Articles | Volume 17, issue 22
https://doi.org/10.5194/gmd-17-8469-2024
© 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-17-8469-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
Laoshan Laboratory, Qingdao, China
Xi Wang
Aerospace NewSky Technology Co., Ltd., Beijing, China
Hailong Liu
CORRESPONDING AUTHOR
Laoshan Laboratory, Qingdao, China
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Pengfei Lin
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Jiangfeng Yu
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Zipeng Yu
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Junlin Wei
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Peng Cheng Laboratory, Shenzhen, China
Xiang Han
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Related authors
Kai Xu, Maoxue Yu, Jiangfeng Yu, Jingwei Xie, Xiang Han, Jiaying Song, Mingyao Geng, Jinrong Jiang, Hailong Liu, Pengfei Wang, and Pengfei Lin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2231, https://doi.org/10.5194/egusphere-2025-2231, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
swLICOM represents a significant advancement in kilometer-scale resolution ocean general circulation models on heterogeneous computing architectures. Our optimization efforts addressed a series of challenges that are particularly crucial for high-resolution modeling. We use swLICOM with a horizontal resolution of 2 km to conduct a short-term simulation test. The 2-km resolution global simulation shows the high capacity of swLICOM to capture the oceanic meso- to submesoscale processes.
Kai Xu, Maoxue Yu, Jiangfeng Yu, Jingwei Xie, Xiang Han, Jiaying Song, Mingyao Geng, Jinrong Jiang, Hailong Liu, Pengfei Wang, and Pengfei Lin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2231, https://doi.org/10.5194/egusphere-2025-2231, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
swLICOM represents a significant advancement in kilometer-scale resolution ocean general circulation models on heterogeneous computing architectures. Our optimization efforts addressed a series of challenges that are particularly crucial for high-resolution modeling. We use swLICOM with a horizontal resolution of 2 km to conduct a short-term simulation test. The 2-km resolution global simulation shows the high capacity of swLICOM to capture the oceanic meso- to submesoscale processes.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
Short summary
Short summary
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Pengfei Wang, Jinrong Jiang, Pengfei Lin, Mengrong Ding, Junlin Wei, Feng Zhang, Lian Zhao, Yiwen Li, Zipeng Yu, Weipeng Zheng, Yongqiang Yu, Xuebin Chi, and Hailong Liu
Geosci. Model Dev., 14, 2781–2799, https://doi.org/10.5194/gmd-14-2781-2021, https://doi.org/10.5194/gmd-14-2781-2021, 2021
Short summary
Short summary
Global ocean general circulation models are a fundamental tool for oceanography research, ocean forecast, and climate change research. The increasing resolution will greatly improve simulations of the models, but it also demands much more computing resources. In this study, we have ported an ocean general circulation model to a heterogeneous computing system and have developed a 3–5 km model version. A 14-year integration has been conducted and the preliminary results have been evaluated.
Eric P. Chassignet, Stephen G. Yeager, Baylor Fox-Kemper, Alexandra Bozec, Frederic Castruccio, Gokhan Danabasoglu, Christopher Horvat, Who M. Kim, Nikolay Koldunov, Yiwen Li, Pengfei Lin, Hailong Liu, Dmitry V. Sein, Dmitry Sidorenko, Qiang Wang, and Xiaobiao Xu
Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, https://doi.org/10.5194/gmd-13-4595-2020, 2020
Short summary
Short summary
This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations to assess the robustness of climate-relevant improvements in ocean simulations associated with moving from coarse (∼1°) to eddy-resolving (∼0.1°) horizontal resolutions. Despite significant improvements, greatly enhanced horizontal resolution does not deliver unambiguous bias reduction in all regions for all models.
