Articles | Volume 17, issue 18
https://doi.org/10.5194/gmd-17-6967-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-6967-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spurious numerical mixing under strong tidal forcing: a case study in the south-east Asian seas using the Symphonie model (v3.1.2)
Adrien Garinet
CORRESPONDING AUTHOR
LEGOS (CNES/CNRS/IRD/UT3), Université de Toulouse, Toulouse, France
Direction Générale de l'Armement, Ministère des Armées, Paris, France
Marine Herrmann
LEGOS (CNES/CNRS/IRD/UT3), Université de Toulouse, Toulouse, France
Patrick Marsaleix
LEGOS (CNES/CNRS/IRD/UT3), Université de Toulouse, Toulouse, France
Juliette Pénicaud
LEGOS (CNES/CNRS/IRD/UT3), Université de Toulouse, Toulouse, France
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Quentin Desmet, Marine Herrmann, and Thanh Ngo-Duc
EGUsphere, https://doi.org/10.5194/egusphere-2025-1579, https://doi.org/10.5194/egusphere-2025-1579, 2025
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Climate model performance at the air–sea interface has long been overlooked across the Southeast Asian seas. We thus assess various regional model physics configurations in this regard. Finding one optimal configuration is challenging: reliable rainfall rarely coincides with correct radiative heating. Simulations of rainfall however yield more dissensus, suggesting that this variable should be prioritized, for which the best results are obtained with the cumulus convection scheme of Tiedtke.
Lisa Weiss, Marine Herrmann, Patrick Marsaleix, Matthieu Bompoil, and Christophe Maes
EGUsphere, https://doi.org/10.5194/egusphere-2025-1918, https://doi.org/10.5194/egusphere-2025-1918, 2025
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We developed a high-resolution ocean model to study the dispersion of marine debris across the Indian Ocean, from small coastal scales to the open sea. Our results show that both model resolution and the effect of wind-driven surface waves play a key role in shaping ocean circulation, seasonal energy budgets and floating debris trajectories. High-resolution currents and wave forcing increase the spread and distance traveled by drifting material, especially during monsoon periods.
Thanh Huyen Tran, Alexei Sentchev, Thai To Duy, Marine Herrmann, Sylvain Ouillon, and Kim Cuong Nguyen
Ocean Sci., 21, 1–18, https://doi.org/10.5194/os-21-1-2025, https://doi.org/10.5194/os-21-1-2025, 2025
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For the first time, high-resolution surface current data from high-frequency radar have been obtained along the central and southern coasts of Vietnam, and combined with a modelling approach, this is helping scientists to understand coastal processes. The research showed that the surface circulation is driven not only by winds, but also by other factors. This can enrich public knowledge of the coastal dynamics that govern other environmental impacts along the coasts.
Marine Herrmann, Thai To Duy, and Patrick Marsaleix
Ocean Sci., 20, 1013–1033, https://doi.org/10.5194/os-20-1013-2024, https://doi.org/10.5194/os-20-1013-2024, 2024
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In summer, deep, cold waters rise to the surface along and off the Vietnamese coast. This upwelling of water lifts nutrients, inducing biological activity that is important for fishery resources. Strong tides occur on the shelf off the Mekong Delta. By increasing the mixing of ocean waters and modifying currents, they are a major factor in the development of upwelling on the shelf, accounting for ~75 % of its average summer intensity.
Ngoc B. Trinh, Marine Herrmann, Caroline Ulses, Patrick Marsaleix, Thomas Duhaut, Thai To Duy, Claude Estournel, and R. Kipp Shearman
Geosci. Model Dev., 17, 1831–1867, https://doi.org/10.5194/gmd-17-1831-2024, https://doi.org/10.5194/gmd-17-1831-2024, 2024
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A high-resolution model was built to study the South China Sea (SCS) water, heat, and salt budgets. Model performance is demonstrated by comparison with observations and simulations. Important discards are observed if calculating offline, instead of online, lateral inflows and outflows of water, heat, and salt. The SCS mainly receives water from the Luzon Strait and releases it through the Mindoro, Taiwan, and Karimata straits. SCS surface interocean water exchanges are driven by monsoon winds.
Caroline Ulses, Claude Estournel, Patrick Marsaleix, Karline Soetaert, Marine Fourrier, Laurent Coppola, Dominique Lefèvre, Franck Touratier, Catherine Goyet, Véronique Guglielmi, Fayçal Kessouri, Pierre Testor, and Xavier Durrieu de Madron
Biogeosciences, 20, 4683–4710, https://doi.org/10.5194/bg-20-4683-2023, https://doi.org/10.5194/bg-20-4683-2023, 2023
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Deep convection plays a key role in the circulation, thermodynamics, and biogeochemical cycles in the Mediterranean Sea, considered to be a hotspot of biodiversity and climate change. In this study, we investigate the seasonal and annual budget of dissolved inorganic carbon in the deep-convection area of the northwestern Mediterranean Sea.
