Climate Dynamics

, Volume 23, Issue 1, pp 29–44 | Cite as

Long time-scale potential predictability in an ensemble of coupled climate models

  • G. J. BoerEmail author


A “diagnostic multi-model ensemble potential predictability study” of surface air temperature is performed using data from nine models participating in the Coupled Model Intercomparison Project (CMIP1). The data are considered to be a sample of results from the population of models “embodying current abilities to simulate the climate system” and represent a range of numerics, resolution and of physical parametrizations. The potential predictability of pentadal, decadal, and 25-year means is analyzed. The multi-model ensemble provides a statistically stable estimate of the potential predictability variance fraction (ppvf) with a narrow confidence interval. This is not the case for individual models with modest lengths of simulation data nor, by implication, for the instrument-based observational record. Potential predictability is found predominately over the high-latitude oceans. There is evidence also for potential predictability at tropical latitudes in the Pacific and Atlantic, but not the Indian oceans, on the shorter of the time scales. The potential predictability variance fraction decreases with increasing time scale but appreciable values exist at all of the time scales considered, especially for the Southern Ocean and for the North Atlantic. Values over land, while statistically non-zero, are small. The autocorrelation structure of the data is investigated to account for its effect on the statistical estimation of the ppvf and to indicate the extent to which the data reflect simple oceanic damping of white noise atmospheric forcing. Ensemble autocorrelation structures differ between tropical and extra-tropical latitudes (at least on the time scales considered) with more oscillatory behaviour implied in tropical regions compared to high latitudes. It appears that the results are inconsistent with simple ocean damping and that higher order autocorrelation structures of temperature cannot be neglected generally or in the determination of the potential predictability. The statistical results suggest that predictability in the extratropics is associated with long ocean time scales while in the tropics it is associated with the coupled atmosphere-ocean system. Physically based analyses are required to understand this long time scale behaviour and an “ensemble” view is also needed in order to determine the behaviour that is robust across models and the real system.


Potential Predictability CMIP1 Model Confidence Band Predictive Skill Time Scale Variance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Thanks to Steve Lambert for downloading the CMIP1 data and casting it into our standard format, to Greg Flato for comments, to Francis Zwiers for statistical advice, and to Slava Kaharin for both advice and comments.