Cited articles
Abernathey, R. and Cessi, P.: Topographic Enhancement of Eddy Efficiency in Baroclinic Equilibration, J. Phys. Oceanogr., 44, 2107–2126, https://doi.org/10.1175/JPO-D-14-0014.1, 2014. a, b, c, d
Abernathey, R., Marshall, J., and Ferreira, D.: The dependence of Southern Ocean meridional overturning on wind stress, J. Phys. Oceanogr., 41, 2261–2278, https://doi.org/10.1175/JPO-D-11-023.1, 2011. a, b, c, d
Adcroft, A., Campin, J.-M., Doddridge, E., and Dutkiewicz, S.: MITgcm Documentation, Release checkpoint67v-18-g1e25bc255, Zenodo, https://doi.org/10.5281/zenodo.1409237, 2021. a
Alfonsi, G.: On Direct Numerical Simulation of Turbulent Flows, Appl. Mech. Rev., 64, 020802, https://doi.org/10.1115/1.4005282, 2011. a
Artana, C., Ferrari, R., Bricaud, C., Lellouche, J. M., Garric, G., Sennéchael, N., Lee, J. H., Park, Y. H., and Provost, C.: Twenty-five years of Mercator ocean reanalysis GLORYS12 at Drake Passage: Velocity assessment and total volume transport, Adv. Space Res., 68, 447–466, https://doi.org/10.1016/j.asr.2019.11.033, 2021a. a
Artana, C., Provost, C., Poli, L., Ferrari, R., and Lellouche, J.-M.: Revisiting the Malvinas Current Upper Circulation and Water Masses Using a High-Resolution Ocean Reanalysis, J. Geophys. Res.-Oceans, 126, e2021JC017271, https://doi.org/10.1029/2021JC017271, 2021b. a
Bachman, S. D.: The GM plus E closure: A framework for coupling backscatter with the Gent and McWilliams parameterization, Ocean Model., 136, 85–106, https://doi.org/10.1016/j.ocemod.2019.02.006, 2019. a
Bachman, S. D., Fox-Kemper, B., and Bryan, F. O.: A tracer-based inversion method for diagnosing eddy-induced diffusivity and advection, Ocean Model., 86, 1–14, https://doi.org/10.1016/j.ocemod.2014.11.006, 2015. a, b
Bachman, S. D., Fox-Kemper, B., and Bryan, F. O.: A Diagnosis of Anisotropic Eddy Diffusion From a High-Resolution Global Ocean Model, J. Adv. Model. Earth Sy., 12, e2019MS001904, https://doi.org/10.1029/2019MS001904, 2020. a, b
Blumen, W.: Uniform potential vorticity flow: Part I. Theory of wave interactions and two-dimensional turbulence, J. Atmos. Sci., 35, 774–783, 1978. a
Bolton, T. and Zanna, L.: Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization, J. Adv. Model. Earth Sy., 11, 376–399, https://doi.org/10.1029/2018MS001472, 2019. a
Boyd, J. P.: The Energy Spectrum of Fronts: Time Evolution of Shocks in Burgers‚ Equation, J. Atmos. Sci., 49, 128–139, 1992. a
Busecke, J. J. and Abernathey, R. P.: Ocean mesoscale mixing linked to climate variability, Science Advances, 5, 5014, https://doi.org/10.1126/sciadv.aav5014, 2019. a
Callies, J. and Ferrari, R.: Interpreting energy and tracer spectra of upper-ocean turbulence in the submesoscale range (1–200 km), J. Phys. Oceanogr., 43, 2456–2474, https://doi.org/10.1175/JPO-D-13-063.1, 2013. a
Capó, E., McWilliams, J. C., Mason, E., and Orfila, A.: Intermittent frontogenesis in the Alboran Sea, J. Phys. Oceanogr., 51, 1417–1439, 2021. a
Cessi, P.: An Energy-Constrained Parameterization of Eddy Buoyancy Flux, J. Phys. Oceanogr., 38, 1807–1819, https://doi.org/10.1175/2007JPO3812.1, 2007. a