Joelle Habib, Caroline Ulses, Claude Estournel, Milad Fakhri, Patrick Marsaleix, Mireille Pujo-Pay, Marine Fourrier, Laurent Coppola, Alexandre Mignot, Laurent Mortier, and Pascal Conan
Biogeosciences, 20, 3203–3228, https://doi.org/10.5194/bg-20-3203-2023, https://doi.org/10.5194/bg-20-3203-2023, 2023
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The Rhodes Gyre, eastern Mediterranean Sea, is the main Levantine Intermediate Water formation site. In this study, we use a 3D physical–biogeochemical model to investigate the seasonal and interannual variability of organic carbon dynamics in the gyre. Our results show its autotrophic nature and its high interannual variability, with enhanced primary production, downward exports, and onward exports to the surrounding regions during years marked by intense heat losses and deep mixed layers.
Marine Herrmann, Thai To Duy, and Claude Estournel
Ocean Sci., 19, 453–467, https://doi.org/10.5194/os-19-453-2023, https://doi.org/10.5194/os-19-453-2023, 2023
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The South Vietnam upwelling develops in summer along and off the Vietnamese coast. It brings cold and nutrient-rich waters to the surface, allowing photosynthesis essential to marine ecosystems and fishing resources. We show here that its daily variations are mainly due to the wind, thus predictable, in the southern shelf and coastal regions. However, they are more chaotic in the offshore area, and especially in the northern area, due to the influence of eddies of a highly chaotic nature.
Thai To Duy, Marine Herrmann, Claude Estournel, Patrick Marsaleix, Thomas Duhaut, Long Bui Hong, and Ngoc Trinh Bich
Ocean Sci., 18, 1131–1161, https://doi.org/10.5194/os-18-1131-2022, https://doi.org/10.5194/os-18-1131-2022, 2022
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The South Vietnam Upwelling develops in the coastal and offshore regions of the southwestern South China Sea under the influence of summer monsoon winds. Cold, nutrient-rich waters rise to the surface, where photosynthesis occurs and is essential for fishing activity. We have developed a very high-resolution model to better understand the factors that drive the variability of this upwelling at different scales: daily chronology to summer mean of wind and mesoscale to regional circulation.
Gaël Many, Caroline Ulses, Claude Estournel, and Patrick Marsaleix
Biogeosciences, 18, 5513–5538, https://doi.org/10.5194/bg-18-5513-2021, https://doi.org/10.5194/bg-18-5513-2021, 2021
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The Gulf of Lion shelf is one of the most productive areas in the Mediterranean. A model is used to study the mechanisms that drive the particulate organic carbon (POC). The model reproduces the annual cycle of primary production well. The shelf appears as an autotrophic ecosystem with a high production and as a source of POC for the adjacent basin. The increase in temperature induced by climate change could impact the trophic status of the shelf.
Caroline Ulses, Claude Estournel, Marine Fourrier, Laurent Coppola, Fayçal Kessouri, Dominique Lefèvre, and Patrick Marsaleix
Biogeosciences, 18, 937–960, https://doi.org/10.5194/bg-18-937-2021, https://doi.org/10.5194/bg-18-937-2021, 2021
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We analyse the seasonal cycle of O2 and estimate an annual O2 budget in the north-western Mediterranean deep-convection region, using a numerical model. We show that this region acts as a large sink of atmospheric O2 and as a major source of O2 for the western Mediterranean Sea. The decrease in the deep convection intensity predicted in recent projections may have important consequences on the overall uptake of O2 in the Mediterranean Sea and on the O2 exchanges with the Atlantic Ocean.