  1. Anderson JH, van den Dool H, Barnston A, Chen W, Stern W, Ploshay J (1999) Present- day capabilities of numerical and statistical models for atmospheric extratropical seasonal simulation and prediction. Bull Am Meterol Soc 80: 1349–1362CrossRefGoogle Scholar
  2. Boer GJ (2000) A study of atmosphere-ocean predictability on long time scales. Clim Dyn 16: 469–477CrossRefGoogle Scholar
  3. Boer GJ, Flato G, Reader MC, Ramsden D (2000a) A transient climate change simulation with greenhouse gas and aerosol forcing: experimental design and comparison with the instrumental record for the twentieth century. Clim Dyn 16: 405–425CrossRefGoogle Scholar
  4. Boer GJ, Flato G, Ramsden D (2000b) A transient climate change simulation with greenhouse gas and aerosol forcing: projected climate for the twentyfirst century. Clim Dyn 16: 427–450CrossRefGoogle Scholar
  5. Boville BA, Gent PR (1998) The NCAR Climate System Model, Version One. J Clim 11: 1115–1130CrossRefGoogle Scholar
  6. Collins M (2002) Climate predictability on interannual to decadal time scales: the initial value problem. Clim Dyn 19: 671–692CrossRefGoogle Scholar
  7. Collins M, Sinha B (2003) Predictability of decadal variations in the thermohaline circulation and climate. Geophys Res Lett 30: 1306CrossRefGoogle Scholar
  8. Covey C (1998) CMIP1 model output. At http://www-pcmdillnlgov/cmip/diagsubhtml#CMIP1 model output. Program for Climate Model Diagnosis and Intercomparison (PCMDI) Lawrence Livermore National Laboratory Livermore CaliforniaGoogle Scholar
  9. CLIVAR (1995) CLIVAR, A study of climate variability and predictability: Science Plan. World Climate Research Programme Report WCRP-89 WMO/TD 690 WMO GenevaGoogle Scholar
  10. Crow EL, Davis FA, Maxfield WM (1960) Statistics manual. Dover Publications New York, pp 288Google Scholar
  11. Delworth T, Manabe S, Stouffer R (1993) Interdecadal variations of the thermohaline circulation in a coupled ocean-atmosphere model. J Clim 6: 1993–2011CrossRefGoogle Scholar
  12. Derome J, Brunet G, Plante A, Gagnon N, Boer GJ et al. (2001) Seasonal predictions based on two dynamical models. Atmos-Ocean 39: 485–501Google Scholar
  13. Flato GM, Boer GJ (2001) Warming asymmetry in climate change simulations. Geophys Res Lett 28: 195–198CrossRefGoogle Scholar
  14. Flato GM, Boer GJ, Lee W, McFarlane N, Ramsden D, Weaver A (2000) The CCCma global coupled model and its climate. Clim Dyn 16: 451–467CrossRefGoogle Scholar
  15. Gates WL, Cubasch U, Meehl GA, Mitchell JFB, Stouffer RJ (1993) An intercomparison of selected features of the control climates simulated by coupled ocean-atmosphere general circulation models. World Climate Research Programme WCRP-82 WMO/TD 574 WMO GenevaGoogle Scholar
  16. Griffies SM, Bryan K (1997) A predictability study of simulated North Atlantic multidecadal variability. Clim Dyn 13: 459–487CrossRefGoogle Scholar
  17. Gordon HB, O’Farrell SP (1997) Transient climate change in the CSIRO coupled model with dynamic sea ice. Mon Weather Rev 125: 875–907CrossRefGoogle Scholar
  18. Hasselmann K (1976) Stochastic climate models Part I: theory. Tellus 28: 473–485Google Scholar
  19. Hildebrand FB (1956) Introduction to numerical analysis. McGraw-Hill, pp 511Google Scholar
  20. IPCC (1995) Climate change 1995, the science of climate change. Cambridge University Press Cambridge, UKGoogle Scholar
  21. IPCC (2001) Climate change 2001; the scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UKGoogle Scholar
  22. Johns TC (1996) A description of the second Hadley Centre Coupled Model (HadCM2). Climate Research Technical Note 71. Hadley Centre, Bracknell Berkshire, pp 19Google Scholar
  23. Johns TC, Carnell RE, Crossley JF, Gregory JM, Mitchell J, Senior C, Tett S, Wood R (1997) The second Hadley Centre coupled ocean-atmosphere GCM: model description, spinup and validation. Clim Dyn 13: 103–134CrossRefGoogle Scholar
  24. Jones PD (1994) Hemispheric surface air temperature variations: a reanalysis and an update to 1993. J Clim 7: 1794–1802CrossRefGoogle Scholar
  25. Kirtman B (ed) (2003) Experimental long lead forecast bulletin. Published by the Centre for Ocean-Land-Atmosphere Studies. Calverton Maryland. Available also at http://gradsigesorg/colahtmlGoogle Scholar
  26. Lambert SJ, Boer GJ (2001) CMIP1 evaluation and intercomparison of coupled climate models. Clim Dyn 17: 83–106Google Scholar
