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Uncertainty in predictions of the climate response to rising levels of greenhouse gases

Nature volume 433, pages 403–406 (2005)Cite this article

Abstract

The range of possibilities for future climate evolution1,2,3 needs to be taken into account when planning climate change mitigation and adaptation strategies. This requires ensembles of multi-decadal simulations to assess both chaotic climate variability and model response uncertainty4,5,6,7,8,9. Statistical estimates of model response uncertainty, based on observations of recent climate change10,11,12,13, admit climate sensitivities—defined as the equilibrium response of global mean temperature to doubling levels of atmospheric carbon dioxide—substantially greater than 5 K. But such strong responses are not used in ranges for future climate change14 because they have not been seen in general circulation models. Here we present results from the ‘climateprediction.net’ experiment, the first multi-thousand-member grand ensemble of simulations using a general circulation model and thereby explicitly resolving regional details15,16,17,18,19,20,21. We find model versions as realistic as other state-of-the-art climate models but with climate sensitivities ranging from less than 2 K to more than 11 K. Models with such extreme sensitivities are critical for the study of the full range of possible responses of the climate system to rising greenhouse gas levels, and for assessing the risks associated with specific targets for stabilizing these levels.

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Figure 1: Frequency distributions of Tg (colours indicate density of trajectories per 0.1 K interval) through the three phases of the simulation.
Figure 2: The response to parameter perturbations.
Figure 3: The temperature (left panels) and precipitation (right panels) anomaly fields in response to doubling the CO2 concentrations.

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References

  1. Schneider, S. H. What is dangerous climate change? Nature 411, 17–19 (2001)

    Article  ADS  CAS  Google Scholar 

  2. Reilly, J. et al. Uncertainty in climate change assessments. Science 293, 430–433 (2001)

    Article  CAS  Google Scholar 

  3. Wigley, T. M. L. & Raper, S. L. Interpretation of high projections for global mean warming. Science 293, 451–454 (2001)

    Article  ADS  CAS  Google Scholar 

  4. Allen, M. R. & Stainforth, D. A. Towards objective probabilistic climate forecasting. Nature 419, 228 (2002)

    Article  ADS  CAS  Google Scholar 

  5. Allen, M. R. & Ingram, W. J. Constraints on future changes in climate and the hydrological cycle. Nature 419, 224–232 (2002)

    Article  ADS  CAS  Google Scholar 

  6. Palmer, T. N. Predicting uncertainty in forecasts of weather and climate. Rep. Prog. Phys. 63, 71–116 (2000)

    Article  ADS  Google Scholar 

  7. Smith, L. What might we learn from climate forecasts? Proc. Natl Acad. Sci. USA 99, 2487–2492 (2002)

    Article  ADS  Google Scholar 

  8. Kennedy, M. C. & O'Hagan, A. Bayesian calibration of computer models. J. R. Stat. Soc. Ser. B Stat. Methodol. 63, 425–450 (2001)

    Article  MathSciNet  Google Scholar 

  9. Murphy, J. M. et al. Quantifying uncertainties in climate change from a large ensemble of general circulation model predictions. Nature 430, 768–772 (2004)

    Article  ADS  CAS  Google Scholar 

  10. Knutti, R., Stocker, T. F., Joos, F. & Plattner, G. K. Constraints on radiative forcing and future climate change from observations and climate model ensembles. Nature 416, 719–723 (2002)

    Article  ADS  CAS  Google Scholar 

  11. Forest, C. E. et al. Quantifying uncertainties in climate system properties with the use of recent climate observations. Science 295, 113–117 (2002)

    Article  ADS  MathSciNet  CAS  Google Scholar 

  12. Allen, M. R., Stott, P. A., Mitchell, J. F. B., Schnur, R. & Delworth, T. L. Quantifying the uncertainty in forecasts of anthropogenic climate change. Nature 417, 617–620 (2000)

    Article  ADS  Google Scholar 

  13. Stott, P. A. & Kettleborough, J. A. Origins and estimates of uncertainty in predictions of twenty-first century temperature rise. Nature 416, 723–726 (2002)

    Article  ADS  CAS  Google Scholar 

  14. Cubasch, U. et al. in Climate Change 2001, The Science of Climate Change (ed. Houghton, J. T.) Ch. 9 527–582 (Cambridge Univ. Press, Cambridge, 2001)

    Google Scholar 

  15. Allen, M. R. Do-it-yourself climate prediction. Nature 401, 627 (1999)

    Article  Google Scholar 

  16. Stainforth, D. et al. Distributed computing for public-interest climate modeling research. Comput. Sci. Eng. 4, 82–89 (2002)

    Article  Google Scholar 

  17. Hansen, J. A. et al. Casino-21: Climate simulation of the 21st century. World Res. Rev. 13, 187–189 (2001)

    ADS  Google Scholar 

  18. Stainforth, D. et al. Climateprediction.net: Design principles for public-resource modeling. In 14th IASTED Int. Conf. on Parallel And Distributed Computing And Systems (4–6 November 2002) 32–38 (ACTA Press, Calgary, 2002)

    Google Scholar 

  19. Stainforth, D. et al. in Environmental Online Communication (ed. Scharl, A.) Ch. 12 (Springer, London, 2004)

    Google Scholar 

  20. Giorgi, F. & Francisco, R. Evaluating uncertainties in the prediction of regional climate change. Geophys. Res. Lett. 27, 1295–1298 (2000)