Cited articles
Adcroft, A. and Campin, J.-M.: Rescaled Height Coordinates for Accurate Representation of Free-Surface Flows in Ocean Circulation Models, Ocean Model., 7, 269–284, https://doi.org/10.1016/j.ocemod.2003.09.003, 2004. a
Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., and Pappenberger, F.: GloFAS – global ensemble streamflow forecasting and flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, 2013. a
Alford, M. H., Gregg, M. C., and Ilyas, M.: Diapycnal Mixing in the Banda Sea: Results of the First Microstructure Measurements in the Indonesian Throughflow, Geophys. Res. Lett., 26, 2741–2744, https://doi.org/10.1029/1999GL002337, 1999. a
Álvarez, Ó., Izquierdo, A., González, C. J., Bruno, M., and Mañanes, R.: Some Considerations about Non-Hydrostatic vs. Hydrostatic Simulation of Short-Period Internal Waves. A Case Study: The Strait of Gibraltar, Cont. Shelf Res., 181, 174–186, https://doi.org/10.1016/j.csr.2019.05.016, 2019. a
Apel, J. R., Holbrook, J. R., Liu, A. K., and Tsai, J. J.: The Sulu Sea Internal Soliton Experiment, J. Phys. Oceanogr., 15, 1625–1651, https://doi.org/10.1175/1520-0485(1985)015<1625:TSSISE>2.0.CO;2, 1985. a, b, c
Arbic, B. K.: Incorporating Tides and Internal Gravity Waves within Global Ocean General Circulation Models: A Review, Prog. Oceanogr., 206, 102824, https://doi.org/10.1016/j.pocean.2022.102824, 2022. a
Argo: Argo float data and metadata from Global Data Assembly Centre (Argo GDAC), SEANOE [data set], https://doi.org/10.17882/42182, 2000. a
Banerjee, T., Danilov, S., and Klingbeil, K.: Discrete variance decay analysis of spurious mixing, Ocean Model., accepted, 2024. a
Bendinger, A., Cravatte, S., Gourdeau, L., Brodeau, L., Albert, A., Tchilibou, M., Lyard, F., and Vic, C.: Regional modeling of internal-tide dynamics around New Caledonia – Part 1: Coherent internal-tide characteristics and sea surface height signature, Ocean Sci., 19, 1315–1338, https://doi.org/10.5194/os-19-1315-2023, 2023. a
Berntsen, J., Xing, J., and Davies, A. M.: Numerical Studies of Flow over a Sill: Sensitivity of the Non-Hydrostatic Effects to the Grid Size, Ocean Dynam., 59, 1043–1059, https://doi.org/10.1007/s10236-009-0227-0, 2009. a
Blumberg, A. F. and Mellor, G. L.: A Description of a Three-Dimensional Coastal Ocean Circulation Model, in: Three-Dimensional Coastal Ocean Models, American Geophysical Union (AGU), 1–16, ISBN 978-1-118-66504-6, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/CO004p0001 (last access: 28 August 2024), 1987. a
Bryan, F.: Parameter Sensitivity of Primitive Equation Ocean General Circulation Models, J. Phys. Oceanogr., 17, 970–985, https://doi.org/10.1175/1520-0485(1987)017<0970:PSOPEO>2.0.CO;2, 1987. a
Burchard, H. and Bolding, K.: Comparative Analysis of Four Second-Moment Turbulence Closure Models for the Oceanic Mixed Layer, J. Phys. Oceanogr., 31, 1943–1968, https://doi.org/10.1175/1520-0485(2001)031<1943:CAOFSM>2.0.CO;2, 2001. a
Burchard, H. and Rennau, H.: Comparative Quantification of Physically and Numerically Induced Mixing in Ocean Models, Ocean Model., 20, 293–311, https://doi.org/10.1016/j.ocemod.2007.10.003, 2008. a, b
Castruccio, F. S., Curchitser, E. N., and Kleypas, J. A.: A Model for Quantifying Oceanic Transport and Mesoscale Variability in the Coral Triangle of the Indonesian/Philippines Archipelago, J. Geophys. Res.-Oceans, 118, 6123–6144, https://doi.org/10.1002/2013JC009196, 2013. a
Cushman-Roisin, B. and Beckers, J.-M.: Introduction to Geophysical Fluid Dynamics: Physical and Numerical Aspects, no. v. 101 in International Geophysics Series, 2nd edn., Academic Press, Waltham, MA, ISBN 978-0-12-088759-0, 2011. a
Damien, P., Bosse, A., Testor, P., Marsaleix, P., and Estournel, C.: Modeling Postconvective Submesoscale Coherent Vortices in the Northwestern Mediterranean Sea, J. Geophys. Res.-Oceans, 122, 9937–9961, https://doi.org/10.1002/2016JC012114, 2017. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) System, Remote Sens. Environ., 116, 140–158, https://doi.org/10.1016/j.rse.2010.10.017, 2012. a
