Intrinsic Variability of Sea Level from Global Ocean Simulations: Spatiotemporal Scales

Guillaume Sérazin CNRS, LGGE (UMR5183), Grenoble, and, Sciences de l’Univers au CERFACS, CERFACS/CNRS, URA1857, Université Paul Sabatier, Toulouse, France

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Thierry Penduff CNRS, Université Grenoble Alpes, LGGE (UMR5183), Grenoble, France

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Sandy Grégorio CNRS, Université Grenoble Alpes, LGGE (UMR5183), Grenoble, France

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Bernard Barnier CNRS, Université Grenoble Alpes, LGGE (UMR5183), Grenoble, France

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Jean-Marc Molines CNRS, Université Grenoble Alpes, LGGE (UMR5183), Grenoble, France

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Laurent Terray Sciences de l’Univers au CERFACS, CERFACS/CNRS, URA1857, Toulouse, France

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Abstract

In high-resolution ocean general circulation models (OGCMs), as in process-oriented models, a substantial amount of interannual to decadal variability is generated spontaneously by oceanic nonlinearities: that is, without any variability in the atmospheric forcing at these time scales. The authors investigate the temporal and spatial scales at which this intrinsic oceanic variability has the strongest imprints on sea level anomalies (SLAs) using a ° global OGCM, by comparing a “hindcast” driven by the full range of atmospheric time scales with its counterpart forced by a repeated climatological atmospheric seasonal cycle. Outputs from both simulations are compared within distinct frequency–wavenumber bins. The fully forced hindcast is shown to reproduce the observed distribution and magnitude of low-frequency SLA variability very accurately. The small-scale (L < 6°) SLA variance is, at all time scales, barely sensitive to atmospheric variability and is almost entirely of intrinsic origin. The high-frequency (mesoscale) part and the low-frequency part of this small-scale variability have almost identical geographical distributions, supporting the hypothesis of a nonlinear temporal inverse cascade spontaneously transferring kinetic energy from high to low frequencies. The large-scale (L > 12°) low-frequency variability is mostly related to the atmospheric variability over most of the global ocean, but it is shown to remain largely intrinsic in three eddy-active regions: the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current (ACC). Compared to its ¼° predecessor, the authors’ ° OGCM is shown to yield a stronger intrinsic SLA variability, at both mesoscale and low frequencies.

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Publisher’s Note: This article was revised on 10 June 2015 to correct a typographical error in the abstract where the wrong inequality was used, and to update the Grégorio et al. (2014) reference throughout the article to Grégorio et al. (2015).

Corresponding author address: Guillaume Sérazin, Laboratoire de Glaciologie et Géophysique de l’Environnement LGGE/CNRS, BP 96, 38402 Saint-Martin d’Heres, Grenoble, France. E-mail: guillaume.serazin@legi.grenoble-inp.fr

Abstract

In high-resolution ocean general circulation models (OGCMs), as in process-oriented models, a substantial amount of interannual to decadal variability is generated spontaneously by oceanic nonlinearities: that is, without any variability in the atmospheric forcing at these time scales. The authors investigate the temporal and spatial scales at which this intrinsic oceanic variability has the strongest imprints on sea level anomalies (SLAs) using a ° global OGCM, by comparing a “hindcast” driven by the full range of atmospheric time scales with its counterpart forced by a repeated climatological atmospheric seasonal cycle. Outputs from both simulations are compared within distinct frequency–wavenumber bins. The fully forced hindcast is shown to reproduce the observed distribution and magnitude of low-frequency SLA variability very accurately. The small-scale (L < 6°) SLA variance is, at all time scales, barely sensitive to atmospheric variability and is almost entirely of intrinsic origin. The high-frequency (mesoscale) part and the low-frequency part of this small-scale variability have almost identical geographical distributions, supporting the hypothesis of a nonlinear temporal inverse cascade spontaneously transferring kinetic energy from high to low frequencies. The large-scale (L > 12°) low-frequency variability is mostly related to the atmospheric variability over most of the global ocean, but it is shown to remain largely intrinsic in three eddy-active regions: the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current (ACC). Compared to its ¼° predecessor, the authors’ ° OGCM is shown to yield a stronger intrinsic SLA variability, at both mesoscale and low frequencies.

Denotes Open Access content.