Chapman, C. C., Lea, M.-A., Meyer, A., Sallée, J.-B., and Hindell, M.: Defining Southern Ocean fronts and their influence on biological and physical processes in a changing climate, Nat. Clim. Change, 10, 209–219, https://doi.org/10.1038/s41558-020-0705-4, 2020. a
Charney, J. G.: Geostrophic turbulence, J. Atmos. Sci., 28, 1087–1095, 1971. a
Chassignet, E. and Xu, X.: Impact of horizontal resolution ( to ) on Gulf Stream separation, penetration, and variability, J. Phys. Oceanogr., 47, 1999–2021, 2017. a
Chaudhuri, A., Ponte, R., Forget, G., and Heimbach, P.: A comparison of atmospheric reanalysis surface products over the ocean and implications for uncertainties in air-sea boundary forcing, J. Climate, 26, 153–170, 2013. a
Chelton, D., Deszoeke, R., Schlax, M. G., Naggar, K., and Siwertz, N.: Geographical Variability of the First Baroclinic Rossby Radius of Deformation, J. Phys. Oceanogr, 28, 433–460, https://doi.org/10.1175/1520-0485(1998)028<0433:GVOTFB>2.0.CO;2, 1998. a, b
Chelton, D. B., Schlax, M. G., and Samelson, R. M.: Global observations of nonlinear mesoscale eddies, Prog. Oceanogr., 91, 167–216, https://doi.org/10.1016/j.pocean.2011.01.002, 2011. a
Chen, C., Kamenkovich, I., and Berloff, P.: On the dynamics of flows induced by topographic ridges, J. Phys. Oceanogr., 45, 927–940, https://doi.org/10.1175/JPO-D-14-0143.1, 2015. a
Cunningham, S. A., Alderson, S. G., King, B. A., and Brandon, M. A.: Transport and variability of the Antarctic circumpolar current in drake passage, J. Geophys. Res.-Oceans, 108, 8084, https://doi.org/10.1029/2001JC001147, 2003. a
Dong, C., McWilliams, J. C., Liu, Y., and Chen, D.: Global heat and salt transports by eddy movement, Nat. Commun., 5, 3294, https://doi.org/10.1038/ncomms4294, 2014. a
Donohue, K. A., Tracey, K. L., Watts, D. R., Chidichimo, M. P., and Chereskin, T. K.: Mean Antarctic Circumpolar Current transport measured in Drake Passage, Geophys. Res. Lett., 43, 11760–11767, https://doi.org/10.1002/2016GL070319, 2016. a
Eden, C. and Greatbatch, R.: Towards a mesoscale eddy closure, Ocean Model., 20, 223–239, 2008. a
Ferrari, R. and Wunsch, C.: Ocean circulation kinetic energy: Reservoirs, sources, and sinks, Annu. Rev. Fluid Mech., 41, 253–282, https://doi.org/10.1146/annurev.fluid.40.111406.102139, 2009. a
Fox-Kemper, B. and Ferrari, R.: Parameterization of mixed layer eddies. Part II: Prognosis and impact, J. Phys. Oceanogr., 38, 1166–1179, https://doi.org/10.1175/2007JPO3788.1, 2008. a
Frezat, H., Le Sommer, J., Fablet, R., Balarac, G., and Lguensat, R.: A posteriori learning for quasi-geostrophic turbulence parametrization, J. Adv. Model. Earth Sy., 14, e2022MS003124, https://doi.org/10.1029/2022MS003124, 2022. a
Fu, L.-L. and Morrow, R.: Remote sensing of the global ocean circulation, in: International Geophysics, vol. 103, 83–111, Elsevier, https://doi.org/10.1016/B978-0-12-391851-2.00004-0, 2013. a
Gent, P. R. and McWilliams, J. C.: Isopycnal Mixing in Ocean Circulation Models, J. Phys. Oceanogr., 20, 150–155, https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2, 1990. a