Dukhovskoy, D. S., Morey, S. L., Martin, P. J., O'Brien, J. J., and Cooper, C.: Application of a Vanishing, Quasi-Sigma, Vertical Coordinate for Simulation of High-Speed, Deep Currents over the Sigsbee Escarpment in the Gulf of Mexico, Ocean Model., 28, 250–265, https://doi.org/10.1016/j.ocemod.2009.02.009, 2009. a
Estournel, C., Marsaleix, P., and Ulses, C.: A New Assessment of the Circulation of Atlantic and Intermediate Waters in the Eastern Mediterranean, Prog. Oceanogr., 198, 102673, https://doi.org/10.1016/j.pocean.2021.102673, 2021. a
E.U. Copernicus Marine Service Information (CMEMS): Global Ocean Physics Reanalysis (GLORYS12V1), Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00021, 2023a. a, b
E.U. Copernicus Marine Service Information (CMEMS): Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed, Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00168, 2023b. a
Ffield, A. and Gordon, A. L.: Vertical Mixing in the Indonesian Thermocline, J. Phys. Oceanogr., 22, 184–195, https://doi.org/10.1175/1520-0485(1992)022<0184:VMITIT>2.0.CO;2, 1992. a
Ffield, A. and Gordon, A. L.: Tidal Mixing Signatures in the Indonesian Seas, J. Phys. Oceanogr., 26, 1924–1937, https://doi.org/10.1175/1520-0485(1996)026<1924:TMSITI>2.0.CO;2, 1996. a
Fox-Kemper, B., Adcroft, A., Böning, C. W., Chassignet, E. P., Curchitser, E., Danabasoglu, G., Eden, C., England, M. H., Gerdes, R., Greatbatch, R. J., Griffies, S. M., Hallberg, R. W., Hanert, E., Heimbach, P., Hewitt, H. T., Hill, C. N., Komuro, Y., Legg, S., Le Sommer, J., Masina, S., Marsland, S. J., Penny, S. G., Qiao, F., Ringler, T. D., Treguier, A. M., Tsujino, H., Uotila, P., and Yeager, S. G.: Challenges and Prospects in Ocean Circulation Models, Frontiers in Marine Science, 6, 65, https://doi.org/10.3389/fmars.2019.00065, 2019. a
Garinet, A.: Spurious numerical mixing in a regional configuration of the Symphonie ocean model over the South-East asian Seas (3.1.2 of the Symphonie ocean model.), Zenodo [data set], https://doi.org/10.5281/zenodo.10715502, 2024. a
Gerdes, R., Köberle, C., and Willebrand, J.: The Influence of Numerical Advection Schemes on the Results of Ocean General Circulation Models, Clim. Dynam., 5, 211–226, https://doi.org/10.1007/BF00210006, 1991. a
Gibson, A. H., Hogg, A. M., Kiss, A. E., Shakespeare, C. J., and Adcroft, A.: Attribution of Horizontal and Vertical Contributions to Spurious Mixing in an Arbitrary Lagrangian–Eulerian Ocean Model, Ocean Model., 119, 45–56, https://doi.org/10.1016/j.ocemod.2017.09.008, 2017. a, b, c
Gonzalez, N.: Modélisation Multi-Échelle Du Détroit de Gibraltar et de Son Rôle de Régulateur Du Climat Méditerranéen, PhD thesis, Université de Toulouse, Université Toulouse III – Paul Sabatier, 2023. a
Gonzalez, N., Waldman, R., Sannino, G., Giordani, H., and Somot, S.: Understanding Tidal Mixing at the Strait of Gibraltar: A High-Resolution Model Approach, Prog. Oceanogr., 212, 102980, https://doi.org/10.1016/j.pocean.2023.102980, 2023. a, b
Good, S., Fiedler, E., Mao, C., Martin, M. J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., and Worsfold, M.: The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses, Remote Sens., 12, 720, https://doi.org/10.3390/rs12040720, 2020. a
Gordon, A.: Oceanography of the Indonesian Seas and Their Throughflow, oceanog, 18, 14–27, https://doi.org/10.5670/oceanog.2005.01, 2005. a
Griffies, S. M. and Hallberg, R. W.: Biharmonic Friction with a Smagorinsky-Like Viscosity for Use in Large-Scale Eddy-Permitting Ocean Models, Mon. Weather Rev., 128, 2935–2946, https://doi.org/10.1175/1520-0493(2000)128<2935:BFWASL>2.0.CO;2, 2000. a
Griffies, S. M., Pacanowski, R. C., and Hallberg, R. W.: Spurious Diapycnal Mixing Associated with Advection in a z-Coordinate Ocean Model, Mon. Weather Rev., 128, 538–564, https://doi.org/10.1175/1520-0493(2000)128<0538:SDMAWA>2.0.CO;2, 2000. a, b, c, d