Publisher’s Note: This article was revised on 10 June 2015 to correct a typographical error in the abstract where the wrong inequality was used, and to update the Grégorio et al. (2014) reference throughout the article to Grégorio et al. (2015).

Corresponding author address: Guillaume Sérazin, Laboratoire de Glaciologie et Géophysique de l’Environnement LGGE/CNRS, BP 96, 38402 Saint-Martin d’Heres, Grenoble, France. E-mail: guillaume.serazin@legi.grenoble-inp.fr
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  • Arbic, B. K., R. B. Scott, G. R. Flierl, A. J. Morten, J. G. Richman, and J. F. Shriver, 2012: Nonlinear cascades of surface oceanic geostrophic kinetic energy in the frequency domain. J. Phys. Oceanogr., 42, 15771600, doi:10.1175/JPO-D-11-0151.1.

    • Search Google Scholar
    • Export Citation
  • Arbic, B. K., M. Müller, J. G. Richman, J. F. Shriver, A. J. Morten, R. B. Scott, G. Sérazin, and T. Penduff, 2014: Geostrophic turbulence in the frequency–wavenumber domain: Eddy-driven low-frequency variability. J. Phys. Oceanogr., 44, 20502069, doi:10.1175/JPO-D-13-054.1.

    • Search Google Scholar
    • Export Citation
  • Barnier, B., and Coauthors, 2006: Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy-permitting resolution. Ocean Dyn., 56 (5–6), 543567, doi:10.1007/s10236-006-0082-1.

    • Search Google Scholar
    • Export Citation
  • Berloff, P. S., A. M. Hogg, and W. Dewar, 2007: The turbulent oscillator: A mechanism of low-frequency variability of the wind-driven ocean gyres. J. Phys. Oceanogr.,37, 2363–2386, doi:10.1175/JPO3118.1.

  • Blanke, B., and P. Delecluse, 1993: Variability of the tropical Atlantic Ocean simulated by a general circulation model with two different mixed-layer physics. J. Phys. Oceanogr., 23, 13631388., doi:10.1175/1520-0485(1993)023<1363:VOTTAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Brachet, S., F. Codron, Y. Feliks, M. Ghil, H. Le Treut, and E. Simonnet, 2012: Atmospheric circulations induced by a midlatitude SST front: A GCM study. J. Climate, 25, 18471853, doi:10.1175/JCLI-D-11-00329.1.

    • Search Google Scholar
    • Export Citation
  • Brodeau, L., B. Barnier, A.-M. Treguier, T. Penduff, and S. Gulev, 2010: An ERA40-based atmospheric forcing for global ocean circulation models. Ocean Modell., 31 (3–4), 88104, doi:10.1016/j.ocemod.2009.10.005.

    • Search Google Scholar
    • Export Citation
  • Bryan, F. O., 2013: Introduction: Ocean modeling—Eddy or not. Ocean Modeling in an Eddying Regime, Geophys. Monogr., Vol. 177, Amer. Geophys. Union, 1–3.

  • Charney, J. G., 1947: The dynamics of long waves in a baroclinic westerly current. J. Meteor., 4, 136162, doi:10.1175/1520-0469(1947)004<0136:TDOLWI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Cleveland, W. S., and S. J. Devlin, 1988: Locally weighted regression: An approach to regression analysis by local fitting. J. Amer. Stat. Assoc., 83, 596610, doi:10.1080/01621459.1988.10478639.

    • Search Google Scholar
    • Export Citation
  • Combes, V., and E. Di Lorenzo, 2007: Intrinsic and forced interannual variability of the Gulf of Alaska mesoscale circulation. Prog. Oceanogr., 75, 266286, doi:10.1016/j.pocean.2007.08.011.

    • Search Google Scholar
    • Export Citation
  • Deshayes, J., and Coauthors, 2013: Oceanic hindcast simulations at high resolution suggest that the Atlantic MOC is bistable. Geophys. Res. Lett., 40, 30693073, doi:10.1002/grl.50534.

    • Search Google Scholar
    • Export Citation
  • Dewar, W. K., 2003: Nonlinear midlatitude ocean adjustment. J. Phys. Oceanogr., 33, 10571082, doi:10.1175/1520-0485(2003)033<1057:NMOA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Dijkstra, H. A., and M. Ghil, 2005: Low-frequency variability of the large-scale ocean circulation: A dynamical systems approach. Rev. Geophys.,43, RG3002, doi:10.1029/2002RG000122.

