• Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129 , 28842903.

  • Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131 , 634642.

  • Bishop, C. H., B. J. Etherton, and S. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129 , 420436.

    • Search Google Scholar
    • Export Citation
  • Burgers, G., P. J. van Leeuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126 , 17191724.

  • Delworth, T. L., and K. Dixon, 2000: Implications of the recent trend in the Arctic/North Atlantic oscillation for the North Atlantic thermohaline circulation. J. Climate, 13 , 37213727.

    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., and R. J. Greatbatch, 2000: Multidecadal thermohaline circulation variability driven by atmospheric surface flux forcing. J. Climate, 13 , 14811495.

    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., and Coauthors, 2006: GFDL’s CM2 global coupled climate models. Part I: Formulation and simulation characteristics. J. Climate, 19 , 643674.

    • Search Google Scholar
    • Export Citation
  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 , 1014310162.

    • Search Google Scholar
    • Export Citation
  • Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125 , 723757.

    • Search Google Scholar
    • Export Citation
  • GFDL Global Atmospheric Model Development Team, 2004: The new GFDL global atmosphere and land model AM2/LM2: Evaluation with prescribed SST simulations. J. Climate, 17 , 46414673.

    • Search Google Scholar
    • Export Citation
  • Gnanadesikan, A., and Coauthors, 2006: GFDL’s CM2 global coupled climate models. Part II: The baseline ocean simulation. J. Climate, 19 , 675697.

    • Search Google Scholar
    • Export Citation
  • Gordon, C. T., L. Umscheid Jr., and K. Miyakoda, 1972: Simulation experiments for determining wind data requirements in the tropics. J. Atmos. Sci., 29 , 10641075.

    • Search Google Scholar
    • Export Citation
  • Griffies, S. M., 2005: Some ocean model fundamentals. Ocean Weather Forecasting: An Integrated View of Oceanography, E. P. Chassignet and J. Verron, Eds., Springer, 19–74.

  • Hahn, D. G., and S. Manabe, 1982: Observational network and climate monitoring. Proc. Sixth Annual Climate Diagnostics Workshop, Palisades, NY, Lamont-Doherty Geological Observatory, 229–235.

  • Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129 , 27762790.

    • Search Google Scholar
    • Export Citation
  • Han, G., J. Zhu, and G. Zhou, 2004: Salinity estimation using the T-S relation in the context of variational data assimilation. J. Geophys. Res., 109 .C03018, doi:10.1029/2003JC001781.

    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126 , 796811.

    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129 , 123137.

    • Search Google Scholar
    • Export Citation
  • Hunke, E. C., and J. K. Dukowicz, 1997: An elastic–viscous–plastic model for sea ice dynamics. J. Phys. Oceanogr., 27 , 18491867.

  • Jazwinski, A. H., 1970: Stochastic Processes and Filtering Theory. Academic Press, 376 pp.

  • Levitus, S., J. I. Antonov, T. P. Boyer, and C. Stephens, 2000: Warming of the World Ocean. Science, 287 , 22252229.

  • Levitus, S., J. I. Antonov, and T. P. Boyer, 2005: Warming of the World Ocean. Geophys. Res. Lett., 32 , 19552005.

  • Miller, R. N., 1998: Introduction to the Kalman filter. Proc. ECMWF Seminar on Data Assimilation, Reading, United Kingdom, ECMWF, 47–59.

  • Miller, R. N., M. Ghil, and P. Gauthiez, 1994: Advanced data assimilation in strongly nonlinear dynamical system. J. Atmos. Sci., 51 , 10371056.

    • Search Google Scholar
    • Export Citation
  • Miller, R. N., E. F. Carter, and S. T. Blue, 1999: Data assimilation into nonlinear stochastic models. Tellus, 51A , 167194.

  • Mitchell, H. L., and P. L. Houtekamer, 2000: An adaptive ensemble Kalman filter. Mon. Wea. Rev., 128 , 416433.

  • Ricci, S., A. T. Weaver, J. Vialard, and P. Rogel, 2005: Incorporating state-dependent temperature–salinity constraints in the background error covariance of variational ocean data assimilation. Mon. Wea. Rev., 133 , 317338.

    • Search Google Scholar
    • Export Citation
  • Rosati, A., K. Miyakoda, and R. Gudgel, 1997: The impact of ocean initial conditions on ENSO forecasting with a coupled model. Mon. Wea. Rev., 125 , 754772.

    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker, 2003: Ensemble square root filters. Mon. Wea. Rev., 131 , 14851490.

    • Search Google Scholar
    • Export Citation
  • Troccoli, A., and K. Haines, 1999: Use of the temperature–salinity relation in a data assimilation context. J. Atmos. Oceanic Technol., 16 , 20112025.

    • Search Google Scholar
    • Export Citation
  • Troccoli, A., and Coauthors, 2002: Salinity adjustments in the presence of temperature data assimilation. Mon. Wea. Rev., 130 , 89102.

    • Search Google Scholar
    • Export Citation
  • van Leeuwen, P. J., 1999: Comments on “Data assimilation using an ensemble Kalman filter technique.”. Mon. Wea. Rev., 127 , 13741377.