Griffies, S. M., Adcroft, A., and Hallberg, R. W.: A Primer on the Vertical Lagrangian-Remap Method in Ocean Models Based on Finite Volume Generalized Vertical Coordinates, J. Adv. Model. Earth Sy., 12, e2019MS001954, https://doi.org/10.1029/2019MS001954, 2020. a
Hatayama, T., Awaji, T., and Akitomo, K.: Tidal Currents in the Indonesian Seas and Their Effect on Transport and Mixing, J. Geophys. Res.-Oceans, 101, 12353–12373, https://doi.org/10.1029/96JC00036, 1996. a
Hecht, M. W.: Cautionary Tales of Persistent Accumulation of Numerical Error: Dispersive Centered Advection, Ocean Model., 35, 270–276, https://doi.org/10.1016/j.ocemod.2010.07.005, 2010. a
Herrmann, M., To Duy, T., and Estournel, C.: Intraseasonal variability of the South Vietnam upwelling, South China Sea: influence of atmospheric forcing and ocean intrinsic variability, Ocean Sci., 19, 453–467, https://doi.org/10.5194/os-19-453-2023, 2023. a
Holmes, R. M., Zika, J. D., Griffies, S. M., Hogg, A. M., Kiss, A. E., and England, M. H.: The Geography of Numerical Mixing in a Suite of Global Ocean Models, J. Adv. Model. Earth Sy., 13, e2020MS002333, https://doi.org/10.1029/2020MS002333, 2021. a, b, c
Hoyer, S. and Hamman, J.: Xarray: N-D Labeled Arrays and Datasets in Python, Journal of Open Research Software, 5, 10, https://doi.org/10.5334/jors.148, 2017. a
Ilıcak, M., Adcroft, A. J., Griffies, S. M., and Hallberg, R. W.: Spurious Dianeutral Mixing and the Role of Momentum Closure, Ocean Model., 45–46, 37–58, https://doi.org/10.1016/j.ocemod.2011.10.003, 2012. a
Iskandar, M. R., Jia, Y., Sasaki, H., Furue, R., Kida, S., Suga, T., and Richards, K. J.: Effects of High-Frequency Flow Variability on the Pathways of the Indonesian Throughflow, J. Geophys. Res.-Oceans, 128, e2022JC019610, https://doi.org/10.1029/2022JC019610, 2023. a
Jochum, M. and Potemra, J.: Sensitivity of Tropical Rainfall to Banda Sea Diffusivity in the Community Climate System Model, J. Climate, 21, 6445–6454, https://doi.org/10.1175/2008JCLI2230.1, 2008. a
Juricke, S., Danilov, S., Koldunov, N., Oliver, M., Sein, D. V., Sidorenko, D., and Wang, Q.: A Kinematic Kinetic Energy Backscatter Parametrization: From Implementation to Global Ocean Simulations, J. Adv. Model. Earth Sy., 12, e2020MS002175, https://doi.org/10.1029/2020MS002175, 2020a. a, b, c
Juricke, S., Danilov, S., Koldunov, N., Oliver, M., and Sidorenko, D.: Ocean Kinetic Energy Backscatter Parametrization on Unstructured Grids: Impact on Global Eddy-Permitting Simulations, J. Adv. Model. Earth Sy., 12, e2019MS001855, https://doi.org/10.1029/2019MS001855, 2020b. a, b
Katavouta, A., Polton, J. A., Harle, J. D., and Holt, J. T.: Effect of Tides on the Indonesian Seas Circulation and Their Role on the Volume, Heat and Salt Transports of the Indonesian Throughflow, J. Geophys. Res.-Oceans, 127, e2022JC018524, https://doi.org/10.1029/2022JC018524, 2022. a, b, c, d
Kent, J., Whitehead, J. P., Jablonowski, C., and Rood, R. B.: Determining the Effective Resolution of Advection Schemes. Part I: Dispersion Analysis, J. Comput. Phys., 278, 485–496, https://doi.org/10.1016/j.jcp.2014.01.043, 2014. a
Klingbeil, K., Mohammadi-Aragh, M., Gräwe, U., and Burchard, H.: Quantification of Spurious Dissipation and Mixing – Discrete Variance Decay in a Finite-Volume Framework, Ocean Model., 81, 49–64, https://doi.org/10.1016/j.ocemod.2014.06.001, 2014. a
Klingbeil, K., Burchard, H., Danilov, S., Goetz, C., and Iske, A.: Reducing Spurious Diapycnal Mixing in Ocean Models, in: Energy Transfers in Atmosphere and Ocean, edited by Eden, C. and Iske, A., Mathematics of Planet Earth, Springer International Publishing, Cham, 245–286, ISBN 978-3-030-05704-6, https://doi.org/10.1007/978-3-030-05704-6_8, 2019. a
Koch-Larrouy, A., Madec, G., Bouruet-Aubertot, P., Gerkema, T., Bessières, L., and Molcard, R.: On the Transformation of Pacific Water into Indonesian Throughflow Water by Internal Tidal Mixing, Geophys. Res. Lett., 34, L04604, https://doi.org/10.1029/2006GL028405, 2007. a, b, c
