Predictability of Mesoscale Variability in the East Australian Current Given Strong-Constraint Data Assimilation

Javier Zavala-Garay Institute of Marine and Coastal Sciences, Rutgers, The State University of New Jersey, New Brunswick, New Jersey

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J. L. Wilkin Institute of Marine and Coastal Sciences, Rutgers, The State University of New Jersey, New Brunswick, New Jersey

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H. G. Arango Institute of Marine and Coastal Sciences, Rutgers, The State University of New Jersey, New Brunswick, New Jersey

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Abstract

One of the many applications of data assimilation is the estimation of adequate initial conditions for model forecasts. In this work, the authors evaluate to what extent the incremental, strong-constraint, four-dimensional variational data assimilation (IS4DVAR) can improve prediction of mesoscale variability in the East Australian Current (EAC) using the Regional Ocean Modeling System (ROMS). The observations considered in the assimilation experiments are daily composites of satellite sea surface temperature (SST), 7-day average reanalysis of satellite altimeter sea level anomalies, and subsurface temperature profiles from high-resolution expendable bathythermograph (XBT). Considering all available observations for years 2001 and 2002, ROMS forecast initial conditions are generated every week by assimilating the available observations from the 7 days prior to the forecast initial time. It is shown that assimilation of surface information only [SST and sea surface height (SSH)] results in poor estimates of the true subsurface ocean state (as depicted by the XBTs) and therefore poor forecast skill of subsurface conditions. Including the XBTs in the assimilation experiments improves the ocean state estimation in the vicinity of the XBT transects. By introducing subsurface pseudo-observations (which are called synthetic CTD) based on an empirical relationship between satellite surface observations and subsurface variability, the authors find a significant improvement in ocean state estimates that leads to skillful forecasts for up to 2 weeks in the domain considered.

Corresponding author address: Javier Zavala-Garay, Institute of Marine and Coastal Sciences, Rutgers University, 71 Dudley Rd., New Brunswick, NJ 08901-8521. E-mail: jzavala@marine.rutgers.edu

Abstract

One of the many applications of data assimilation is the estimation of adequate initial conditions for model forecasts. In this work, the authors evaluate to what extent the incremental, strong-constraint, four-dimensional variational data assimilation (IS4DVAR) can improve prediction of mesoscale variability in the East Australian Current (EAC) using the Regional Ocean Modeling System (ROMS). The observations considered in the assimilation experiments are daily composites of satellite sea surface temperature (SST), 7-day average reanalysis of satellite altimeter sea level anomalies, and subsurface temperature profiles from high-resolution expendable bathythermograph (XBT). Considering all available observations for years 2001 and 2002, ROMS forecast initial conditions are generated every week by assimilating the available observations from the 7 days prior to the forecast initial time. It is shown that assimilation of surface information only [SST and sea surface height (SSH)] results in poor estimates of the true subsurface ocean state (as depicted by the XBTs) and therefore poor forecast skill of subsurface conditions. Including the XBTs in the assimilation experiments improves the ocean state estimation in the vicinity of the XBT transects. By introducing subsurface pseudo-observations (which are called synthetic CTD) based on an empirical relationship between satellite surface observations and subsurface variability, the authors find a significant improvement in ocean state estimates that leads to skillful forecasts for up to 2 weeks in the domain considered.

Corresponding author address: Javier Zavala-Garay, Institute of Marine and Coastal Sciences, Rutgers University, 71 Dudley Rd., New Brunswick, NJ 08901-8521. E-mail: jzavala@marine.rutgers.edu
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  • AVISO, 1996: Merged TOPEX-Poseidon products (GDR-Ms). 3rd ed. AVISO User Handbook AVI-NT-02-101-CN, 201 pp.

  • Bennett, A. F., B. S. Chua, D. E. Harrison, and M. J. McPhaden, 2000: Generalized inversion of Tropical Atmosphere–Ocean (TAO) data and a coupled model of the tropical Pacific. Part II: The 1995–96 La Niña and 1997–98 El Niño. J. Climate, 13, 27702785.

    • Search Google Scholar
    • Export Citation
  • Bowen, M. M., J. L. Wilkin, and E. W. L., 2005: Variability and forcing of the East Australian Current. J. Geophys. Res., 110, C03019, doi:10.1029/2004JC002533.

    • Search Google Scholar
    • Export Citation
  • Chapman, D. C., 1985: Numerical treatment of cross-shelf open boundaries in a barotropic coastal ocean model. J. Phys. Oceanogr., 15, 10601075.

