Articles | Volume 18, issue 15
https://doi.org/10.5194/gmd-18-4789-2025
© 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/gmd-18-4789-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
04 Aug 2025
Development and technical paper |
| 04 Aug 2025
| 04 Aug 2025
Correction of sea surface biases in the NEMO ocean general circulation model using neural networks
National Research Council of Italy (CNR), Institute of Marine Sciences (ISMAR), Rome, Italy
National Research Center for High Performance Computing, Big Data and Quantum Computing (ICSC), Italy
Sergey Frolov
National Oceanic and Atmospheric Administration (NOAA), Physical Sciences Laboratory (PSL), Boulder, CO, USA
Laura Slivinski
National Oceanic and Atmospheric Administration (NOAA), Physical Sciences Laboratory (PSL), Boulder, CO, USA
Chunxue Yang
National Research Council of Italy (CNR), Institute of Marine Sciences (ISMAR), Rome, Italy
National Research Center for High Performance Computing, Big Data and Quantum Computing (ICSC), Italy
Related authors
Haohao Zhang, Andrea Storto, Xuezhi Bai, and Chunxue Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-3030, https://doi.org/10.5194/egusphere-2025-3030, 2025
Short summary
Short summary
Using a 1D coupled ice-ocean model, we quantified the effects of meltwater and ice-albedo feedback independently. The meltwater reduces melting by 19 % through thermal isolation, while ice-albedo feedback increases melting by 41 %, with nonlinear coupling between them. In winter, meltwater protects ice in weakly stratified areas by blocking Atlantic heat. Our study provides new insights into the relative importance of different components in the Arctic ice-ocean system.
Marie Bouih, Anne Barnoud, Chunxue Yang, Andrea Storto, Alejandro Blazquez, William Llovel, Robin Fraudeau, and Anny Cazenave
Ocean Sci., 21, 1425–1440, https://doi.org/10.5194/os-21-1425-2025, https://doi.org/10.5194/os-21-1425-2025, 2025
Short summary
Short summary
Present-day sea level rise is not uniform regionally. For better understanding of regional sea level variations, a classical approach is to compare the observed sea level trend patterns with those of the sum of the contributions. If the regional sea level trend budget is not closed, this allows the detection of errors in the observing systems. Our study shows that the trend budget is not closed in the North Atlantic Ocean and identifies errors in Argo-based salinity data as the main suspect.
Andrea Storto, Giulia Chierici, Julia Pfeffer, Anne Barnoud, Romain Bourdalle-Badie, Alejandro Blazquez, Davide Cavaliere, Noémie Lalau, Benjamin Coupry, Marie Drevillon, Sebastien Fourest, Gilles Larnicol, and Chunxue Yang
State Planet, 4-osr8, 12, https://doi.org/10.5194/sp-4-osr8-12-2024, https://doi.org/10.5194/sp-4-osr8-12-2024, 2024
Short summary
Short summary
The variability in the manometric sea level (i.e. the sea level mass component) in three ocean basins is investigated in this study using three different methods (reanalyses, gravimetry, and altimetry in combination with in situ observations). We identify the emerging long-term signals, the consistency of the datasets, and the influence of large-scale climate modes on the regional manometric sea level variations at both seasonal and interannual timescales.
Vincenzo de Toma, Daniele Ciani, Yassmin Hesham Essa, Chunxue Yang, Vincenzo Artale, Andrea Pisano, Davide Cavaliere, Rosalia Santoleri, and Andrea Storto
Geosci. Model Dev., 17, 5145–5165, https://doi.org/10.5194/gmd-17-5145-2024, https://doi.org/10.5194/gmd-17-5145-2024, 2024
Short summary
Short summary
This study explores methods to reconstruct diurnal variations in skin sea surface temperature in a model of the Mediterranean Sea. Our new approach, considering chlorophyll concentration, enhances spatial and temporal variations in the warm layer. Comparative analysis shows context-dependent improvements. The proposed "chlorophyll-interactive" method brings the surface net total heat flux closer to zero annually, despite a net heat loss from the ocean to the atmosphere.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833, https://doi.org/10.5194/gmd-16-4811-2023, https://doi.org/10.5194/gmd-16-4811-2023, 2023
Short summary
Short summary
Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Haohao Zhang, Andrea Storto, Xuezhi Bai, and Chunxue Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-3030, https://doi.org/10.5194/egusphere-2025-3030, 2025
Short summary
Short summary
Using a 1D coupled ice-ocean model, we quantified the effects of meltwater and ice-albedo feedback independently. The meltwater reduces melting by 19 % through thermal isolation, while ice-albedo feedback increases melting by 41 %, with nonlinear coupling between them. In winter, meltwater protects ice in weakly stratified areas by blocking Atlantic heat. Our study provides new insights into the relative importance of different components in the Arctic ice-ocean system.