  • Douglass, E. M., S. R. Jayne, F. O. Bryan, S. Peacock, and M. Maltrud, 2012: Kuroshio pathways in a climatologically forced model. J. Oceanogr., 68, 625639, doi:10.1007/s10872-012-0123-y.

    • Search Google Scholar
    • Export Citation
  • Douglass, E. M., Y.-O. Kwon, and S. R. Jayne, 2013: A comparison of North Pacific and North Atlantic subtropical mode waters in a climatologically-forced model. Deep-Sea Res. II, 91, 139151, doi:10.1016/j.dsr2.2013.02.023.

    • Search Google Scholar
    • Export Citation
  • Duchon, C., 1979: Lanczos filtering in one and two dimensions. J. Appl. Meteor.,18, 1016–1022, doi:10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2.

  • Dussin, R., and B. Barnier, 2013: The making of DFS 5.1. Drakkar Project Rep., 40 pp. [Available online at http://www.drakkar-ocean.eu/publications/reports/dfs5-1-report.]

  • Eady, E. T., 1949: Long waves and cyclone waves. Tellus, 1, 3352, doi:10.1111/j.2153-3490.1949.tb01265.x.

  • Greatbatch, R. J., 1994: A note on the representation of steric sea level in models that conserve volume rather than mass. J. Geophys. Res., 99 (C6), 12 76712 771, doi:10.1029/94JC00847.

    • Search Google Scholar
    • Export Citation
  • Grégorio, S., T. Penduff, G. Sérazin, J.-M. Molines, B. Barnier, and J. Hirschi, 2015: Intrinsic variability of the Atlantic Meridional Overturning Circulation at interannual-to-multidecadal timescales. J. Phys. Oceanogr., in press. doi:10.1175/JPO-D-14-0163.1.

    • Search Google Scholar
    • Export Citation
  • Gulev, S. K., M. Latif, N. Keenlyside, W. Park, and K. P. Koltermann, 2013: North Atlantic Ocean control on surface heat flux on multidecadal timescales. Nature, 499, 464467, doi:10.1038/nature12268.

    • Search Google Scholar
    • Export Citation
  • Hazeleger, W., and S. S. Drijfhout, 2000: A model study on internally generated variability in subtropical mode water formation. J. Geophys. Res., 105 (C6), 13 965, doi:10.1029/2000JC900041.

    • Search Google Scholar
    • Export Citation
  • Holland, W. R., 1978: The role of mesoscale eddies in the general circulation of the ocean–numerical experiments using a wind-driven quasi-geostrophic model. J. Phys. Oceanogr., 8, 363392, doi:10.1175/1520-0485(1978)008<0363:TROMEI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hurlburt, H. E., and Coauthors, 2009: High-resolution global and basin-scale ocean analyses and forecasts. Oceanography, 22, 110–127, doi:10.5670/oceanog.2009.70.

  • Le Sommer, J., T. Penduff, S. Theetten, G. Madec, and B. Barnier, 2009: How momentum advection schemes influence current-topography interactions at eddy permitting resolution. Ocean Modell., 29, 114, doi:10.1016/j.ocemod.2008.11.007.

    • Search Google Scholar
    • Export Citation
  • Madec, G., 2008: NEMO ocean engine. Institut Pierre-Simon Laplace (IPSL) Note du Pole de Modélisation 27, 217 pp.

  • Molines, J.-M., B. Barnier, T. Penduff, A. M. Treguier, and J. Le Sommer, 2014: ORCA12.L46 climatological and interannual simulations forced with DFS4.4: GJM02 and MJM88. Drakkar Group Experiment Rep. GDRI-DRAKKAR-2014-03-19, 50 pp. [Available online at http://www.drakkar-ocean.eu/publications/reports/orca12_reference_experiments_2014.]

  • O’Kane, T. J., R. J. Matear, M. A. Chamberlain, J. S. Risbey, B. M. Sloyan, and I. Horenko, 2013: Decadal variability in an OGCM Southern ocean: Intrinsic modes, forced modes and metastable states. Ocean Modell., 69, 121, doi:10.1016/j.ocemod.2013.04.009.

