Cover Monthly Weather Review
Monthly Weather Review

  • Bennett, A. F., 1992: Inverse Methods in Physical Oceanography. Cambridge University Press, 347 pp.

  • Bennett, A. F., 2002: Inverse Modeling of the Ocean and Atmosphere. Cambridge University Press, 234 pp.

  • Bennett, A. F., and M. A. Thorburn, 1992: The generalized inverse of a nonlinear quasi- geostrophic ocean circulation model. J. Phys. Oceanogr., 22 , 213–231.

    • Search Google Scholar
    • Export Citation

  • Chua, B. S., and A. F. Bennett, 2001: An inverse ocean modeling system. Ocean Modell., 3 , 137–165.

  • Conkright, M. E., R. A. Locarnini, H. E. Garcia, T. D. O’Brien, T. P. Boyer, C. Stephens, and J. I. Antonov, 2002: World Ocean Atlas 2001: Objective analyses, data statistics, and figures. CD-ROM documentation, National Oceanographic Data Center, Silver Spring, MD, 17 pp.

    • Search Google Scholar
    • Export Citation

  • Courtier, P., 1997: Dual formulation of four-dimensional variational assimilation. Quart. J. Roy. Meteor. Soc., 123 , 2449–2461.

  • Cox, M. D., 1984: A primitive equation, 3-dimensional model of the ocean. GFDL Ocean Group Tech. Rep. 1, 250 pp.

  • Cox, M. D., and K. Bryan, 1984: A numerical model of the ventilated thermocline. J. Phys. Oceanogr., 14 , 674–687.

  • Daley, R., 1991: Atmospheric Data Analysis. Cambridge Univeristy Press, 457 pp.

  • Fukumori, I., R. Raghunath, L-L. Fu, and Y. Chao, 1999: Assimilation of TOPEX/POSEIDON altimeter data into a global ocean circulation model: How good are the results? J. Geophys. Res., 104 , 25647–25665.

    • Search Google Scholar
    • Export Citation

  • Frankignoul, C., P. Müller, and E. Zorita, 1997: A simple model of the decadal response of the ocean to stochastic wind forcing. J. Phys. Oceanogr., 27 , 1533–1546.

    • Search Google Scholar
    • Export Citation

  • Gille, S. T., 2005: Statistical characterization of zonal and meridional ocean wind stress. J. Atmos. Oceanic Technol., 22 , 1353–1372.

    • Search Google Scholar
    • Export Citation

  • Köhl, A., D. Stammer, and B. Cornuelle, 2007: Interannual to decadal changes in the ECCO global synthesis. J. Phys. Oceanogr., 37 , 313–337.

    • Search Google Scholar
    • Export Citation

  • Lorenc, A. C., 2003: Modelling of error covariances by 4D-Var data assimilation. Quart. J. Roy. Meteor. Soc., 129 , 3167–3182.

  • Masuda, S., T. Awaji, N. Sugiura, Y. Ishikawa, K. Baba, K. Horiuchi, and N. Komori, 2003: Improved estimates of the dynamical state of the North Pacific Ocean from a 4 dimensional variational data assimilation. Geophys. Res. Lett., 30 , 1868. doi:10.1029/2003GL017604.

    • Search Google Scholar
    • Export Citation

  • Miller, R. N., and M. A. Cane, 1989: A Kalman filter analysis of sea level height in the tropical Pacific. J. Phys. Oceanogr., 19 , 773–790.

    • Search Google Scholar
    • Export Citation

  • Milliff, R. F., J. Morzel, D. B. Chelton, and M. H. Freilich, 2004: Wind stress curl and wind stress divergence biases from rain effects on QSCAT surface wind retrievals. J. Atmos. Oceanic Technol., 21 , 1216–1231.

    • Search Google Scholar
    • Export Citation

  • Muccino, J. C., and Coauthors, 2008: The Inverse Ocean Modeling System. Part II: Applications. J. Atmos. Oceanic Technol., 25 , 1623–1637.

    • Search Google Scholar
    • Export Citation

  • Müller, P., and C. Frankignoul, 1981: Direct atmospheric forcing of geostrophic eddies. J. Phys. Oceanogr., 11 , 287–308.

  • Ngodock, H. E., B. S. Chua, and A. F. Bennett, 2000: Generalized inverse of a reduced gravity primitive equation ocean model and tropical atmosphere–ocean data. Mon. Wea. Rev., 128 , 1757–1777.

    • Search Google Scholar
    • Export Citation

  • Pacanowski, R. C., and S. M. Griffies, 1999: The MOM3 manual. GFDL Ocean Group Tech. Rep. 4, NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, 680 pp.

    • Search Google Scholar
    • Export Citation

  • Priestley, M. B., 1981: Spectral Analysis and Time Series. Vol. 1, Academic Press, 702 pp.