Marie Bouih, Anne Barnoud, Chunxue Yang, Andrea Storto, Alejandro Blazquez, William Llovel, Robin Fraudeau, and Anny Cazenave
Ocean Sci., 21, 1425–1440, https://doi.org/10.5194/os-21-1425-2025, https://doi.org/10.5194/os-21-1425-2025, 2025
Short summary
Short summary
Present-day sea level rise is not uniform regionally. For better understanding of regional sea level variations, a classical approach is to compare the observed sea level trend patterns with those of the sum of the contributions. If the regional sea level trend budget is not closed, this allows the detection of errors in the observing systems. Our study shows that the trend budget is not closed in the North Atlantic Ocean and identifies errors in Argo-based salinity data as the main suspect.
Andrea Storto, Giulia Chierici, Julia Pfeffer, Anne Barnoud, Romain Bourdalle-Badie, Alejandro Blazquez, Davide Cavaliere, Noémie Lalau, Benjamin Coupry, Marie Drevillon, Sebastien Fourest, Gilles Larnicol, and Chunxue Yang
State Planet, 4-osr8, 12, https://doi.org/10.5194/sp-4-osr8-12-2024, https://doi.org/10.5194/sp-4-osr8-12-2024, 2024
Short summary
Short summary
The variability in the manometric sea level (i.e. the sea level mass component) in three ocean basins is investigated in this study using three different methods (reanalyses, gravimetry, and altimetry in combination with in situ observations). We identify the emerging long-term signals, the consistency of the datasets, and the influence of large-scale climate modes on the regional manometric sea level variations at both seasonal and interannual timescales.
Tim Trent, Marc Schröder, Shu-Peng Ho, Steffen Beirle, Ralf Bennartz, Eva Borbas, Christian Borger, Helene Brogniez, Xavier Calbet, Elisa Castelli, Gilbert P. Compo, Wesley Ebisuzaki, Ulrike Falk, Frank Fell, John Forsythe, Hans Hersbach, Misako Kachi, Shinya Kobayashi, Robert E. Kursinski, Diego Loyola, Zhengzao Luo, Johannes K. Nielsen, Enzo Papandrea, Laurence Picon, Rene Preusker, Anthony Reale, Lei Shi, Laura Slivinski, Joao Teixeira, Tom Vonder Haar, and Thomas Wagner
Atmos. Chem. Phys., 24, 9667–9695, https://doi.org/10.5194/acp-24-9667-2024, https://doi.org/10.5194/acp-24-9667-2024, 2024
Short summary
Short summary
In a warmer future, water vapour will spend more time in the atmosphere, changing global rainfall patterns. In this study, we analysed the performance of 28 water vapour records between 1988 and 2014. We find sensitivity to surface warming generally outside expected ranges, attributed to breakpoints in individual record trends and differing representations of climate variability. The implication is that longer records are required for high confidence in assessing climate trends.
