• Brohan, P., J. Kennedy, I. Harris, S. Tett, and P. 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
  • Cochrane, D., and G. Orcutt, 1949: Application of least squares regression to relationships containing auto-correlated error terms. J. Amer. Stat. Assoc., 44 , 3261.

    • 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
  • Keenlyside, N., M. Latif, J. Jungclaus, L. Kornblueh, and E. Roeckner, 2008: Advancing decadal-scale climate prediction in the North Atlantic sector. Nature, 453 (7191) 8488.

    • Search Google Scholar
    • Export Citation
  • Krueger, O., 2009: A simple empirical model for decadal climate prediction. Diplomarbeit, Meteorological Institute, University of Hamburg, 99 pp. [Available from Meteorological Institute, University of Hamburg, Bundesstr. 55, 20146 Hamburg, Germany].

    • Search Google Scholar
    • Export Citation
  • Laepple, T., S. Jewson, and K. Coughlin, 2008: Interannual temperature predictions using the CMIP3 multi-model ensemble mean. Geophys. Res. Lett., 35 , L10701. doi:10.1029/2008GL033576.

    • 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 time scales. J. Climate, 19 , 59715987.

    • Search Google Scholar
    • Export Citation
  • Lean, J. L., and D. H. Rind, 2008: How natural and anthropogenic influences alter global and regional surface temperatures: 1889 to 2006. Geophys. Res. Lett., 35 , L18701. doi:10.1029/2008GL034864.

    • Search Google Scholar
    • Export Citation
  • Lean, J. L., and D. H. Rind, 2009: How will earth’s surface temperature change in future decades. Geophys. Res. Lett., 36 , L15708. doi:10.1029/2009GL038932.

    • Search Google Scholar
    • Export Citation
  • Lee, T., F. Zwiers, X. Zhang, and M. Tsao, 2006: Evidence of decadal climate prediction skill resulting from changes in anthropogenic forcing. J. Climate, 19 , 53055318.

    • Search Google Scholar
    • Export Citation
  • Livezey, R., 1999: The evaluation of forecasts. Analysis of Climate Variability: Applications of Statistical Techniques, H. von Storch and A. Navarra, Eds., Springer Verlag, 177–196.

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

    • Search Google Scholar
    • Export Citation
  • Meehl, G., W. Washington, W. Collins, J. Arblaster, A. Hu, L. Buja, W. Strand, and H. Teng, 2005: How much more global warming and sea level rise? Science, 307 (5716),. 1769–1772.

    • Search Google Scholar
    • Export Citation
  • Meehl, G., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. Mitchell, R. Stouffer, and K. Taylor, 2007: The WCRP CMIP3 multimodel dataset. Bull. Amer. Meteor. Soc., 88 , 13831394.

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

  • Nakicenovic, N., and Coauthors, 2000: Special Report on Emissions Scenarios. Cambridge University Press, 612 pp.

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

    • Search Google Scholar
    • Export Citation
  • Smith, D., S. Cusack, A. Colman, C. Folland, G. Harris, and J. Murphy, 2007: Improved surface temperature prediction for the coming decade from a global climate model. Science, 317 (5839) 796799.

    • Search Google Scholar
    • Export Citation
  • Taylor, K., R. Stouffer, and G. Meehl, cited. 2008: A summary of the CMIP5 experiment design. [Available online at http://www.clivar.org/organization/wgcm/references/Taylor_CMIP5.pdf].

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and D. J. Shea, 2006: Atlantic hurricanes and natural variability in 2005. Geophys. Res. Lett., 33 , L12704. doi:10.1029/2006GL026894.

    • Search Google Scholar
    • Export Citation
  • van Oldenborgh, G., M. Balmaseda, L. Ferranti, T. Stockdale, and D. Anderson, 2005: Evaluation of atmospheric fields from the ECMWF seasonal forecasts over a 15-yr period. J. Climate, 18 , 32503269.

    • Search Google Scholar
    • Export Citation
  • van Oldenborgh, G., S. Drijfhout, A. van Ulden, R. Haarsma, A. Sterl, C. Severijns, W. Hazeleger, and H. Dijkstra, 2009: Western Europe is warming much faster than expected. Climate Past, 5 , 112.

    • Search Google Scholar
    • Export Citation
  • von Storch, J., 2008: Toward climate prediction: Interannual potential predictability due to an increase in CO2 concentration as diagnosed from an ensemble of AOGCM integrations. J. Climate, 21 , 46074628.

    • Search Google Scholar
    • Export Citation
  • Zwiers, F., 2002: The 20-year forecast. Nature, 416 (6882) 690691.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 205 75 4
PDF Downloads 128 42 2

A Simple Empirical Model for Decadal Climate Prediction

View More View Less
  • 1 Institute for Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany
  • | 2 Max Planck Institute for Meteorology, Hamburg, Germany
Restricted access

Abstract

Decadal climate prediction is a challenging aspect of climate research. It has been and will be tackled by various modeling groups. This study proposes a simple empirical forecasting system for the near-surface temperature that can be used as a benchmark for climate predictions obtained from atmosphere–ocean GCMs (AOGCMs). It is assumed that the temperature time series can be decomposed into components related to external forcing and internal variability. The considered external forcing consists of the atmospheric CO2 concentration. Separation of the two components is achieved by using the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) twentieth-century integrations. Temperature anomalies due to changing external forcing are described by a linear regression onto the forcing. The future evolution of the external forcing that is needed for predictions is approximated by a linear extrapolation of the forcing prior to the initial time. Temperature anomalies owing to the internal variability are described by an autoregressive model. An evaluation of hindcast experiments shows that the empirical model has a cross-validated correlation skill of 0.84 and a cross-validated rms error of 0.12 K in hindcasting global-mean temperature anomalies 10 years ahead.

Corresponding author address: Oliver Krueger, Institute for Coastal Research, Helmholtz-Zentrum Geesthacht, Max-Planck-Str. 1, 21502 Geesthacht, Germany. Email: oliver.krueger@hzg.de

Abstract

Decadal climate prediction is a challenging aspect of climate research. It has been and will be tackled by various modeling groups. This study proposes a simple empirical forecasting system for the near-surface temperature that can be used as a benchmark for climate predictions obtained from atmosphere–ocean GCMs (AOGCMs). It is assumed that the temperature time series can be decomposed into components related to external forcing and internal variability. The considered external forcing consists of the atmospheric CO2 concentration. Separation of the two components is achieved by using the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) twentieth-century integrations. Temperature anomalies due to changing external forcing are described by a linear regression onto the forcing. The future evolution of the external forcing that is needed for predictions is approximated by a linear extrapolation of the forcing prior to the initial time. Temperature anomalies owing to the internal variability are described by an autoregressive model. An evaluation of hindcast experiments shows that the empirical model has a cross-validated correlation skill of 0.84 and a cross-validated rms error of 0.12 K in hindcasting global-mean temperature anomalies 10 years ahead.

Corresponding author address: Oliver Krueger, Institute for Coastal Research, Helmholtz-Zentrum Geesthacht, Max-Planck-Str. 1, 21502 Geesthacht, Germany. Email: oliver.krueger@hzg.de

Save