Gent, P. R., Willebrand, J., McDougall T., and McWilliams, J.: Parameterizing Eddy-Induced Tracer Transports in Ocean Circulation Models, J. Phys. Oceanogr, 25, 463–474, https://doi.org/10.1175/1520-0485(1995)025<0463:PEITTI>2.0.CO;2, 1995. a
Germano, M.: Turbulence-The filtering approach, J. Fluid Mech., 238, 325–336, https://doi.org/10.1017/S0022112092001733, 1992. a
Germano, M., Piomelli, U., Moin, P., and Cabot, W.: A dynamic subgrid-scale eddy viscosity model, Phys. Fluids, 3, 1760–1765, https://doi.org/10.1063/1.857955, 1991. a
Graham, J. and Ringler, T.: A framework for the evaluation of turbulence closures used in mesoscale ocean large-eddy simulations, Ocean Model., 65, 25–39, https://doi.org/10.1016/j.ocemod.2013.01.004, 2013. a, b, c
Griffies, S., Gnanadesikan, A., Pacanowski, R., Larichev, V., and Smith, R.: Isoneutral Diffusion in a z-Coordinate Ocean Model, J. Phys. Oceanogr., 28, 805–830, https://doi.org/10.1175/1520-0485(1998)028<0805:IDIAZC>2.0.CO;2, 1998. a
Guillaumin, A. P. and Zanna, L.: Stochastic-deep learning parameterization of ocean momentum forcing, J. Adv. Model. Earth Sy., 13, e2021MS002534, https://doi.org/10.1029/2021MS002534, 2021. a
Gula, J., Molemaker, M. J., and McWilliams, J. C.: Submesoscale cold filaments in the Gulf Stream, J. Phys. Oceanogr., 44, 2617–2643, https://doi.org/10.1175/JPO-D-14-0029.1, 2014. a, b
Gula, J., Taylor, J., Shcherbina, A., and Mahadevan, A.: Submesoscale processes and mixing, in: Ocean mixing, Elsevier, 181–214, https://doi.org/10.1016/B978-0-12-821512-8.00015-3, 2022. a
Haigh, M. and Berloff, P.: On co-existing diffusive and anti-diffusive tracer transport by oceanic mesoscale eddies, Ocean Model., 168, 101909, https://doi.org/10.1016/j.ocemod.2021.101909, 2021. a
Hallberg, R.: Using a resolution function to regulate parameterizations of oceanic mesoscale eddy effects, Ocean Model., 72, 92–103, https://doi.org/10.1016/j.ocemod.2013.08.007, 2013. a, b
Held, I. M., Pierrehumbert, R. T., Garner, S. T., and Swanson, K. L.: Surface quasi-geostrophic dynamics, J. Fluid Mech., 282, 1–20, https://doi.org/10.1017/S0022112095000012, 1995. a
Jansen, M. F. and Held, I. M.: Parameterizing subgrid-scale eddy effects using energetically consistent backscatter, Ocean Model., 80, 36–48, https://doi.org/10.1016/j.ocemod.2014.06.002, 2014. a
Jansen, M. F., Adcroft, A., Khani, S., and Kong, H.: Toward an energetically consistent, resolution aware parameterization of ocean mesoscale eddies, J. Adv. Model. Earth Sy., 11, 2844–2860, https://doi.org/10.1029/2019MS001750, 2019. a
Kaneda, Y. and Ishihara, T.: High-resolution direct numerical simulation of turbulence, J. Turbul., 7, https://doi.org/10.1080/14685240500256099, 2006. a
Khani, S. and Dawson, C. N.: A Gradient Based Subgrid-Scale Parameterization for Ocean Mesoscale Eddies, J. Adv. Model. Earth Sy., 15, e2022MS003356, https://doi.org/10.1029/2022MS003356, 2023. a, b, c
Khani, S. and Waite, M.: An anisotropic subgrid-scale parameterization for large-eddy simulations of stratified turbulence, Mon. Weather Rev., 148, 4299–4311, 2020. a