Vincenzo de Toma, Daniele Ciani, Yassmin Hesham Essa, Chunxue Yang, Vincenzo Artale, Andrea Pisano, Davide Cavaliere, Rosalia Santoleri, and Andrea Storto
Geosci. Model Dev., 17, 5145–5165, https://doi.org/10.5194/gmd-17-5145-2024, https://doi.org/10.5194/gmd-17-5145-2024, 2024
Short summary
Short summary
This study explores methods to reconstruct diurnal variations in skin sea surface temperature in a model of the Mediterranean Sea. Our new approach, considering chlorophyll concentration, enhances spatial and temporal variations in the warm layer. Comparative analysis shows context-dependent improvements. The proposed "chlorophyll-interactive" method brings the surface net total heat flux closer to zero annually, despite a net heat loss from the ocean to the atmosphere.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833, https://doi.org/10.5194/gmd-16-4811-2023, https://doi.org/10.5194/gmd-16-4811-2023, 2023
Short summary
Short summary
Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Ed Hawkins, Philip Brohan, Samantha N. Burgess, Stephen Burt, Gilbert P. Compo, Suzanne L. Gray, Ivan D. Haigh, Hans Hersbach, Kiki Kuijjer, Oscar Martínez-Alvarado, Chesley McColl, Andrew P. Schurer, Laura Slivinski, and Joanne Williams
Nat. Hazards Earth Syst. Sci., 23, 1465–1482, https://doi.org/10.5194/nhess-23-1465-2023, https://doi.org/10.5194/nhess-23-1465-2023, 2023
Short summary
Short summary
We examine a severe windstorm that occurred in February 1903 and caused significant damage in the UK and Ireland. Using newly digitized weather observations from the time of the storm, combined with a modern weather forecast model, allows us to determine why this storm caused so much damage. We demonstrate that the event is one of the most severe windstorms to affect this region since detailed records began. The approach establishes a new tool to improve assessments of risk from extreme weather.
Stefan Brönnimann, Peter Stucki, Jörg Franke, Veronika Valler, Yuri Brugnara, Ralf Hand, Laura C. Slivinski, Gilbert P. Compo, Prashant D. Sardeshmukh, Michel Lang, and Bettina Schaefli
Clim. Past, 18, 919–933, https://doi.org/10.5194/cp-18-919-2022, https://doi.org/10.5194/cp-18-919-2022, 2022
Short summary
Short summary
Floods in Europe vary on time scales of several decades. Flood-rich and flood-poor periods alternate. Recently floods have again become more frequent. Long time series of peak stream flow, precipitation, and atmospheric variables reveal that until around 1980, these changes were mostly due to changes in atmospheric circulation. However, in recent decades the role of increasing atmospheric moisture due to climate warming has become more important and is now the main driver of flood changes.
Cited articles
Agarwal, N., Small, R. J., Bryan, F. O., Grooms, I., and Pegion, P. J.: Impact of stochastic ocean density corrections on air-sea flux variability, Geophys. Res. Lett., 50, e2023GL104248, https://doi.org/10.1029/2023GL104248, 2023.
Balmaseda, M. A., Dee, D., Vidard, A., and Anderson, D. L. T.: A multivariate treatment of bias for sequential data assimilation: Application to the tropical oceans, Q. J. Roy. Meteor. Soc., 133, 167–179, https://doi.org/10.1002/qj.12, 2007.
Barnier, B., Madec, G., Penduff, T., Molines, J.-M., Treguier, A.-M., Le Sommer, J., Beckmann, A., Biastoch, A., Böning, C., Dengg, J., Derval, C., Durand, E., Gulev, S., Remy, E., Talandier, C., Theetten, S., Maltrud, M., McClean, J., and De Cuevas, B.: Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy-permitting resolution, Ocean Dynam., 56, 543–567, https://doi.org/10.1007/s10236-006-0082-1, 2006.
Bonavita, M. and Laloyaux, P.: Machine learning for model error inference and correction, J. Adv. Model. Earth Sy., 12, e2020MS002232, https://doi.org/10.1029/2020MS002232, 2020.
Brodeau, L., Barnier, B., Treguier, A.-M., Penduff, T., and Gulev, S.: An ERA40-based atmospheric forcing for global ocean circulation models, Ocean Model., 31, 88–104, https://doi.org/10.1016/j.ocemod.2009.10.005, 2010.
