• Alexander, M., and Coauthors, 2006: Extratropical atmosphere–ocean variability in CCSM3. J. Climate, 19 , 24962525.

  • Alexander, M., L. Matrosova, C. Penland, J. D. Scott, and P. Chang, 2008: Forecasting Pacific SSTs: Linear inverse model predictions of the PDO. J. Climate, 21 , 385402.

    • Search Google Scholar
    • Export Citation
  • Boer, G. J., 2000: A study of atmosphere–ocean predictability on long time scales. Climate Dyn., 16 , 469477.

  • Boer, G. J., 2009: Changes in interannual variability and decadal potential predictability under global warming. J. Climate, 22 , 30983109.

    • Search Google Scholar
    • Export Citation
  • Boer, G. J., 2010: Decadal potential predictability of twenty-first century climate. Climate Dyn., doi:10.1007/s00382-010-0747-9, in press.

    • Search Google Scholar
    • Export Citation
  • Brohan, P., J. J. Kennedy, I. Harris, S. F. B. Tett, and P. D. Jones, 2006: Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850. J. Geophys. Res., 111 , D12106. doi:10.1029/2005JD006548.

    • Search Google Scholar
    • Export Citation
  • Bryan, F. O., and Coauthors, 2006: Response of the North Atlantic thermohaline circulation and ventilation to increased carbon dioxide in CCSM3. J. Climate, 19 , 23822397.

    • Search Google Scholar
    • Export Citation
  • Collins, M., 2002: Climate predictability on interannual to decadal time scales: The initial value problem. Climate Dyn., 19 , 671692.

    • Search Google Scholar
    • Export Citation
  • Collins, M., and M. R. Allen, 2002: Assessing the relative roles of initial and boundary conditions in interannual to decadal climate predictability. J. Climate, 15 , 31043109.

    • Search Google Scholar
    • Export Citation
  • Collins, M., and B. Sinha, 2003: Predictability of decadal variations in the thermohaline circulation and climate. Geophys. Res. Lett., 30 , 1306. doi:10.1029/2002GL016504.

    • Search Google Scholar
    • Export Citation
  • Collins, M., and Coauthors, 2006: Interannual to decadal climate predictability in the North Atlantic: A multimodel-ensemble study. J. Climate, 19 , 11951203.

    • Search Google Scholar
    • Export Citation
  • Collins, W. D., and Coauthors, 2006: The Community Climate System Model version 3 (CCSM3). J. Climate, 19 , 21222143.

  • Danabasoglu, G., 2008: On multidecadal variability of the Atlantic overturning circulation in the Community Climate System Model version 3. J. Climate, 21 , 55245544.

    • Search Google Scholar
    • Export Citation
  • Griffies, S. M., and K. Bryan, 1997a: A predictability study of simulated North Atlantic multidecadal variability. Climate Dyn., 13 , 459488.

    • Search Google Scholar
    • Export Citation
  • Griffies, S. M., and K. Bryan, 1997b: Predictability of North Atlantic multidecadal climate variability. Science, 275 , 181184.

  • Grötzner, A., M. Latif, A. Timmermann, and R. Voss, 1999: Interannual to decadal predictability in a coupled ocean–atmosphere general circulation model. J. Climate, 12 , 26072624.

    • Search Google Scholar
    • Export Citation
  • Gu, D. F., and S. G. H. Philander, 1997: Interdecadal climate fluctuations that depend on exchanges between the tropics and extratopics. Science, 275 , 805807.

    • Search Google Scholar
    • Export Citation
  • Hawkins, E., and R. Sutton, 2009: The potential to narrow uncertainty in regional climate predictions. Bull. Amer. Meteor. Soc., 90 , 10951107.

    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., and Coauthors, 2009: Decadal climate prediction: Opportunities and challenges. OceanObs ’09 Community White Paper, 1–21.

    • Search Google Scholar
    • Export Citation
  • Karoly, D. J., and Q. Wu, 2005: Detection of regional surface temperature trends. J. Climate, 18 , 43374343.