Khatri, H. and Berloff, P.: A mechanism for jet drift over topography, J. Fluid Mech., 845, 392–416, https://doi.org/10.1017/jfm.2018.260, 2018. a
LaCasce, J. H. and Groeskamp, S.: Baroclinic modes over rough bathymetry and the surface deformation radius, J. Phys. Oceanogr., 50, 2835–2847, https://doi.org/10.1175/JPO-D-20-0055.1, 2020. a
Lapeyre, G.: Surface quasi-geostrophy, Fluids, 2, 7, https://doi.org/10.3390/fluids2010007, 2017. a
Large, W., McWilliams, J., and Doney, S.: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization, Rev. Geophys., 32, 363–403, 1994. a
Leith, C.: Stochastic models of chaotic systems, Physica D, 98, 481–491, 1996. a
Leonard, A.: Energy cascade in large-eddy simulations of turbulent fluid flows, Adv. Geophys., 18, 237–248, https://doi.org/10.1016/S0065-2687(08)60464-1, 1975. a
Lu, J. and Speer, K.: Topography, jets, and eddy mixing in the Southern Ocean, J. Mar. Res., 68, 479–502, https://elischolar.library.yale.edu/journal_of_marine_research/276 (last access: 8 April 2024), 2010. a
Lu, J., Wang, F., Liu, H., and Lin, P.: Stationary mesoscale eddies, upgradient eddy fluxes, and the anisotropy of eddy diffusivity, Geophys. Res. Lett., 43, 743–751, https://doi.org/10.1002/2015GL067384, 2016. a
Lutjeharms, J. R. E and Van Ballegooyen, R. C.: Topographic control in the Agulhas Current system, Deep Sea Research Part A. Oceanographic Research Papers, 31, 1321–1337, https://doi.org/10.1016/0198-0149(84)90004-9, 1984. a
Lutjeharms, J. R. E.: Three decades of research on the greater Agulhas Current, Ocean Sci., 3, 129–147, https://doi.org/10.5194/os-3-129-2007, 2007. a
Ma, X., Jing, Z., Chang, P., Liu, X., Montuoro, R., Small, R. J., Bryan, F. O., Greatbatch, R. J., Brandt, P., Wu, D., Lin, X., and Wu, L.: Western boundary currents regulated by interaction between ocean eddies and the atmosphere, Nature, 535, 533–537, https://doi.org/10.1038/nature18640, 2016. a
Mak, J., Maddison, J. R., Marshall, D. P., and Munday, D. R.: Implementation of a geometrically informed and energetically constrained mesoscale eddy parameterization in an ocean circulation model, J. Phys. Oceanogr., 48, 2363–2382, https://doi.org/10.1175/JPO-D-18-0017.1, 2018. a, b
Marques, G. M., Loose, N., Yankovsky, E., Steinberg, J. M., Chang, C.-Y., Bhamidipati, N., Adcroft, A., Fox-Kemper, B., Griffies, S. M., Hallberg, R. W., Jansen, M. F., Khatri, H., and Zanna, L.: NeverWorld2: an idealized model hierarchy to investigate ocean mesoscale eddies across resolutions, Geosci. Model Dev., 15, 6567–6579, https://doi.org/10.5194/gmd-15-6567-2022, 2022. a, b, c, d, e, f, g, h, i
Marshall, J., Adcroft, A., Hill, C., Perelman, L., and Heisey, C.: A finite-volume, incompressible Navier-Stokes model for studies of the ocean on parallel computers, J. Geophys. Res.-Oceans, 102, 5753–5766, https://doi.org/10.1029/96JC02775, 1997. a
Massachusetts Institute of Technology: Massachusetts Institute of Technology general circulation model, Massachusetts Institute of Technology [code], http://mitgcm.org/, last access: 8 April 2024. a