Brodeau, L., Barnier, B., Gulev, S., and Woods, C.: Climatologically significant effects of some approximations in the bulk parameterizations of turbulent air-sea fluxes, J. Phys. Oceanogr., 47, 5–28, https://doi.org/10.1175/JPO-D-16-0169.1, 2016.
Carton, J. A., Chepurin, G. A., Chen, L., and Grodsky, S. A.: Improved global net surface heat flux, J. Geophys. Res.-Oceans, 123, 3144–3163, https://doi.org/10.1002/2017JC013137, 2018.
Chapman, W. E. and Berner, J.: A State-Dependent Model-Error Representation for Online Climate Model Bias Correction, ESS Open Archive, 23 November 2024, https://doi.org/10.22541/essoar.172526800.05354621/v2, 2024.
Chen, T.-C., Penny, S. G., Whitaker, J. S., Frolov, S., Pincus, R., and Tulich, S.: Correcting systematic and state-dependent errors in the NOAA FV3-GFS using neural networks, J. Adv. Model. Earth Sy., 14, e2022MS003309, https://doi.org/10.1029/2022MS003309, 2022.
Cronin, M. F., Gentemann, C. L., Edson, J., Ueki, I., Bourassa, M., Brown, S., Clayson, C. A., Fairall, C. W., Farrar, J. T., Gille, S. T., Gulev, S., Josey, S. A., Kato, S., Katsumata, M., Kent, E., Krug, M., Minnett, P. J., Parfitt, R., Pinker, R. T., Stackhouse Jr., P. W., Swart, S., Tomita, H., Vandemark, D., Weller, R. A., Yoneyama, K., Yu, L., and Zhang, D.: Air-Sea Fluxes With a Focus on Heat and Momentum, Front. Mar. Sci., 6, 430, https://doi.org/10.3389/fmars.2019.00430, 2019.
Deppenmeier, A. L., Haarsma, R. J., LeSager, P., and Hazeleger, W.: The effect of vertical ocean mixing on the tropical Atlantic in a coupled global climate model, Clim. Dynam., 54, 5089–5109, https://doi.org/10.1007/s00382-020-05270-x, 2020.
Farchi, A., Laloyaux, P., Bonavita, M., and Bocquet, M.: Using machine learning to correct model error in data assimilation and forecast applications, Q. J. Roy. Meteor. Soc., 147, 3067–3084, https://doi.org/10.1002/qj.4116, 2021.
Fisher, A., Rudin, C., and Dominici, F.: All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously, J. Mach. Learn. Res., 20, 1–81, 2019.
Forget, G., Campin, J.-M., Heimbach, P., Hill, C. N., Ponte, R. M., and Wunsch, C.: ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation, Geosci. Model Dev., 8, 3071–3104, https://doi.org/10.5194/gmd-8-3071-2015, 2015.
Frolov, S., Reynolds, C. A., Alexander, M., Flatau, M., Barton, N. P., Hogan, P., and Rowley, C.: Coupled Ocean–Atmosphere Covariances in Global Ensemble Simulations: Impact of an Eddy-Resolving Ocean, Mon. Weather Rev., 149, 1193–1209, https://doi.org/10.1175/MWR-D-20-0352.1, 2021.
Gaspar, P., Grégoris, Y., and Lefevre, J.-M.: A simple eddy kinetic energy model for simulations of the oceanic vertical mixing: Tests at station Papa and long-term upper ocean study site, J. Geophys. Res., 95, 16179–16193, https://doi.org/10.1029/JC095iC09p16179, 1990.
Good, S. A., Martin, M. J., and Rayner N. A.: EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, J. Geophys. Res.-Oceans, 118, 6704–6716, https://doi.org/10.1002/2013JC009067, 2013 (data available at: https://www.metoffice.gov.uk/hadobs/en4/download-en4-2-2.html, last access: 30 July 2025).
Greenwell, B. M., and Boehmke, B. C.: Variable Importance Plots – An Introduction to the vip Package, R J., 12, 343–366, https://doi.org/10.32614/RJ-2020-013, 2020.