    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130 , 19131924.

  • Winton, M., 2000: A reformulated three-layer sea ice model. J. Atmos. Oceanic Technol., 17 , 525531.

  • Zhang, S., and J. L. Anderson, 2003: Impact of spatially and temporally varying estimates of error covariance on assimilation in a simple atmospheric model. Tellus, 55A , 126147.

    • Search Google Scholar
    • Export Citation
  • Zhang, S., M. J. Harrison, A. T. Wittenberg, A. Rosati, J. L. Anderson, and V. Balaji, 2005: Initialization of an ENSO forecast system using a parallelized ensemble filter. Mon. Wea. Rev., 133 , 31763201.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 34 34 34
PDF Downloads 6 6 6

System Design and Evaluation of Coupled Ensemble Data Assimilation for Global Oceanic Climate Studies

View More View Less
  • 1 Geophysical Fluid Dynamics Laboratory, Princeton University, Princeton, New Jersey
Restricted access

Abstract

A fully coupled data assimilation (CDA) system, consisting of an ensemble filter applied to the Geophysical Fluid Dynamics Laboratory’s global fully coupled climate model (CM2), has been developed to facilitate the detection and prediction of seasonal-to-multidecadal climate variability and climate trends. The assimilation provides a self-consistent, temporally continuous estimate of the coupled model state and its uncertainty, in the form of discrete ensemble members, which can be used directly to initialize probabilistic climate forecasts. Here, the CDA is evaluated using a series of perfect model experiments, in which a particular twentieth-century simulation—with temporally varying greenhouse gas and natural aerosol radiative forcings—serves as a “truth” from which observations are drawn, according to the actual ocean observing network for the twentieth century. These observations are then assimilated into a coupled model ensemble that is subjected only to preindustrial forcings. By examining how well this analysis ensemble reproduces the “truth,” the skill of the analysis system in recovering anthropogenically forced trends and natural climate variability is assessed, given the historical observing network. The assimilation successfully reconstructs the twentieth-century ocean heat content variability and trends in most locations. The experiments highlight the importance of maintaining key physical relationships among model fields, which are associated with water masses in the ocean and geostrophy in the atmosphere. For example, when only oceanic temperatures are assimilated, the ocean analysis is greatly improved by incorporating the temperature–salinity covariance provided by the analysis ensemble. Interestingly, wind observations are more helpful than atmospheric temperature observations for constructing the structure of the tropical atmosphere; the opposite holds for the extratropical atmosphere. The experiments indicate that the Atlantic meridional overturning circulation is difficult to constrain using the twentieth-century observational network, but there is hope that additional observations—including those from the newly deployed Argo profiles—may lessen this problem in the twenty-first century. The challenges for data assimilation of model systematic biases and evolving observing systems are discussed.

Corresponding author address: Shaoqing Zhang, NOAA/GFDL, Princeton University, P.O. Box 308, Princeton, NJ 08542. Email: shaoqing.zhang@noaa.gov

Abstract

A fully coupled data assimilation (CDA) system, consisting of an ensemble filter applied to the Geophysical Fluid Dynamics Laboratory’s global fully coupled climate model (CM2), has been developed to facilitate the detection and prediction of seasonal-to-multidecadal climate variability and climate trends. The assimilation provides a self-consistent, temporally continuous estimate of the coupled model state and its uncertainty, in the form of discrete ensemble members, which can be used directly to initialize probabilistic climate forecasts. Here, the CDA is evaluated using a series of perfect model experiments, in which a particular twentieth-century simulation—with temporally varying greenhouse gas and natural aerosol radiative forcings—serves as a “truth” from which observations are drawn, according to the actual ocean observing network for the twentieth century. These observations are then assimilated into a coupled model ensemble that is subjected only to preindustrial forcings. By examining how well this analysis ensemble reproduces the “truth,” the skill of the analysis system in recovering anthropogenically forced trends and natural climate variability is assessed, given the historical observing network. The assimilation successfully reconstructs the twentieth-century ocean heat content variability and trends in most locations. The experiments highlight the importance of maintaining key physical relationships among model fields, which are associated with water masses in the ocean and geostrophy in the atmosphere. For example, when only oceanic temperatures are assimilated, the ocean analysis is greatly improved by incorporating the temperature–salinity covariance provided by the analysis ensemble. Interestingly, wind observations are more helpful than atmospheric temperature observations for constructing the structure of the tropical atmosphere; the opposite holds for the extratropical atmosphere. The experiments indicate that the Atlantic meridional overturning circulation is difficult to constrain using the twentieth-century observational network, but there is hope that additional observations—including those from the newly deployed Argo profiles—may lessen this problem in the twenty-first century. The challenges for data assimilation of model systematic biases and evolving observing systems are discussed.

Corresponding author address: Shaoqing Zhang, NOAA/GFDL, Princeton University, P.O. Box 308, Princeton, NJ 08542. Email: shaoqing.zhang@noaa.gov

Save