5725, https://doi.org/10.1111/gcb.16301, 2022.
Giamalaki, K., Beaulieu, C., and Prochaska, J. X.: Assessing Predictability of Marine Heatwaves With Random Forests, Geophys. Res. Lett., 49, e2022GL099069, https://doi.org/10.1029/2022GL099069, 2022.
Han, M., Feng, Y., Zhao, X., Sun, C., Hong, F., and Liu, C.: A Convolutional Neural Network Using Surface Data to Predict Subsurface Temperatures in the Pacific Ocean, IEEE Access, 7, 172816–172829, https://doi.org/10.1109/ACCESS.2019.2955957, 2019.
Hobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C. J., Benthuysen, J. A., Burrows, M. T., Donat, M. G., Feng, M., Holbrook, N. J., Moore, P. J., Scannell, H. A., Sen Gupta, A., and Wernberg, T.: A hierarchical approach to defining marine heatwaves, Prog. Oceanogr., 141, 227–238, https://doi.org/10.1016/j.pocean.2015.12.014, 2016.
Hornik, K.: Approximation capabilities of multilayer feedforward networks, Neural Networks, 4, 251–257, https://doi.org/10.1016/0893-6080(91)90009-T, 1991.
Ibtehaz, N. and Rahman, M. S.: MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation, Neural Networks, 121, 74–87, https://doi.org/10.1016/j.neunet.2019.08.025, 2020.
Jacox, M. G., Alexander, M. A., Amaya, D., Becker, E., Bograd, S. J., Brodie, S., Hazen, E. L., Pozo Buil, M., and Tommasi, D.: Global seasonal forecasts of marine heatwaves, Nature, 604, 486–490, https://doi.org/10.1038/s41586-022-04573-9, 2022.
Jacques-Dumas, V., Ragone, F., Borgnat, P., Abry, P., and Bouchet, F.: Deep Learning-Based Extreme Heatwave Forecast, Front. Clim., 4, https://doi.org/10.3389/fclim.2022.789641, 2022.
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, arXiv [preprint], https://doi.org/10.48550/arXiv.1412.6980, 2014.
Konsta, K., Doxa, A., Katsanevakis, S., and Mazaris, A.: Projected intensification of subsurface marine heatwaves under climate change, Research Square [preprint], https://doi.org/10.21203/rs.3.rs-3091828/v1, 2023.
Lacoue-Labarthe, T., Nunes, P. A. L. D., Ziveri, P., Cinar, M., Gazeau, F., Hall-Spencer, J. M., Hilmi, N., Moschella, P., Safa, A., Sauzade, D., and Turley, C.: Impacts of ocean acidification in a warming Mediterranean Sea: An overview, Reg. Stud. Mar. Sci., 5, 1–11, https://doi.org/10.1016/j.rsma.2015.12.005, 2016.
Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., and Battaglia, P.: Learning skillful medium-range global weather forecasting, Science, 382, 1416–1421, https://doi.org/10.1126/science.adi2336, 2023.
Lin, T. Y., Goyal, P., Girshick, R., He, K., and Dollár, P.: Focal Loss for Dense Object Detection, IEEE T. Pattern Anal., 42, 318–327, https://doi.org/10.1109/TPAMI.2018.2858826, 2020.
Liu, J., Zhang, T., Han, G., and Gou, Y.: TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction, Sensors, 18, 3797, https://doi.org/10.3390/s18113797, 2018.
Liu, Y., Weisberg, R. H., Sorinas, L., Law, J. A., and Nickerson, A. K.: Rapid Intensification of Hurricane Ian in Relation to Anomalously Warm Subsurface Water on the Wide Continental Shelf, Geophys. Res. Lett., 52, e2024GL113192, https://doi.org/10.1029/2024GL113192, 2025.
Maas, A. L., Hannun, A. Y., and Ng, A. Y.: Rectifier Nonlinearities Improve Neural Network Acoustic Models, Proceedings of the 30th International Conference on Machine Learning, 28, 3, https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf (last access: May 2025), 2013.
Mavropoulou, A.-M., Mantziafou, A., Jarosz, E., and Sofianos, S.: The influence of Black Sea Water inflow and its synoptic time-scale variability in the North Aegean Sea hydrodynamics, Ocean Dynam., 66, 195–206, https://doi.org/10.1007/s10236-016-0923-5, 2016.
McAdam, R., Masina, S., and Gualdi, S.: Seasonal forecasting of subsurface marine heatwaves, Commun. Earth Environ., 4, 225, https://doi.org/10.1038/s43247-023-00892-5, 2023.
McMillin, L. M.: Estimation of sea surface temperatures from two infrared window measurements with different absorption, J. Geophys. Res., 80, 5113–5117, https://doi.org/10.1029/JC080i036p05113, 1975.
Menna, M., Gačić, M., Martellucci, R., Notarstefano, G., Fedele, G., Mauri, E., Gerin, R., and Poulain, P.-M.: Climatic, Decadal, and Interannual Variability in the Upper Layer of the Mediterranean Sea Using Remotely Sensed and In-Situ Data, Remote Sens., 14, 1322, https://doi.org/10.3390/rs14061322, 2022.
Nguyen, Q. D. and Thai, H.-T.: Crack segmentation of imbalanced data: The role of loss functions, Eng. Struct., 297, 116988, https://doi.org/10.1016/j.engstruct.2023.116988, 2023.
Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M. J., Heinrich, M. P., Misawa, K., Mori, K., McDonagh, S. G., Hammerla, N. Y., Kainz, B., Glocker, B., and Rueckert, D. J. A.: Attention U-Net: Learning Where to Look for the Pancreas, arXiv [preprint], https://doi.org/10.48550/arXiv.1804.03999, 2018.
Oliver, E. C. J., Donat, M. G., Burrows, M. T., Moore, P. J., Smale, D. A., Alexander, L. V., Benthuysen, J. A., Feng, M., Sen Gupta, A., Hobday, A. J., Holbrook, N. J., Perkins-Kirkpatrick, S. E., Scannell, H. A., Straub, S. C., and Wernberg, T.: Longer and more frequent marine heatwaves over the past century, Nat. Commun., 9, 1324, https://doi.org/10.1038/s41467-018-03732-9, 2018.
Parasyris, A., Alexandrakis, G., Kozyrakis, G. V., Spanoudaki, K., and Kampanis, N. A.: Predicting Meteorological Variables on Local Level with SARIMA, LSTM and Hybrid Techniques, Atmosphere, 13, 878, https://doi.org/10.3390/atmos13060878, 2022.
Petrelli, P.: XMHW: Xarray based code to identify Marine HeatWave events and their characteristics, Zenodo [code], https://doi.org/10.5281/zenodo.6270280, 2022.
Pisano, A., Ciani, D., Marullo, S., Santoleri, R., and Buongiorno Nardelli, B.: A new operational Mediterranean diurnal optimally interpolated sea surface temperature product within the Copernicus Marine Service, Earth Syst. Sci. Data, 14, 4111–4128, https://doi.org/10.5194/essd-14-4111-2022, 2022.
Ronneberger, O., Fischer, P., and Brox, T.: U-net: Convolutional networks for biomedical image segmentation, Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, 5–9 October 2015, Proceedings, Part III 18, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28, 2015.
Schultz, M. G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L. H., Mozaffari, A., and Stadtler, S.: Can deep learning beat numerical weather prediction?, Philos. T. Roy. Soc. A, 379, 20200097, https://doi.org/10.1098/rsta.2020.0097, 2021.
Sevault, F.: Atlas of the 1980–2018 ERA-interim simulation with the coupled regional climate system model CNRM-RCSM6 (version v2), Zenodo, https://doi.org/10.5281/zenodo.11066601, 2024.
Sharma, S., Sharma, S., and Athaiya, A.: Activation Functions in Neural Networks, Int. J. Eng. Appl. Sci. Tech., 4, 310–316, https://doi.org/10.33564/IJEAST.2020.v04i12.054, 2020.
Smith, K. E., Burrows, M. T., Hobday, A. J., Sen Gupta, A., Moore, P. J., Thomsen, M., Wernberg, T., and Smale, D. A.: Socioeconomic impacts of marine heatwaves: Global issues and opportunities, Science, 374, eabj3593, https://doi.org/10.1126/science.abj3593, 2021.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting, J. Mach. Learn. Res., 15, 1929–1958, 2014.
Sun, W., Zhou, S., Yang, J., Gao, X., Ji, J., and Dong, C.: Artificial Intelligence Forecasting of Marine Heatwaves in the South China Sea Using a Combined U-Net and ConvLSTM System, Remote Sens., 15, 4068, https://doi.org/10.3390/rs15164068, 2023.
Taylor, J. and Feng, M.: A deep learning model for forecasting global monthly mean sea surface temperature anomalies, Front. Clim., 4, https://doi.org/10.3389/fclim.2022.932932, 2022.
Velaoras, D., Zervakis, V., and Theocharis, A.: The Physical Characteristics and Dynamics of the Aegean Water Masses, in: The Aegean Sea Environment: The Geodiversity of the Natural System, edited by: Anagnostou, C. L., Kostianoy, A. G., Mariolakos, I. D., Panayotidis, P., Soilemezidou, M., and Tsaltas, G., Springer International Publishing, Cham, 231–259, https://doi.org/10.1007/698_2020_730, 2024.
Waldman, R., Somot, S., Herrmann, M., Bosse, A., Caniaux, G., Estournel, C., Houpert, L., Prieur, L., Sevault, F., and Testor, P.: Modeling the intense 2012–2013 dense water formation event in the northwestern Mediterranean Sea: Evaluation with an ensemble simulation approach, J. Geophys. Res.-Oceans, 122, 1297–1324, https://doi.org/10.1002/2016JC012437, 2017.
Xiao, C., Chen, N., Hu, C., Wang, K., Gong, J., and Chen, Z.: Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach, Remote Sens. Environ., 233, 111358, https://doi.org/10.1016/j.rse.2019.111358, 2019.
Xu, Z., Xiao, Z., Zhao, X., Ma, Z., Zhang, Q., Zeng, P., and Zhang, X.: Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China, Sustainability, 16, 4044, https://doi.org/10.3390/su16104044, 2024.
Zanetta, F., Nerini, D., Beucler, T., and Liniger, M. A.: Physics-Constrained Deep Learning Postprocessing of Temperature and Humidity, Artificial Intelligence for the Earth Systems, 2, e220089, https://doi.org/10.1175/AIES-D-22-0089.1, 2023.
Short summary
The Mediterranean faces more frequent and intense marine heat waves, harming ecosystems and fisheries. Using machine learning, we developed a model to forecast these events up to 7 d in the future, outperforming traditional methods. This approach enables faster, accurate forecasts, helping authorities mitigate impacts and protect marine resources.
The Mediterranean faces more frequent and intense marine heat waves, harming ecosystems and...
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