Mazloff, M., Heimbach, P., and Wunsch, C.: An Eddy-Permitting Southern Ocean State Estimate, J. Phys. Oceanogr., 40, 880–899, https://doi.org/10.1175/2009JPO4236.1, 2010a. a
Mazloff, M., Heimbach, P., and Wunsch, C.: Southern Ocean State Estimate solution, Ocean State Estimation at Scripps Institution of Oceanography [data set], http://sose.ucsd.edu/sose_stateestimation_data_05to10.html (last access: 8 April 2024), 2010b. a
McWilliams, J.: Submesoscale currents in the ocean, P. Roy. Soc. A-Math. Phy., 472, 20160117, https://doi.org/10.1098/rspa.2016.0117, 2016. a, b, c
Meneveau, C.: Statistics of turbulence subgrid-scale stresses: Necessary conditions and experimental tests, Phys. Fluids, 6, 815–833, https://doi.org/10.1063/1.868320, 1994. a
Moin, P. and Mahesh, K.: Direct numerical simulation: a tool in turbulence research, Annu. Rev. Fluid Mech., 30, 539–578, https://doi.org/10.1146/annurev.fluid.30.1.539, 1998. a
Moser, R., Haering, S., and Yalla, G.: Statistical Properties of Subgrid-Scale Turbulence Models, Annu. Rev. Fluid Mech., 53, 255–286, https://doi.org/10.1146/annurev-fluid-060420-023735, 2021. a
Orsi, A. H., Whitworth III, T., and Nowlin Jr., W. D.: On the meridional extent and fronts of the Antarctic Circumpolar Current, Deep-Sea Res. Pt. I, 42, 641–673, https://doi.org/10.1016/0967-0637(95)00021-W, 1995. a
Park, Y.-h., Vivier, F., Roquet, F., and Kestenare, E.: Direct observations of the ACC transport across the Kerguelen Plateau, Geophys. Res. Lett., 36, L18603, https://doi.org/10.1029/2009GL039617, 2009. a
Pearson, B., Fox-Kemper, B., Bachman, S., and Bryan, F.: Evaluation of scale-aware subgrid mesoscale eddy models in a global eddy-rich model, Ocean Model., 115, 42–58, https://doi.org/10.1016/j.ocemod.2017.05.007, 2017. a
Perezhogin, P. and Glazunov, A.: Subgrid parameterizations of ocean mesoscale eddies based on Germano decomposition, J. Adv. Model. Earth Sy., 15, e2023MS003771, https://doi.org/10.1029/2023MS003771, 2023. a, b
Pope, S. B.: Turbulent Flows, Meas. Sci. Technol., 12, 11, https://doi.org/10.1088/0957-0233/12/11/705, 2001. a, b, c
Radko, T. and Kamenkovich, I.: On the topographic modulation of large-scale eddying flows, J. Phys. Oceanogr., 47, 2157–2172, https://doi.org/10.1175/JPO-D-17-0024.1, 2017. a
Redi, M.: Oceanic Isopycnal Mixing by Coordinate Rotation, J. Phys. Oceanogr., 12, 1154–1158, https://doi.org/10.1175/1520-0485(1982)012<1154:OIMBCR>2.0.CO;2, 1982. a
Rhines, P. B.: Jets and Orography: Idealized Experiments with Tip Jets and Lighthill Blocking, J. Atmos. Sci., 64, 3627, https://doi.org/10.1175/JAS4008.1, 2006. a
Schubert, R., Gula, J., Greatbatch, R. J., Baschek, B., and Biastoch, A.: The submesoscale kinetic energy cascade: Mesoscale absorption of submesoscale mixed layer eddies and frontal downscale fluxes, J. Phys. Oceanogr., 50, 2573–2589, https://doi.org/10.1175/JPO-D-19-0311.1, 2020. a, b, c, d
Siedler, G., Griffies, S. M., Gould, J., and Church, J. A.: Ocean circulation and climate: A 21st century perspective, second edn., International Geophysics Series, 103, Academic Press, Oxford, Amsterdam, 868 pp., ISBN 978-0-12-391851-2, 2013. a