    • Search Google Scholar
    • Export Citation
  • Okkonen, S. R., G. A. Jacobs, E. Joseph Metzger, H. E. Hurlburt, and J. F. Shriver, 2001: Mesoscale variability in the boundary currents of the Alaska gyre. Cont. Shelf Res., 21 (11–12), 12191236, doi:10.1016/S0278-4343(00)00085-6.

    • Search Google Scholar
    • Export Citation
  • Penduff, T., J. Le Sommer, B. Barnier, A.-M. Treguier, J.-M. Molines, and G. Madec, 2007: Influence of numerical schemes on current-topography interactions in ° global ocean simulations. Ocean Sci., 3, 509524, doi:10.5194/os-3-509-2007.

    • Search Google Scholar
    • Export Citation
  • Penduff, T., M. Juza, L. Brodeau, G. C. Smith, B. Barnier, J.-M. Molines, A.-M. Treguier, and G. Madec, 2010: Impact of global ocean model resolution on sea-level variability with emphasis on interannual time scales. Ocean Sci.,6, 269–284, doi:10.5194/os-6-269-2010.

  • Penduff, T., M. Juza, B. Barnier, J. Zika, W. K. Dewar, A.-M. Treguier, J.-M. Molines, and N. Audiffren, 2011: Sea level expression of intrinsic and forced ocean variabilities at interannual time scales. J. Climate,24, 5652–5670, doi:10.1175/JCLI-D-11-00077.1.

  • Pierini, S., 2006: A Kuroshio Extension system model study: Decadal chaotic self-sustained oscillations. J. Phys. Oceanogr., 36, 16051625, doi:10.1175/JPO2931.1.

    • Search Google Scholar
    • Export Citation
  • Pierini, S., 2011: Low-frequency variability, coherence resonance, and phase selection in a low-order model of the wind-driven ocean circulation. J. Phys. Oceanogr., 41, 15851604, doi:10.1175/JPO-D-10-05018.1.

    • Search Google Scholar
    • Export Citation
  • Pierini, S., 2014: Kuroshio Extension bimodality and the North Pacific Oscillation: A case of intrinsic variability paced by external forcing. J. Climate, 27, 448454, doi:10.1175/JCLI-D-13-00306.1.

    • Search Google Scholar
    • Export Citation
  • Quattrocchi, G., S. Pierini, and H. A. Dijkstra, 2012: Intrinsic low-frequency variability of the Gulf Stream. Nonlinear Processes Geophys., 19, 155164, doi:10.5194/npg-19-155-2012.

    • Search Google Scholar
    • Export Citation
  • Simonnet, E., and H. A. Dijkstra, 2002: Spontaneous generation of low-frequency modes of variability in the wind-driven ocean circulation. J. Phys. Oceanogr., 32, 17471762, doi:10.1175/1520-0485(2002)032<1747:SGOLFM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Spall, M. A., 1996: Dynamics of the Gulf Stream/deep western boundary current crossover. Part II: Low-frequency internal oscillations. J. Phys. Oceanogr., 26, 21692182, doi:10.1175/1520-0485(1996)026<2169:DOTGSW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Taguchi, B., S.-P. Xie, N. Schneider, M. Nonaka, H. Sasaki, and Y. Sasai, 2007: Decadal variability of the Kuroshio Extension: Observations and an eddy-resolving model hindcast. J. Climate, 20, 23572377, doi:10.1175/JCLI4142.1.

    • Search Google Scholar
    • Export Citation
  • Thomas, M. D., and X. Zhai, 2013: Eddy-induced variability of the meridional overturning circulation in a model of the North Atlantic. Geophys. Res. Lett., 40, 27422747, doi:10.1002/grl.50532.

    • Search Google Scholar
    • Export Citation
  • Thompson, A. F., and K. J. Richards, 2011: Low frequency variability of Southern Ocean jets. J. Geophys. Res.,116, C09022, doi:10.1029/2010JC006749.

  • Treguier, A. M., and Coauthors, 2014: Meridional transport of salt in the global ocean from an eddy-resolving model. Ocean Sci., 10, 243255, doi:10.5194/os-10-243-2014.

    • Search Google Scholar
    • Export Citation
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