Gregory, W., Bushuk, M., Adcroft, A., Zhang, Y., and Zanna, L.: Deep learning of systematic sea ice model errors from data assimilation increments, J. Adv. Model. Earth Sy., 15, e2023MS003757, https://doi.org/10.1029/2023MS003757, 2023.
Griffies, S. M., Danabasoglu, G., Durack, P. J., Adcroft, A. J., Balaji, V., Böning, C. W., Chassignet, E. P., Curchitser, E., Deshayes, J., Drange, H., Fox-Kemper, B., Gleckler, P. J., Gregory, J. M., Haak, H., Hallberg, R. W., Heimbach, P., Hewitt, H. T., Holland, D. M., Ilyina, T., Jungclaus, J. H., Komuro, Y., Krasting, J. P., Large, W. G., Marsland, S. J., Masina, S., McDougall, T. J., Nurser, A. J. G., Orr, J. C., Pirani, A., Qiao, F., Stouffer, R. J., Taylor, K. E., Treguier, A. M., Tsujino, H., Uotila, P., Valdivieso, M., Wang, Q., Winton, M., and Yeager, S. G.: OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project, Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, 2016.
Gupta, A. S., Jourdain, N. C., Brown, J. N., and Monselesan, D.: Climate Drift in the CMIP5 Models, J. Climate, 26, 8597–8615, https://doi.org/10.1175/JCLI-D-12-00521.1, 2013.
Hakuba, M. Z., Fourest, S., Boyer, T., Meyssignac, B., Carton, J. A., Forget, G., Cheng, L., Giglio, D., Johnson, G. C., Kato, S., Killick, R. E., Kolodziejczyk, N., Kuusela, M., Landerer, F., Llovel, W., Locarnini, R., Loeb, N., Lyman, J. M., Mishonov, A., Pilewskie, P., Reagan, J., Storto, A., Sukianto, T., and von Schuckmann, K.: Trends and Variability in Earth's Energy Imbalance and Ocean Heat Uptake Since 2005, Surv. Geophys., 45, 1721–1756, https://doi.org/10.1007/s10712-024-09849-5, 2024.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
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.
Hornik, K., Stinchcombe, M., and White, H.: Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359–366, https://doi.org/10.1016/0893-6080(89)90020-8, 1989.
Huang, B., Liu, C., Banzon, V., Freeman, E., Graham, G., Hankins, B., Smith, T., and Zhang, H.-M.: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1, J. Climate, 34, 2923–2939, https://doi.org/10.1175/JCLI-D-20-0166.1, 2021 (data available at: https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html, last access: 30 July 2025).
Huber, M. B. and Zanna, L.: Drivers of uncertainty in simulated ocean circulation and heat uptake, Geophys. Res. Lett., 44, 1402–1413, https://doi.org/10.1002/2016GL071587, 2017.
Ishii, M., Shouji, A., Sugimoto, S., and Matsumoto, T.: Objective analyses of sea-surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe Collection, Int. J. Climatol., 25, 865–879, 2005.
Jia, Y., Richards, K. J., and Annamalai, H.: The impact of vertical resolution in reducing biases in sea surface temperature in a tropical Pacific Ocean model, Ocean Model., 157, 101722, https://doi.org/10.1016/j.ocemod.2020.101722, 2021.
Kato, S., Loeb, N. G., Rose, F. G., Doelling, D. R., Rutan, D. A., Caldwell, T. E., Yu, L., and Weller, R. A.: Surface irradiances consistent with CERES-derived top-of-atmosphere shortwave and longwave irradiances, J. Climate, 26, 2719–2740, 2013.
Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K., and Janssen, P.: A coupled data assimilation system for climate reanalysis, Q. J. Roy. Meteor. Soc., 142, 65–78, 2016.
Large, W. G. and Yeager, S. G.: 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.
Lewis, H. W., Siddorn, J., Castillo Sanchez, J. M., Petch, J., Edwards, J. M., and Smyth, T.: Evaluating the impact of atmospheric forcing and air–sea coupling on near-coastal regional ocean prediction, Ocean Sci., 15, 761–778, https://doi.org/10.5194/os-15-761-2019, 2019.