Koch-Larrouy, A., Lengaigne, M., Terray, P., Madec, G., and Masson, S.: Tidal Mixing in the Indonesian Seas and Its Effect on the Tropical Climate System, Clim. Dynam., 34, 891–904, https://doi.org/10.1007/s00382-009-0642-4, 2010. a
Kolodziejczyk, N., Hamon, M., Boutin, J., Vergely, J.-L., Reverdin, G., Supply, A., and Reul, N.: Objective Analysis of SMOS and SMAP Sea Surface Salinity to Reduce Large-Scale and Time-Dependent Biases from Low to High Latitudes, J. Atmos. Ocean. Tech., 38, 405–421, https://doi.org/10.1175/JTECH-D-20-0093.1, 2021. a, b
Large, W. G. and Yeager, S.: Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies (No. NCAR/TN-460+STR), University Corporation for Atmospheric Research, https://doi.org/10.5065/D6KK98Q6, 2004. a, b
Leclair, M. and Madec, G.: -Coordinate, an Arbitrary Lagrangian–Eulerian Coordinate Separating High and Low Frequency Motions, Ocean Model., 37, 139–152, https://doi.org/10.1016/j.ocemod.2011.02.001, 2011. a, b
Lee, M.-M., Coward, A. C., and Nurser, A. J. G.: Spurious Diapycnal Mixing of the Deep Waters in an Eddy-Permitting Global Ocean Model, J. Phys. Oceanogr., 32, 1522–1535, https://doi.org/10.1175/1520-0485(2002)032<1522:SDMOTD>2.0.CO;2, 2002. a, b
Lemarié, F., Debreu, L., Madec, G., Demange, J., Molines, J. M., and Honnorat, M.: Stability Constraints for Oceanic Numerical Models: Implications for the Formulation of Time and Space Discretizations, Ocean Model., 92, 124–148, https://doi.org/10.1016/j.ocemod.2015.06.006, 2015. a
Leonard, B. P.: A Stable and Accurate Convective Modelling Procedure Based on Quadratic Upstream Interpolation, Comput. Method. Appl. M., 19, 59–98, https://doi.org/10.1016/0045-7825(79)90034-3, 1979. a
Lyard, F. H., Allain, D. J., Cancet, M., Carrère, L., and Picot, N.: FES2014 global ocean tide atlas: design and performance, Ocean Sci., 17, 615–649, https://doi.org/10.5194/os-17-615-2021, 2021. a
Madec, G., Bourdallé-Badie, R., Chanut, J., Clementi, E., Coward, A., Ethé, C., Iovino, D., Lea, D., Lévy, C., Lovato, T., Martin, N., Masson, S., Mocavero, S., Rousset, C., Storkey, D., Müeller, S., Nurser, G., Bell, M., Samson, G., Mathiot, P., Mele, F., and Moulin, A.: NEMO Ocean Engine, Tech. Rep., Zenodo, https://doi.org/10.5281/zenodo.1472492, 2022. a
Marchesiello, P., Debreu, L., and Couvelard, X.: Spurious Diapycnal Mixing in Terrain-Following Coordinate Models: The Problem and a Solution, Ocean Model., 26, 156–169, https://doi.org/10.1016/j.ocemod.2008.09.004, 2009. a, b
Megann, A.: Estimating the Numerical Diapycnal Mixing in an Eddy-Permitting Ocean Model, Ocean Model., 121, 19–33, https://doi.org/10.1016/j.ocemod.2017.11.001, 2018. a, b
Megann, A.: Quantifying Numerical Mixing in a Tidally Forced Global Eddy-Permitting Ocean Model, Ocean Model., 188, 102329, https://doi.org/10.1016/j.ocemod.2024.102329, 2024. a
Megann, A., Chanut, J., and Storkey, D.: Assessment of the z∼ Time-Filtered Arbitrary Lagrangian-Eulerian Coordinate in a Global Eddy-Permitting Ocean Model, J. Adv. Model. Earth Sy., 14, e2022MS003056, https://doi.org/10.1029/2022MS003056, 2022. a, b
Meredith, M. and Naveira Garabato, A.: Ocean Mixing, Elsevier, ISBN 978-0-12-821512-8, https://doi.org/10.1016/C2019-0-03674-6, 2022. a
Nagai, T. and Hibiya, T.: Combined Effects of Tidal Mixing in Narrow Straits and the Ekman Transport on the Sea Surface Temperature Cooling in the Southern Indonesian Seas, J. Geophys. Res.-Oceans, 125, e2020JC016314, https://doi.org/10.1029/2020JC016314, 2020. a
Nagai, T., Hibiya, T., and Syamsudin, F.: Direct Estimates of Turbulent Mixing in the Indonesian Archipelago and Its Role in the Transformation of the Indonesian Throughflow Waters, Geophys. Res. Lett., 48, e2020GL091731, https://doi.org/10.1029/2020GL091731, 2021. a
Nguyen-Duy, T., Ayoub, N. K., Marsaleix, P., Toublanc, F., De Mey-Frémaux, P., Piton, V., Herrmann, M., Duhaut, T., Tran, M. C., and Ngo-Duc, T.: Variability of the Red River Plume in the Gulf of Tonkin as Revealed by Numerical Modeling and Clustering Analysis, Front. Mar. Sci., 8, 772139, https://doi.org/10.3389/fmars.2021.772139, 2021. a, b