Smith, R. and Gent, P.: Anisotropic Gent-McWilliams Parameterization for Ocean Models, J. Phys. Oceanogr., 34, 2541, https://doi.org/10.1175/JPO2613.1, 2004. a
Speich, S., Lutjeharms, J. R. E., Penven, P., and Blanke, B.: Role of bathymetry in Agulhas Current configuration and behaviour, Geophys. Res. Lett., 33, L23611, https://doi.org/10.1029/2006GL027157, 2006. a
Srinivasan, K., Chekroun, M., and McWilliams, J.: Turbulence Closure With Small, Local Neural Networks: Forced Two-Dimensional and β-Plane Flows, J. Adv. Model. Earth Sy., 16, e2023MS003795, https://doi.org/10.1029/2023MS003795, 2024. a
Stammer, D.: On eddy characteristics, eddy transports, and mean flow properties, J. Phys. Oceanogr., 28, 727–739, https://doi.org/10.1175/1520-0485(1998)028<0727:OECETA>2.0.CO;2, 1998. a
Su, Z., Wang, J., Klein, P., Thompson, A., and Menemenlis, D.: Ocean submesoscales as a key component of the global heat budget, Nat. Commun., 9, 775, https://doi.org/10.1038/s41467-018-02983-w, 2018. a
Taylor, J. R. and Thompson, A. F.: Submesoscale dynamics in the upper ocean, Annu. Rev. Fluid Mech., 55, 103–127, https://doi.org/10.1146/annurev-fluid-031422-095147, 2023. a
Thiry, L., Li, L., Roullet, G., and Mémin, E.: MQGeometry-1.0: a multi-layer quasi-geostrophic solver on non-rectangular geometries, Geosci. Model Dev., 17, 1749–1764, https://doi.org/10.5194/gmd-17-1749-2024, 2024. a
Thomas, L. N., Tandon, A., and Mahadevan, A.: Submesoscale Processes and Dynamics, vol. 177, in: Ocean Modeling in an Eddying Regime, American Geophysical Union (AGU), 17–38, https://doi.org/10.1029/177GM04, 2008. a
Thompson, A. F.: Jet Formation and Evolution in Baroclinic Turbulence with Simple Topography, J. Phys. Oceanogr., 40, 257–278, https://doi.org/10.1175/2009JPO4218.1, 2010. a
Thompson, A. F. and Naveira-Garabato, A. C.: Equilibration of the Antarctic Circumpolar Current by Standing Meanders, J. Phys. Oceanogr., 44, 1811–1828, https://doi.org/10.1175/JPO-D-13-0163.1, 2014. a, b
Thompson, A. F. and Sallée, J.-P.: Jets and Topography: Jet Transitions and the Impact on Transport in the Antarctic Circumpolar Current, J. Phys. Oceanogr., 42, 956–972, https://doi.org/10.1175/JPO-D-11-0135.1, 2012. a
Uchida, T., Le Sommer, J., Stern, C., Abernathey, R. P., Holdgraf, C., Albert, A., Brodeau, L., Chassignet, E. P., Xu, X., Gula, J., Roullet, G., Koldunov, N., Danilov, S., Wang, Q., Menemenlis, D., Bricaud, C., Arbic, B. K., Shriver, J. F., Qiao, F., Xiao, B., Biastoch, A., Schubert, R., Fox-Kemper, B., Dewar, W. K., and Wallcraft, A.: Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models, Geosci. Model Dev., 15, 5829–5856, https://doi.org/10.5194/gmd-15-5829-2022, 2022. a
Vallis, G.: Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-scale Circulation, 2nd edn., Cambridge University Press, https://doi.org/10.1017/9781107588417, 2017. a, b, c
Verdy, A. and Mazloff, M.: A data assimilating model for estimating Southern Ocean biogeochemistry, J. Geophys. Res.-Oceans, 122, 6968–6988, https://doi.org/10.1002/2016JC012650, 2017a. a