  • Keenlyside, N., M. Latif, J. Junclaus, L. Kornblueh, and E. Roeckner, 2008: Advancing decadal climate scale prediction in the North Atlantic. Nature, 453 , 8488.

    • Search Google Scholar
    • Export Citation
  • Kleeman, R., 2002: Measuring dynamical prediction utility using relative entropy. J. Atmos. Sci., 59 , 20572072.

  • Knutson, T. R., T. L. Delworth, K. W. Dixon, and R. J. Stouffer, 1999: Model assessment of regional surface temperature trends (1949–1997). J. Geophys. Res., 104 , 3098130996.

    • Search Google Scholar
    • Export Citation
  • Kwon, Y. O., and C. Deser, 2007: North Pacific decadal variability in the Community Climate System Model version 2. J. Climate, 20 , 24162433.

    • Search Google Scholar
    • Export Citation
  • Latif, M., and T. P. Barnett, 1994: Causes of decadal climate variability over the North Pacific and North America. Science, 266 , 634637.

    • Search Google Scholar
    • Export Citation
  • Latif, M., M. Collins, H. Pohlmann, and N. Keenlyside, 2006: A review of predictability studies of Atlantic sector climate on decadal scales. J. Climate, 19 , 59715987.

    • Search Google Scholar
    • Export Citation
  • Leith, C. E., 1978: Predictability of climate. Nature, 276 , 352355. doi:10.1038/276352a0.

  • Lorenz, E. N., 1963: Deterministic non-periodic flow. J. Atmos. Sci., 20 , 130141.

  • Majda, A., R. Abramov, and M. Grote, 2005: Information Theory and Stochastics for Multiscale Nonlinear Systems. American Mathematical Society, 133 pp.

    • Search Google Scholar
    • Export Citation
  • Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific decadal climate oscillation with impacts on salmon. Bull. Amer. Meteor. Soc., 78 , 10691079.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and Coauthors, 2006a: Climate change projections for the twenty-first century and climate change commitment in the CCSM3. J. Climate, 19 , 25972626.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., H. Teng, and G. Branstator, 2006b: Future change of El Niño in two global coupled climate models. Climate Dyn., 26 , 549566. doi:10.1007/s00382-005-0098-0.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and Coauthors, 2009: Decadal prediction: Can it be skillful? Bull. Amer. Meteor. Soc., 90 , 14671485.

  • Newman, M., 2007: Interannual to decadal predictability of tropical and North Pacific sea surface temperatures. J. Climate, 20 , 23332356.

    • Search Google Scholar
    • Export Citation
  • Pohlmann, H., and Coauthors, 2004: Estimating the decadal predictability of a coupled AOGCM. J. Climate, 17 , 44634472.

  • Pohlmann, H., J. H. Jungclaus, A. Kohl, D. Stammer, and J. Marotzke, 2009: Initialized decadal climate predictions with the GECCO oceanic synthesis: Effects on the North Atlantic. J. Climate, 22 , 39263938.

    • Search Google Scholar
    • Export Citation
  • Saravanan, R., and J. C. McWilliams, 1998: Advective ocean–atmosphere interaction: An analytical stochastic model with implications for decadal variability. J. Climate, 11 , 165188.

    • Search Google Scholar
    • Export Citation
  • Schneider, N., A. J. Miller, and D. W. Pierce, 2002: Anatomy of North Pacific decadal variability. J. Climate, 15 , 586605.

  • Smith, D., and Coauthors, 2007: Improved surface temperature prediction for the coming decade from a global climate model. Science, 317 , 796799.

    • Search Google Scholar
    • Export Citation
  • Solomon, A., and Coauthors, 2011: Distinguishing the roles of natural and anthropogenically forced decadal climate variability: Implication for prediction. Bull. Amer. Meteor. Soc., in press.