    • Search Google Scholar
    • Export Citation
  • Cooper, M. C., and K. Haines, 1996: Data assimilation with water property conservation. J. Geophys. Res., 101, 10591077.

  • Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 13671387.

    • Search Google Scholar
    • Export Citation
  • Cummings, J. A., 2005: Operational multivariate ocean data assimilation. Quart. J. Roy. Meteor. Soc., 131, 35833604.

  • Cummings, J. A., and Coauthors, 2009: Ocean data assimilation systems for GODAE. Oceanography, 22, 97109.

  • Di Lorenzo, E., A. M. Moore, H. G. Arango, B. D. Cornuelle, A. J. Miller, B. Powell, B. S. Chua, and A. F. Bennett, 2007: Weak and strong constraint data assimilation in the inverse Regional Ocean Modeling System (ROMS): Development and application for a baroclinic coastal upwelling system. Ocean Modell., 16 (3–4), 160187.

    • Search Google Scholar
    • Export Citation
  • Dobricic, S., N. Pinardi, M. Adani, A. Bonazzi, C. Fratianni, and M. Tonani, 2005: Mediterranean Forecasting System: An improved assimilation scheme for sea-level anomaly and its validation. Quart. J. Roy. Meteor. Soc., 131, 36273642.

    • Search Google Scholar
    • Export Citation
  • Dunn, J., and K. R. Ridgway, 2002: Mapping ocean properties in regions of complex topography. Deep-Sea Res. I, 49, 591604.

  • Evensen, G., 2006: Data Assimilation: The Ensemble Kalman Filter. Springer, 307 pp.

  • Farrell, B. F., and P. J. Ioannou, 1996a: Generalized stability theory. Part I: Autonomous operators. J. Atmos. Sci., 53, 20252040.

  • Farrell, B. F., and P. J. Ioannou, 1996b: Generalized stability theory. Part II: Nonautonomous operators. J. Atmos. Sci., 53, 20412053.

    • Search Google Scholar
    • Export Citation
  • Flather, R. A., 1976: A tidal model of the northwest European continental shelf. Mem. Soc. Roy. Sci. Liege, 6, 141164.

  • Fujii, Y., and M. Kamachi, 2003: Three-dimensional analysis of temperature and salinity in the equatorial Pacific using a variational method with vertical coupled temperature-salinity empirical orthogonal function modes. J. Geophys. Res., 108, 3297, doi:10.1029/2002JC001745.

    • Search Google Scholar
    • Export Citation
  • Fukumori, I., 2002: A partitioned Kalman filter and smoother. Mon. Wea. Rev., 130, 13701383.

  • Gangopadhyay, A., and A. R. Robinson, 2002: Feature-oriented regional modeling of oceanic fronts. Dyn. Atmos. Oceans, 36, 201232.

  • Godfrey, J. S., G. R. Cresswell, T. J. Golding, A. F. Pearce, and R. Boyd, 1980: The separation of the East Australian Current. J. Phys. Oceanogr., 10, 430440.

    • Search Google Scholar
    • Export Citation
  • Haidvogel, D. B., H. Arango, K. Hedstrom, A. Beckmann, P. Malanotte-Rizzoli, and A. F. Shchepetkin, 2000: Model evaluation experiments in the North Atlantic basin: Simulations in nonlinear terrain-following coordinates. Dyn. Atmos. Oceans, 32 (3–4), 239281.

    • Search Google Scholar
    • Export Citation
  • Haidvogel, D. B., and Coauthors, 2008: Ocean forecasting in terrain-following coordinates: Formulation and skill assessment of the Regional Ocean Modeling system. J. Comput. Phys., 227, 35953624.

    • Search Google Scholar
    • Export Citation
  • Kurapov, A. L., A. L. Egbert, J. S. Allen, and R. N. Miller, 2009: Representer-based analyses in the coastal upwelling system. Dyn. Atmos. Oceans, 48, 198218.

    • Search Google Scholar
    • Export Citation
  • Large, W. G., J. C. McWilliams, and S. C. Doney, 1994: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization. Rev. Geophys., 32, 363403.

    • Search Google Scholar
    • Export Citation
  • Le Dimet, F.-X., and O. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 38A, 97110.

    • Search Google Scholar
    • Export Citation
  • Lermusiaux, P. F. J., 2002: On the mapping of multivariate geostrophic fields: Sensitivities to size, scales, and dynamics. J. Atmos. Oceanic Technol., 19, 16021636.