    Article  ADS  Google Scholar 

  21. Palmer, T. N. & Raisanen, J. Quantifying the risk of extreme seasonal precipitation events in a changing climate. Nature 415, 512–517 (2002)

    Article  CAS  Google Scholar 

  22. Collins, M. & Allen, M. R. Assessing the relative roles of initial and boundary conditions in interannual to decadal climate predictability. J. Clim. 15, 3104–3109 (2002)

    Article  ADS  Google Scholar 

  23. Pope, V. D., Gallani, M., Rowntree, P. R. & Stratton, R. A. The impact of new physical parameterisations in the Hadley Centre climate model – HadAM3. Clim. Dyn. 16, 123–146 (2000)

    Article  Google Scholar 

  24. Gregory, J. M. et al. An observationally-based estimate of the climate sensitivity. J. Clim. 15, 3117–3121 (2002)

    Article  ADS  Google Scholar 

  25. Andronova, N. G. & Schlesinger, M. E. Objective estimation of the probability density function for climate sensitivity. J. Geophys. Res. 106, 22605–22612 (2001)

    Article  ADS  Google Scholar 

  26. Morgan, M. G. & Keith, D. W. Climate-change — subjective judgments by climate experts. Environ. Sci. Technol. 29, 468–476 (1995)

    ADS  Google Scholar 

  27. Smith, L. A. in Disentangling Uncertainty and Error: On the Predictability of Nonlinear Systems (ed. Mees, A. I.) Ch. 2 (Birkhauser, Boston, 2000)

    Google Scholar 

  28. Covey, C. et al. An overview of results from the coupled model intercomparison project. Glob. Planet. Change 37(1–2), 103–133 (2003)

    Article  ADS  Google Scholar 

  29. McAveney, B. J. et al. in Climate Change 2001, The Science of Climate Change (ed. Houghton, J. T.) Ch. 8, 471–524 (Cambridge Univ. Press, Cambridge, 2001)

    Google Scholar 

  30. Williams, K. D., Senior, C. A. & Mitchell, J. F. B. Transient climate change in the Hadley centre models: the role of physical processes. J. Clim. 14, 2659–2674 (2001)

    Article  ADS  Google Scholar 

Download references

Acknowledgements

We thank all participants in the ‘climateprediction.net’ experiment and the many individuals who have given their time to make the project a reality and a success. This work was supported by the Natural Environment Research Council's COAPEC, e-Science and fellowship programmes, the UK Department of Trade and Industry, the UK Department of the Environment, Food and Rural Affairs, and the US National Oceanic and Atmospheric Administration. We also thank Tessella Support Services plc, Research Systems Inc., Numerical Algorithms Group Ltd, Risk Management Solutions Inc. and the CMIP II modelling groups.

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Authors and Affiliations

  1. Department of Physics, University of Oxford, Parks Road, OX1 3PU, Oxford, UK

    D. A. Stainforth, T. Aina, N. Faull, D. J. Frame, S. Knight, C. Piani & M. R. Allen

  2. Computing Laboratory, University of Oxford, Parks Road, OX1 3QD, Oxford, UK

    C. Christensen & A. Martin

  3. Hadley Centre for Climate Prediction and Research, Met Office, EX1 3PB, Exeter, UK

    M. Collins, J. M. Murphy & D. Sexton

  4. Rutherford Appleton Laboratory, Chilton, Oxfordshire, OX11 0QX, UK

    J. A. Kettleborough

  5. London School of Economics, WC2A 2AE, London, UK

    L. A. Smith

  6. Department of Earth Sciences, The Open University, Milton Keynes, MK7 6AA, UK

    R. A. Spicer

  7. Department of Meteorology, University of Reading, RG6 6BB, Reading, UK

    A. J. Thorpe

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  1. D. A. Stainforth
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Corresponding author

Correspondence to D. A. Stainforth.

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Supplementary information

Supplementary Discussion

Discussion of the PC based model and how the control climates of different model versions compare with observations. Also, discussion of the mechanism which can lead to unphysical cooling in a model with a mixed layer ocean. Finally, some comments on the sensitivity of results to the choice of perturbations. (DOC 30 kb)

Supplementary Figure Legends

Legends for the supplementary figures. (DOC 24 kb)

Supplementary Figure 1

Comparison of annual mean temperature and precipitation fields with observations for: i) the super computer version of HadSM3, ii) the unperturbed climateprediction.net model version, iii) a low sensitivity model version, and iv) a high sensitivity model version. (PDF 444 kb)

Supplementary Figure 2

The difference in annual mean surface temperature between control and calibration phases for a stable and an unstable simulation; the latter exhibiting the cooling problem described in the supplementary discussion. (PDF 110 kb)

Supplementary Figure 3

Temperature and temperature anomaly fields from a simulation exhibiting the cooling problem in the double CO2 phase. (PDF 132 kb)

Supplementary Figure 4

Further exploration of the sensitivity of the simulated climate sensitivity distribution to the choice of parameter perturbations. (PDF 14 kb)

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Stainforth, D., Aina, T., Christensen, C. et al. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433, 403–406 (2005). https://doi.org/10.1038/nature03301

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