Lin, X., Massonnet, F., Fichefet, T., and Vancoppenolle, M.: Impact of atmospheric forcing uncertainties on Arctic and Antarctic sea ice simulations in CMIP6 OMIP models, The Cryosphere, 17, 1935–1965, https://doi.org/10.5194/tc-17-1935-2023, 2023.
Madec, G. and The NEMO System Team: NEMO Ocean Engine, Note Du Pole De Modélisation, Institut Pierre-Simon Laplace, Paris, France, https://doi.org/10.5281/zenodo.3248739, 2017.
Marzocchi, A., Nurser, A. J. G., Clément, L., and McDonagh, E. L.: Surface atmospheric forcing as the driver of long-term pathways and timescales of ocean ventilation, Ocean Sci., 17, 935–952, https://doi.org/10.5194/os-17-935-2021, 2021.
Mitchell, L. and Carrassi, A.: Accounting for model error due to unresolved scales within ensemble Kalman filtering, Q. J. Roy. Meteor. Soc., 141, 1417–1428, https://doi.org/10.1002/qj.2451, 2015.
Ohishi, S., Miyoshi, T., and Kachi, M.: Impact of atmospheric forcing on SST biases in the LETKF-based ocean research analysis (LORA), Ocean Model., 189, 102357, https://doi.org/10.1016/j.ocemod.2024.102357, 2024.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003 (data available at: https://www.metoffice.gov.uk/hadobs/hadisst, last access: 30 July 2025).
Richards, K. J., Xie, S., and Miyama, T.: Vertical Mixing in the Ocean and Its Impact on the Coupled Ocean–Atmosphere System in the Eastern Tropical Pacific, J. Climate, 22, 3703–3719, https://doi.org/10.1175/2009JCLI2702.1, 2009.
Roberts, M. J., Hewitt, H. T., Hyder, P., Ferreira, D., Josey, S. A., Mizielinski, M., and Shelly, A.: Impact of ocean resolution on coupled air-sea fluxes and large-scale climate, Geophys. Res. Lett., 43, 10430–10438, https://doi.org/10.1002/2016GL070559, 2016.
Sausen, R., Barthel, K., and Hasselmann, K.: Coupled ocean-atmosphere models with flux correction, Clim. Dynam., 2, 145–163, https://doi.org/10.1007/BF01053472, 1988.
Small, R. J., Bryan, F. O., Bishop, S. P., and Tomas, R. A.: Air–Sea Turbulent Heat Fluxes in Climate Models and Observational Analyses: What Drives Their Variability?, J. Climate, 32, 2397–2421, https://doi.org/10.1175/JCLI-D-18-0576.1, 2019.
Storto, A.: Assets (code, scripts and datasets) for the manuscript “Correction of the Air-Sea Heat Fluxes in Ocean General Circulation Models Using Neural Networks” (1.0), Zenodo [Data set and Code], https://doi.org/10.5281/zenodo.13380698, 2024.
Storto, A.: NEMO_4.0.7, GitLab [code], https://baltig.cnr.it/nemo_ismar-rm/nemo_4.0.7/-/tree/3.0?ref_type=tags (last access: 30 July 2025), 2025.
Storto, A. and Oddo, P.: Optimal Assimilation of Daytime SST Retrievals from SEVIRI in a Regional Ocean Prediction System, Remote Sens.-Basel, 11, 2776, https://doi.org/10.3390/rs11232776, 2019.
Storto, A. and Yang, C.: Stochastic schemes for the perturbation of the atmospheric boundary conditions in ocean general circulation models, Front. Mar. Sci., 10, 1155803, https://doi.org/10.3389/fmars.2023.1155803, 2023.
Storto, A. and Yang, C.: Acceleration of the ocean warming from 1961 to 2022 unveiled by large-ensemble reanalyses, Nat. Commun., 15, 545, https://doi.org/10.1038/s41467-024-44749-7, 2024.