Niwa, Y. and Hibiya, T.: Estimation of Baroclinic Tide Energy Available for Deep Ocean Mixing Based on Three-Dimensional Global Numerical Simulations, J. Oceanogr., 67, 493–502, https://doi.org/10.1007/s10872-011-0052-1, 2011. a
Nugroho, D.: La Marée Dans Un Modèle de Circulation Générale Dans Les Mers Indonésiennes, PhD thesis, Université de Toulouse, Université Toulouse III – Paul Sabatier, 2017. a
Nugroho, D., Koch-Larrouy, A., Gaspar, P., Lyard, F., Reffray, G., and Tranchant, B.: Modelling Explicit Tides in the Indonesian Seas: An Important Process for Surface Sea Water Properties, Mar. Pollut. Bull., 131, 7–18, https://doi.org/10.1016/j.marpolbul.2017.06.033, 2018. a, b
Piton, V., Herrmann, M., Marsaleix, P., Duhaut, T., Ngoc, T. B., Tran, M. C., Shearman, K., and Ouillon, S.: Influence of Winds, Geostrophy and Typhoons on the Seasonal Variability of the Circulation in the Gulf of Tonkin: A High-Resolution 3D Regional Modeling Study, Regional Studies in Marine Science, 45, 101849, https://doi.org/10.1016/j.rsma.2021.101849, 2021. a
Polzin, K. L., Toole, J. M., Ledwell, J. R., and Schmitt, R. W.: Spatial Variability of Turbulent Mixing in the Abyssal Ocean, Science, 276, 93–96, https://doi.org/10.1126/science.276.5309.93, 1997. a
Purwandana, A., Cuypers, Y., Bouruet-Aubertot, P., Nagai, T., Hibiya, T., and Atmadipoera, A. S.: Spatial Structure of Turbulent Mixing Inferred from Historical CTD Datasets in the Indonesian Seas, Prog. Oceanogr., 184, 102312, https://doi.org/10.1016/j.pocean.2020.102312, 2020. a
Ray, R. D. and Susanto, R. D.: Tidal Mixing Signatures in the Indonesian Seas from High-Resolution Sea Surface Temperature Data, Geophys. Res. Lett., 43, 8115–8123, https://doi.org/10.1002/2016GL069485, 2016. a
Rougier, N. P.: Scientific Visualization: Python + Matplotlib, https://hal.science/hal-03427242v1 (last access: 19 August 2024), 2021. a
Sanderson, B. G.: Order and Resolution for Computational Ocean Dynamics, J. Phys. Oceanogr., 28, 1271–1286, https://doi.org/10.1175/1520-0485(1998)028<1271:OARFCO>2.0.CO;2, 1998. a
Sasaki, H., Kida, S., Furue, R., Nonaka, M., and Masumoto, Y.: An Increase of the Indonesian Throughflow by Internal Tidal Mixing in a High-Resolution Quasi-Global Ocean Simulation, Geophys. Res. Lett., 45, 8416–8424, https://doi.org/10.1029/2018GL078040, 2018. a
Siegelman, L., Klein, P., Rivière, P., Thompson, A. F., Torres, H. S., Flexas, M., and Menemenlis, D.: Enhanced Upward Heat Transport at Deep Submesoscale Ocean Fronts, Nat. Geosci., 13, 50–55, https://doi.org/10.1038/s41561-019-0489-1, 2020. a
Soufflet, Y., Marchesiello, P., Lemarié, F., Jouanno, J., Capet, X., Debreu, L., and Benshila, R.: On Effective Resolution in Ocean Models, Ocean Model., 98, 36–50, https://doi.org/10.1016/j.ocemod.2015.12.004, 2016. a, b
Sprintall, J., Gordon, A. L., Wijffels, S. E., Feng, M., Hu, S., Koch-Larrouy, A., Phillips, H., Nugroho, D., Napitu, A., Pujiana, K., Susanto, R. D., Sloyan, B., Peña-Molino, B., Yuan, D., Riama, N. F., Siswanto, S., Kuswardani, A., Arifin, Z., Wahyudi, A. J., Zhou, H., Nagai, T., Ansong, J. K., Bourdalle-Badié, R., Chanut, J., Lyard, F., Arbic, B. K., Ramdhani, A., and Setiawan, A.: Detecting Change in the Indonesian Seas, Front. Mar. Sci., 6, 257, https://doi.org/10.3389/fmars.2019.00257, 2019. a, b, c
Susanto, R. D. and Ray, R. D.: Seasonal and Interannual Variability of Tidal Mixing Signatures in Indonesian Seas from High-Resolution Sea Surface Temperature, Remote Sensing, 14, 1934, https://doi.org/10.3390/rs14081934, 2022. a
Thakur, R., Arbic, B. K., Menemenlis, D., Momeni, K., Pan, Y., Peltier, W. R., Skitka, J., Alford, M. H., and Ma, Y.: Impact of Vertical Mixing Parameterizations on Internal Gravity Wave Spectra in Regional Ocean Models, Geophys. Res. Lett., 49, e2022GL099614, https://doi.org/10.1029/2022GL099614, 2022. a
To Duy, T., Herrmann, M., Estournel, C., Marsaleix, P., Duhaut, T., Bui Hong, L., and Trinh Bich, N.: The role of wind, mesoscale dynamics, and coastal circulation in the interannual variability of the South Vietnam Upwelling, South China Sea – answers from a high-resolution ocean model, Ocean Sci., 18, 1131–1161, https://doi.org/10.5194/os-18-1131-2022, 2022. a, b