  27. Latif M (1998) Dynamics of interdecadal variability in coupled ocean-atmosphere models. J Clim 11: 602–624CrossRefGoogle Scholar
  28. Madden RA (1976) Estimates of natural variability of time-averaged sea level pressure. Mon Weather Rev 104: 942–952CrossRefGoogle Scholar
  29. Manabe S, Stouffer RJ(1996) Low-frequency variability of surface air temperature in a 1000-year integration of a coupled atmosphere-ocean-land surface model. J Clim 9: 376–393CrossRefGoogle Scholar
  30. Manabe S, Stouffer RJ, Spelman JM, Bryan K(1991) Transient responses of a coupled ocean-atmosphere model to gradual changes of atmospheric CO2 Part I: annual mean response. J Clim 4: 785–818CrossRefGoogle Scholar
  31. Meehl GA, Washington WM (1995) Cloud albedo feedback and the super greenhouse effect in a global coupled GCM. Clim Dyn 11: 299–411CrossRefGoogle Scholar
  32. Meehl GA, Boer GJ, Covey C, Latif M, Stouffer RJ (1997) Intercomparison makes for a better climate model EOS 78: 445–451Google Scholar
  33. Parker DE, Folland CK, Jackson M (1995) Marine surface temperature: observed variations and data requirements. Clim Change 31: 559–600Google Scholar
  34. Phillips T (1998) Summary documentation: CMIP I model features and experimental implementation (Version 10). At http://www-pcmdillnlgov/modeldoc/cmip/indexhtml. Program for Climate Model Diagnosis and Intercomparison (PCMDI). Lawrence Livermore National Laboratory, Livermore, California, USAGoogle Scholar
  35. Power SB, Colman RA, McAvaney BJ, Dahni RR, Moore AM, Smith NR (1993) The BMRC Coupled atmosphere/ocean/sea-ice model. BMRC Research Report 37. Bureau of Meteorology Research Centre, Melbourne, Australia, pp 58 Google Scholar
  36. Räisänen J (2001) CO2-induced climate change in CMIP2 experiments: quantification of agreement and role of internal variability. J Clim 14: 2088–2104CrossRefGoogle Scholar
  37. Roeckner E, Oberhuber J, Bacher A, Christoph M, Kirchner I (1996) ENSO variability and atmospheric response in a global coupled atmosphere-ocean GCM. Clim Dyn 12: 737–754CrossRefGoogle Scholar
  38. Rowell DP (1998) Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations. J Clim 11: 109–120CrossRefGoogle Scholar
  39. Rowell DP, Zwiers F (1999) The global distribution of sources of atmospheric decadal variability and mechanisms over the tropical Pacific and southern North America. Clim Dyn 15: 751–772CrossRefGoogle Scholar
  40. Schnneider EK, Zhu Z (1998) Sensitivity of the simulated annual cycle of sea surface temperature in the equitorial pacific to sunlight penetration. J Clim 11: 1932–1950Google Scholar
  41. Schnneider EK, Zhu Z, Giese B, Huang B, Kirtman BP, Shukla J, Carton JA (1997) Annual cycle and ENSO in a coupled ocean-atmosphere general circulation model. Mon Weather Rev 125: 680–702CrossRefGoogle Scholar
  42. Timmermann A, Latif M, Voss R, Grotzner A(1998) Northern Hemispheric interdecadal variability: a coupled air-sea mode. J Clim 11: 1906–1931Google Scholar
  43. Tokioka T, Noda A, Kitoh A, Nikaidou Y, Nakagawa S, Motoi T, Yukimoto S, Takata K (1996) A transient CO2 experiment with the MRI CGCM: annual mean response. CGER’s Supercomputer Monograph Report vol 2. Centre for Global Environmental Research National Institute for Environmental Studies, Ibaraki, Japan, pp 86Google Scholar
  44. von Storch H, Zwiers F (1999) Statistical analysis in climate research. Cambridge University Press, Cambridge UK pp 484Google Scholar
  45. Voss R, Sausen R, Cubasch U (1998) Periodically synchronously coupled integrations with the atmosphere-ocean general circulation model ECHAM3/LSG. Clim Dyn 14: 249–266CrossRefGoogle Scholar
  46. Washington WM, Meehl GA (1996) High-latitude climate change in a global coupled ocean-atmosphere-sea ice model with increased atmospheric CO2. J Geophys Res 101(D8): 12,795–12,801CrossRefGoogle Scholar
  47. Zwiers F (1996) Interannual variability and predictability in an ensemble of AMIP climate simulations conducted with the CCC GCM2. Clim Dyn 12: 825–847CrossRefGoogle Scholar

Copyright information

© Springer-Verlag  2004

Authors and Affiliations

  1. 1.Canadian Centre for Climate Modelling and Analysis, Meteorological Service of CanadaUniversity of VictoriaVictoriaCanada

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