Storto, A., Yang, C., and Masina, S.: Sensitivity of global ocean heat content from reanalyses to the atmospheric reanalysis forcing: A comparative study, Geophys. Res. Lett., 43, 5261–5270, https://doi.org/10.1002/2016GL068605, 2016a.
Storto, A., Masina, S., and Navarra, A.: Evaluation of the CMCC eddy-permitting global ocean physical reanalysis system (C-GLORS, 1982–2012) and its assimilation components, Q. J. Roy. Meteor. Soc., 142, 738–758, https://doi.org/10.1002/qj.2673, 2016b.
Storto, A., Balmaseda, M. A., de Boisseson, E., Giese, B. S., Masina, S., and Yang, C.: The 20th century global warming signature on the ocean at global and basin scales as depicted from historical reanalyses, Int. J. Climatol., 41, 5977–5997, https://doi.org/10.1002/joc.7163, 2021.
Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., Kim, W. M., Yeager, S. G., Danabasoglu, G., Suzuki, T., Bamber, J. L., Bentsen, M., Böning, C. W., Bozec, A., Chassignet, E. P., Curchitser, E., Boeira Dias, F., Durack, P. J., Griffies, S. M., Harada, Y., Ilicak, M., Josey, S. A., Kobayashi, C., Kobayashi, S., Komuro, Y., Large, W. G., Le Sommer, J., Marsland, S. J., Masina, S., Scheinert, M., Tomita, H., Valdivieso, M., and Yamazaki, D.: JRA-55 based surface dataset for driving ocean-sea-ice models (JRA55-do), Ocean Model., 130, 79–139, https://doi.org/10.1016/j.ocemod.2018.07.002, 2018.
Valdivieso, M., Haines, K., Balmaseda, M., Chang, Y.-S., Drevillon, M., Ferry, N., Fujii, Y., Kohl, A., Storto, A., Toyoda, T., Wang, X., Waters, J., Xue, Y., Yin, Y., Barnier, B., Hernandez, F., Kumar, A., Lee, T., Masina, S., Peterson, A. k.: An assessment of air–sea heat fluxes from ocean and coupled reanalyses, Clim. Dynam., 49, 983–1008, https://doi.org/10.1007/s00382-015-2843-3, 2017.
Vidard, P. A., Le Dimet, F.-X., and Piacentini, A.: Determination of optimal nudging coefficients, Tellus A, 55, 1–15, 2003.
Waters, J., Lea, D. J., Martin, M. J., Mirouze, I., Weaver, A., and While, J.: Implementing a variational data assimilation system in an operational 1/4 degree global ocean model, Q. J. Roy. Meteor. Soc., 141, 333–349, https://doi.org/10.1002/qj.2388, 2015.
Yang, C., Masina, S., and Storto, A.: Historical ocean reanalyses (1900–2010) using different data assimilation strategies, Q. J. Roy. Meteor. Soc., 143, 479–493, https://doi.org/10.1002/qj.2936, 2017.
Yu, L.: Global air–sea fluxes of heat, fresh water, and momentum: energy budget closure and unanswered questions, Annu. Rev. Mar. Sci., 11, 227–248, https://doi.org/10.1146/annurev-marine-010816-060704, 2019.
Zhou, S., Shi, R., Yu, H., Zhang, X., Dai, J., Huang, X., and Xu, F.: A physical-informed neural network for improving air-sea turbulent heat flux parameterization, J. Geophys. Res.-Atmos., 129, e2023JD040603, https://doi.org/10.1029/2023JD040603, 2024.
Zou, X., Navon, I. M., and Ledimet, F. X.: An optimal nudging data assimilation scheme using parameter estimation, Q. J. Roy. Meteor. Soc., 118, 1163–1186, 1992.
Short summary
Inaccuracies in air–sea heat fluxes severely degrade the accuracy of ocean numerical simulations. Here, we use artificial neural networks to correct air–sea heat fluxes as a function of oceanic and atmospheric state predictors. The correction successfully improves surface and subsurface ocean temperatures beyond the training period and in prediction experiments.
Inaccuracies in air–sea heat fluxes severely degrade the accuracy of ocean numerical...
Special issue