    • Search Google Scholar
    • Export Citation
  • Sugiura, N., and Coauthors, 2009: Potential for decadal predictability in the North Pacific region. Geophys. Res. Lett., 36 , L20701. doi:10.1029/2009GL039787.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, cited. 2009: A summary of the CMIP5 experimental design. [Available online at http://www-pcmdi.llnl.gov/].

    • Search Google Scholar
    • Export Citation
  • Teng, H., and G. Branstator, 2010: Initial-value predictability of prominent modes of North Pacific subsurface temperature in a CGCM. Climate Dyn., doi:10.1007/s00382-010-0749-7, in press.

    • Search Google Scholar
    • Export Citation
  • Troccoli, A., and T. N. Palmer, 2007: Ensemble decadal predictions from analysed initial conditions. Philos. Trans. Roy. Soc., 365A , 21792191.

    • 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

Two Limits of Initial-Value Decadal Predictability in a CGCM

View More View Less
  • 1 National Center for Atmospheric Research, Boulder, Colorado
Restricted access

Abstract

When the climate system experiences time-dependent external forcing (e.g., from increases in greenhouse gas and aerosol concentrations), there are two inherent limits on the gain in skill of decadal climate predictions that can be attained from initializing with the observed ocean state. One is the classical initial-value predictability limit that is a consequence of the system being chaotic, and the other corresponds to the forecast range at which information from the initial conditions is overcome by the forced response. These limits are not caused by model errors; they correspond to limits on the range of useful forecasts that would exist even if nature behaved exactly as the model behaves. In this paper these two limits are quantified for the Community Climate System Model, version 3 (CCSM3), with several 40-member climate change scenario experiments. Predictability of the upper-300-m ocean temperature, on basin and global scales, is estimated by relative entropy from information theory. Despite some regional variations, overall, information from the ocean initial conditions exceeds that from the forced response for about 7 yr. After about a decade the classical initial-value predictability limit is reached, at which point the initial conditions have no remaining impact. Initial-value predictability receives a larger contribution from ensemble mean signals than from the distribution about the mean. Based on the two quantified limits, the conclusion is drawn that, to the extent that predictive skill relies solely on upper-ocean heat content, in CCSM3 decadal prediction beyond a range of about 10 yr is a boundary condition problem rather than an initial-value problem. Factors that the results of this study are sensitive and insensitive to are also discussed.

Corresponding author address: Grant Branstator, NCAR, 1850 Table Mesa Dr., Boulder, CO 80305. Email: branst@ucar.edu

Abstract

When the climate system experiences time-dependent external forcing (e.g., from increases in greenhouse gas and aerosol concentrations), there are two inherent limits on the gain in skill of decadal climate predictions that can be attained from initializing with the observed ocean state. One is the classical initial-value predictability limit that is a consequence of the system being chaotic, and the other corresponds to the forecast range at which information from the initial conditions is overcome by the forced response. These limits are not caused by model errors; they correspond to limits on the range of useful forecasts that would exist even if nature behaved exactly as the model behaves. In this paper these two limits are quantified for the Community Climate System Model, version 3 (CCSM3), with several 40-member climate change scenario experiments. Predictability of the upper-300-m ocean temperature, on basin and global scales, is estimated by relative entropy from information theory. Despite some regional variations, overall, information from the ocean initial conditions exceeds that from the forced response for about 7 yr. After about a decade the classical initial-value predictability limit is reached, at which point the initial conditions have no remaining impact. Initial-value predictability receives a larger contribution from ensemble mean signals than from the distribution about the mean. Based on the two quantified limits, the conclusion is drawn that, to the extent that predictive skill relies solely on upper-ocean heat content, in CCSM3 decadal prediction beyond a range of about 10 yr is a boundary condition problem rather than an initial-value problem. Factors that the results of this study are sensitive and insensitive to are also discussed.

Corresponding author address: Grant Branstator, NCAR, 1850 Table Mesa Dr., Boulder, CO 80305. Email: branst@ucar.edu

Save