    • Search Google Scholar
    • Export Citation
  • Lermusiaux, P. F. J., D. G. M. Anderson, and C. J. Lozano, 2000: On the mapping of multivariate geostrophic fields: Error and variability subspace estimates. Quart. J. Roy. Meteor. Soc., 126, 13871429.

    • Search Google Scholar
    • Export Citation
  • Lermusiaux, P. F. J., J. Xu, C.-F. Chen, S. Jan, L. Y. Chiu, and Y.-J. Yang, 2010: Coupled ocean-acoustic prediction of transmission loss in a continental shelfbreak region: Predictive skill, uncertainty quantification and dynamical sensitivities. J. Oceanic Eng., 35, 895916.

    • Search Google Scholar
    • Export Citation
  • Le Traon, P.-Y., and F. Ogor, 1998: ERS-1/2 orbit improvement using TOPEX-POSEIDON: The 2 cm challenge. J. Geophys. Res., 103, 80458057.

    • Search Google Scholar
    • Export Citation
  • Marchesiello, P., and J. H. Middleton, 2000: Modeling the East Australian Current in the western Tasman Sea. J. Phys. Oceanogr., 30, 29562971.

    • Search Google Scholar
    • Export Citation
  • Mata, M. M., M. Tomczak, S. Wijffels, and J. A. Church, 2000: East Australian Current volume transports at 30°S: Estimates from the World Ocean Circulation Experiment hydrographic sections PR11/P6 and the PCM3 current meter array. J. Geophys. Res., 105, 28 50928 526.

    • Search Google Scholar
    • Export Citation
  • Mata, M. M., S. E. Wijffels, J. A. Church, and M. Tomczak, 2006: Eddy shedding and energy conversions in the East Australian Current. J. Geophys. Res., 111, C09034, doi:10.1029/2006JC003592.

    • Search Google Scholar
    • Export Citation
  • Moore, A. M., H. G. Arango, E. Di Lorenzo, B. D. Cornuelle, A. J. Miller, and D. J. Neilson, 2004: A comprehensive ocean prediction and analysis system based on the tangent linear and adjoint components of a regional ocean model. Ocean Modell., 7 (1–2), 227258.

    • Search Google Scholar
    • Export Citation
  • Moore, A. M., H. G. Arango, E. Di Lorenzo, A. J. Miller, and B. D. Cornuelle, 2009: An adjoint sensitivity analysis of the Southern California Current circulation and ecosystem. J. Phys. Oceanogr., 39, 702720.

    • Search Google Scholar
    • Export Citation
  • Moore, A. M., H. G. Arango, G. Broquet, B. S. Powell, A. T. Weaver, and J. Zavala-Garay, 2011a: The Regional Ocean Modeling System (ROMS) 4-dimenensional variational data assimilation systems. Part I—System overview and formulation. Prog. Oceanogr., 91, 5073.

    • Search Google Scholar
    • Export Citation
  • Moore, A. M., and Coauthors, 2011b: The Regional Ocean Modeling System (ROMS) 4-dimenensional variational data assimilation systems. Part II—Performance and application to the California Current system. Prog. Oceanogr., 91, 5073.

    • Search Google Scholar
    • Export Citation
  • Moore, A. M., and Coauthors, 2011c: The Regional Ocean Modeling System (ROMS) 4-dimenensional variational data assimilation systems. Part III—Observation impact and observation sensitivity in the California Current system. Prog. Oceanogr., 91, 7494.

    • Search Google Scholar
    • Export Citation
  • Oke, P., G. B. Brassington, D. A. Griffin, and A. Schiller, 2008: The Bluelink ocean data assimilation system (BODAS). Ocean Modell., 21, 4670.

    • Search Google Scholar
    • Export Citation
  • Pacual, A., Y. Faugere, G. Larnicol, and P.-Y. Le Traon, 2006: Improved description of the ocean mesoscale variability by combining four satellite altimeters. Geophys. Res. Lett., 33, L02611, doi:10.1029/2005GL024633.

    • Search Google Scholar
    • Export Citation
  • Powell, B. S., H. G. Arango, A. M. Moore, E. Di Lorenzo, R. F. Milliff, and D. Foley, 2008: 4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS). Ocean Modell., 23 (3–4), 130145, doi:10.1016/j.ocemod.2008.04.008.

    • Search Google Scholar
    • Export Citation
  • Rasmond, T. E., 1992: The design and testing of the Navy Operational Global Atmospheric Prediction System. Wea. Forecasting, 7, 262272.