  • 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

  • Stammer, D., and Coauthors, 2003: Volume, heat, and freshwater transports of the global ocean circulation 1993–2000, estimated from a general circulation model constrained by World Ocean Circulation Experiment (WOCE) data. J. Geophys. Res., 108 , 3007. doi:10.1029/2001JC001115.

    • Search Google Scholar
    • Export Citation

  • Todling, R., 2000: Estimation theory and atmospheric data assimilation. Inverse Methods in Global Biogeochemical Cycles, Geophys. Monogr., Vol. 114, Amer. Geophys. Union, 49–65.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., J. G. Olson, and W. G. Large, 1989: A global ocean wind stress climatology based on ECMWF analyses. NCAR Tech. Note NCAR/TN-338+STR, 93 pp.

    • Search Google Scholar
    • Export Citation

  • Veronis, G., 1970: Effect of fluctuating wind on ocean circulation. Deep-Sea Res., 17 , 421–434.

  • Veronis, G., and H. Stommel, 1956: The action of variable wind stresses on a stratified ocean. J. Mar. Res., 15 , 43–75.

  • Vialard, J., F. Vitart, M. Balmaseda, T. Stockdale, and D. Anderson, 2005: An ensemble generation method for seasonal forecasting with an ocean–atmosphere coupled model. Mon. Wea. Rev., 133 , 441–453.

    • Search Google Scholar
    • Export Citation

  • Vossepoel, F. C., A. T. Weaver, J. Vialard, and P. Delecluse, 2004: Adjustment of near- equatorial wind stress with four-dimensional variational data assimilation in a model of the Pacific Ocean. Mon. Wea. Rev., 132 , 2070–2083.

    • 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 , 1815–1846.

    • Search Google Scholar
    • Export Citation

  • Weaver, A. T., J. Vialard, and D. L. T. Anderson, 2003: Three- and four-dimensional variational assimilation in a general circulation model of the tropical Pacific Ocean. Part I: Formulation, internal diagnostics, and consistency checks. Mon. Wea. Rev., 131 , 1360–1378.

    • Search Google Scholar
    • Export Citation

  • Willebrand, J., S. G. H. Philander, and R. C. Pacanowski, 1980: The oceanic response to large-scale atmospheric disturbances. J. Phys. Oceanogr., 10 , 411–429.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2 2 2
PDF Downloads 1 1 1
Editorial Type:
Article
Article Type:
Research Article

On the Influence of Random Wind Stress Errors on the Four-Dimensional, Midlatitude Ocean Inverse Problem

Tsuyoshi Wakamatsu1, Michael G. G. Foreman1, Patrick F. Cummins1, and Josef Y. Cherniawsky1
View More View Less
  • 1 Fisheries and Oceans Canada, Institute of Ocean Sciences, Sidney, British Columbia, Canada
Print Publication:
01 Jun 2009
DOI:
https://doi.org/10.1175/2008MWR2621.1
Page(s):
1844–1862
Restricted access

Abstract

The effects of the parameterized wind stress error covariance function on the a priori error covariance of an ocean general circulation model (OGCM) are examined. These effects are diagnosed by computing the projection of the a priori model state error covariance matrix to sea surface height (SSH). The sensitivities of the a priori error covariance to the wind stress curl error are inferred from the a priori SSH error covariance and are shown to differ between the subpolar and subtropical gyres because of different contributions from barotropic and baroclinic ocean dynamics. The spatial structure of the SSH error covariance due to the wind stress error indicates that the a priori model state error is determined indirectly by the wind stress curl error. The impact of this sensitivity on the solution of a four-dimensional inverse problem is inferred.

Corresponding author address: Tsuyoshi Wakamatsu, Fisheries and Oceans Canada, Institute of Ocean Sciences, 9860 West Saanich Rd., P.O. Box 6000, Sidney BC V8L 4B2, Canada. Email: tsuyoshi.wakamatsu@dfo-mpo.gc.ca

Abstract

The effects of the parameterized wind stress error covariance function on the a priori error covariance of an ocean general circulation model (OGCM) are examined. These effects are diagnosed by computing the projection of the a priori model state error covariance matrix to sea surface height (SSH). The sensitivities of the a priori error covariance to the wind stress curl error are inferred from the a priori SSH error covariance and are shown to differ between the subpolar and subtropical gyres because of different contributions from barotropic and baroclinic ocean dynamics. The spatial structure of the SSH error covariance due to the wind stress error indicates that the a priori model state error is determined indirectly by the wind stress curl error. The impact of this sensitivity on the solution of a four-dimensional inverse problem is inferred.

Corresponding author address: Tsuyoshi Wakamatsu, Fisheries and Oceans Canada, Institute of Ocean Sciences, 9860 West Saanich Rd., P.O. Box 6000, Sidney BC V8L 4B2, Canada. Email: tsuyoshi.wakamatsu@dfo-mpo.gc.ca

Save