Tranchant, B., Reffray, G., Greiner, E., Nugroho, D., Koch-Larrouy, A., and Gaspar, P.: Evaluation of an operational ocean model configuration at ° spatial resolution for the Indonesian seas (NEMO2.3/INDO12) – Part 1: Ocean physics, Geosci. Model Dev., 9, 1037–1064, https://doi.org/10.5194/gmd-9-1037-2016, 2016. a, b
Trinh, N. B., Herrmann, M., Ulses, C., Marsaleix, P., Duhaut, T., To Duy, T., Estournel, C., and Shearman, R. K.: New insights into the South China Sea throughflow and water budget seasonal cycle: evaluation and analysis of a high-resolution configuration of the ocean model SYMPHONIE version 2.4, Geosci. Model Dev., 17, 1831–1867, https://doi.org/10.5194/gmd-17-1831-2024, 2024. a
Webb, D. J., de Cuevas, B. A., and Richmond, C. S.: Improved Advection Schemes for Ocean Models, J. Atmos. Ocean. Tech., 15, 1171–1187, https://doi.org/10.1175/1520-0426(1998)015<1171:IASFOM>2.0.CO;2, 1998. a, b, c
Winther, N. G., Morel, Y. G., and Evensen, G.: Efficiency of High Order Numerical Schemes for Momentum Advection, J. Marine Syst., 67, 31–46, https://doi.org/10.1016/j.jmarsys.2006.08.004, 2007. a, b
Wong, A. P. S., Wijffels, S. E., Riser, S. C., Pouliquen, S., Hosoda, S., Roemmich, D., Gilson, J., Johnson, G. C., Martini, K., Murphy, D. J., Scanderbeg, M., Bhaskar, T. V. S. U., Buck, J. J. H., Merceur, F., Carval, T., Maze, G., Cabanes, C., André, X., Poffa, N., Yashayaev, I., Barker, P. M., Guinehut, S., Belbéoch, M., Ignaszewski, M., Baringer, M. O., Schmid, C., Lyman, J. M., McTaggart, K. E., Purkey, S. G., Zilberman, N., Alkire, M. B., Swift, D., Owens, W. B., Jayne, S. R., Hersh, C., Robbins, P., West-Mack, D., Bahr, F., Yoshida, S., Sutton, P. J. H., Cancouët, R., Coatanoan, C., Dobbler, D., Juan, A. G., Gourrion, J., Kolodziejczyk, N., Bernard, V., Bourlès, B., Claustre, H., D'Ortenzio, F., Le Reste, S., Le Traon, P.-Y., Rannou, J.-P., Saout-Grit, C., Speich, S., Thierry, V., Verbrugge, N., Angel-Benavides, I. M., Klein, B., Notarstefano, G., Poulain, P.-M., Vélez-Belchí, P., Suga, T., Ando, K., Iwasaska, N., Kobayashi, T., Masuda, S., Oka, E., Sato, K., Nakamura, T., Sato, K., Takatsuki, Y., Yoshida, T., Cowley, R., Lovell, J. L., Oke, P. R., van Wijk, E. M., Carse, F., Donnelly, M., Gould, W. J., Gowers, K., King, B. A., Loch, S. G., Mowat, M., Turton, J., Rama Rao, E. P., Ravichandran, M., Freeland, H. J., Gaboury, I., Gilbert, D., Greenan, B. J. W., Ouellet, M., Ross, T., Tran, A., Dong, M., Liu, Z., Xu, J., Kang, K., Jo, H., Kim, S.-D., and Park, H.-M.: Argo Data 1999–2019: Two Million Temperature-Salinity Profiles and Subsurface Velocity Observations From a Global Array of Profiling Floats, Front. Mar. Sci., 7, 700, https://doi.org/10.3389/fmars.2020.00700, 2020. a
Wunsch, C. and Ferrari, R.: Vertical Mixing, Energy, and the General Circulation of the Oceans, Ann. Rev. Fluid Mech., 36, 281–314, https://doi.org/10.1146/annurev.fluid.36.050802.122121, 2004. a
Zalesak, S. T.: Fully Multidimensional Flux-Corrected Transport Algorithms for Fluids, J. Comput. Phys., 31, 335–362, https://doi.org/10.1016/0021-9991(79)90051-2, 1979. a
Zaron, E. D.: Baroclinic Tidal Sea Level from Exact-Repeat Mission Altimetry, J. Phys. Oceanogr., 49, 193–210, https://doi.org/10.1175/JPO-D-18-0127.1, 2019. a
Short summary
Mixing is a crucial aspect of the ocean, but its accurate representation in computer simulations is made challenging by errors that result in unwanted mixing, compromising simulation realism. Here we illustrate the spurious effect that tides can have on simulations of south-east Asia. Although they play an important role in determining the state of the ocean, they can increase numerical errors and make simulation outputs less realistic. We also provide insights into how to reduce these errors.
Mixing is a crucial aspect of the ocean, but its accurate representation in computer simulations...