    • Search Google Scholar
    • Export Citation
  • Ridgway, K. R., and J. S. Godfrey, 1997: Seasonal cycle of the East Australia Current. J. Geophys. Res., 102, 22 92122 936.

  • Ridgway, K. R., J. R. Dunn, and J. L. Wilkin, 2002: Ocean interpolation by four-dimensional weighted least squares—Application to the waters around Australia. J. Atmos. Oceanic Technol., 19, 13571375.

    • Search Google Scholar
    • Export Citation
  • Sasaki, Y., 1970: Some basic formalisms on numerical variational analysis. Mon. Wea. Rev., 98, 875883.

  • Shchepetkin, A. F., and J. C. McWilliams, 2003: A method for computing horizontal pressure-gradient force in an oceanic model with a non-aligned vertical coordinate. J. Geophys. Res., 108, 3090, doi:10.1029/2001JC001047.

    • Search Google Scholar
    • Export Citation
  • Shchepetkin, A. F., and J. C. McWilliams, 2005: The Regional Ocean Modeling System (ROMS): A split-explicit, free-surface, topography-following coordinates ocean model. Ocean Modell., 9, 347404.

    • Search Google Scholar
    • Export Citation
  • Stammer, D., 1997: Global characteristics of ocean variability estimated from regional TOPEX/Poseidon altimeter measurements. J. Phys. Oceanogr., 27, 17431769.

    • Search Google Scholar
    • Export Citation
  • Stammer, D., and Coauthors, 2002: The global ocean circulation during 1992–1997 estimated from ocean observations and a general circulation model. J. Geophys. Res., 107, 3118, doi:10.1029/2001JC000888.

    • Search Google Scholar
    • Export Citation
  • Sun, C., and D. R. Watts, 2001: A circumpolar gravest empirical mode for the Southern Ocean hydrography. J. Geophys. Res., 106, 28332855.

    • Search Google Scholar
    • Export Citation
  • Talagrand, O., and P. Courtier, 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation. I: Theory. Quart. J. Roy. Meteor. Soc., 113, 13111328.

    • Search Google Scholar
    • Export Citation
  • Tiburg, E. C., H. E. Hulbert, J. J. O’Brien, and J. F. Shriver, 2001: The dynamics of the East Australia Current system: The Tasman Front, the East Auckland Current, and the East Cape Current. J. Phys. Oceanogr., 31, 29172943.

    • Search Google Scholar
    • Export Citation
  • Tomczak, M. J., 1981: Bass Strait water intrusions in the Tasman Sea and mean temperature-salinity curves. Aust. J. Mar. Freshwater Res., 32, 699708.

    • Search Google Scholar
    • Export Citation
  • Vossepoel, F. C., G. Burgers, and P. J. van Leeuwen, 2002: Effects of correcting salinity with altimeter measurements in an equatorial Pacific Ocean model. J. Geophys. Res., 107, 8001, doi:10.1029/2001JC000816.

    • Search Google Scholar
    • Export Citation
  • Walker, A., and J. L. Wilkin, 1998: Optimal averaging of NOAA/NASA Pathfinder satellite sea surface temperature data. J. Geophys. Res., 103, 12 86912 883.

    • Search Google Scholar
    • Export Citation
  • Weaver, A. T., and P. Courtier, 2001: Correlation modelling on the sphere using a generalized diffusion equation. Quart. J. Roy. Meteor. Soc., 127, 18151842.

    • Search Google Scholar
    • Export Citation
  • Weaver, A. T., J. Vialard, and D. L. T. Anderson, 2003: Three- and four-dimensional variational assimilation with an ocean general circulation model of the tropical Pacific Ocean. Part I: Formulation, internal diagnostics, and consistency checks. Mon. Wea. Rev., 131, 13601378.

    • Search Google Scholar
    • Export Citation
  • Weaver, A. T., C. Deltel, E. Machu, S. Ricci, and N. Daget, 2005: A multivariate balance operator for variational ocean data assimilation. Quart. J. Roy. Meteor. Soc., 131, 36053625.

    • Search Google Scholar
    • Export Citation
  • Wilkin, J. L., and W. G. Zhang, 2007: Modes of mesoscale sea surface height and temperature variability in the East Australian Current. J. Geophys. Res., 112, C01013, doi:10.1029/2006JC003590.

    • Search Google Scholar
    • Export Citation
  • Wilkin, J. L., M. M. Bowen, and W. J. Emery, 2002: Mapping mesoscale currents by optimal interpolation of satellite radiometer and altimeter data. Ocean Dyn., 52, 95103.

    • Search Google Scholar
    • Export Citation
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