Verdy, A. and Mazloff, M.: Biogeochemical Southern Ocean State Estimate solution, [data set], http://sose.ucsd.edu/BSOSE6_iter106_solution.html (last access: 8 April 2024), 2017b. a
Visbeck, M., Marshall, J., Haine, T., and Spall, M.: Specification of Eddy Transfer Coefficients in Coarse-Resolution Ocean Circulation Models, J. Phys. Oceanogr., 27, 381–402, https://doi.org/10.1175/1520-0485(1997)027<0381:SOETCI>2.0.CO;2, 1997. a
Wei, H. and Wang, Y.: Full-depth scalings for isopycnal eddy mixing across continental slopes under upwelling-favorable winds, J. Adv. Model. Earth Sy., 13, e2021MS002498, https://doi.org/10.1029/2021MS002498, 2021. a
Wei, H., Wang, Y., and Mak, J.: Parameterizing Eddy Buoyancy Fluxes Across Prograde Shelf/Slope Fronts Using a Slope-Aware GEOMETRIC Closure, J. Phys. Oceanogr., 54, 359–377, https://doi.org/10.1175/JPO-D-23-0152.1, 2024a. a
Wei, J., Han, X., Yu, J., Jiang, J., Liu, H., Lin, P., Yu, M., Xu, K., Zhao, L., Wang, P., Zheng, W., Xie, J., Zhou, Y., Zhang, T., Zhang, F., Zhang, Y., Yu, Y., Wang, Y., Bai, Y., Li, C., Yu, Z., Deng, H., Li, Y., and Chi, X.: A Performance-Portable Kilometer-Scale Global Ocean Model on ORISE and New Sunway Heterogeneous Supercomputers, in: 2024 SC24: International Conference for High Performance Computing, Networking, Storage and Analysis SC, pp. 24–35, IEEE Computer Society, https://www.computer.org/csdl/proceedings-article/sc/2024/529100a024/21HUV4y76OA, last access: 27 November 2024b. a, b
Xie, J.: Model and Dataset for ISOM 1.0: A fully mesoscale-resolving idealized Southern Ocean model, [DS/OL], V2, Science Data Bank [code and data set], https://doi.org/10.57760/sciencedb.11634, 2024. a
Xu, X., Chassignet, E. P., Firing, Y. L., and Donohue, K.: Antarctic Circumpolar Current Transport Through Drake Passage: What Can We Learn From Comparing High‐Resolution Model Results to Observations?, J. Geophys. Res.-Oceans, 125, e2020JC016365, https://doi.org/10.1029/2020JC016365, 2020. a
Yang, H., Chen, Z., Sun, S., Li, M., Cai, W., Wu, L., Cai, J., Sun, B., Ma, K., Ma, X., Jing, Z., and Gan, B.: Observations Reveal Intense Air-Sea Exchanges Over Submesoscale Ocean Front, Geophys. Res. Lett., 51, e2023GL106840, https://doi.org/10.1029/2023GL106840, 2024. a
Yankovsky, E., Bachman, S., Smith, K. S., and Zanna, L.: Vertical Structure and Energetic Constraints for a Backscatter Parameterization of Ocean Mesoscale Eddies, J. Adv. Model. Earth Sy., 16, e2023MS004093, https://doi.org/10.1029/2023MS004093, 2024. a
Youngs, M., Thompson, A., Lazar, A., and Richards, K.: ACC Meanders, Energy Transfer, and Mixed Barotropic-Baroclinic Instability, J. Phys. Oceanogr., 47, 1291–1305, https://doi.org/10.1175/JPO-D-16-0160.1, 2017. a, b, c
Zanna, L. and Bolton, T.: Data-driven equation discovery of ocean mesoscale closures, Geophys. Res. Lett., 47, e2020GL088376, https://doi.org/10.1029/2020GL088376, 2020. a
Zhai, X., Johnson, H. L., and Marshall, D. P.: Significant sink of ocean-eddy energy near western boundaries, Nat. Geosci., 3, 608–612, https://doi.org/10.1038/NGEO943, 2010. a
Short summary
We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should...