Quantifying the uncertainty in forecasts of anthropogenic climate change

Abstract

Forecasts of climate change are inevitably uncertain. It is therefore essential to quantify the risk of significant departures from the predicted response to a given emission scenario. Previous analyses of this risk have been based either on expert opinion1, perturbation analysis of simplified climate models2,3,4,5 or the comparison of predictions from general circulation models6. Recent observed changes that appear to be attributable to human influence7,8,9,10,11,12 provide a powerful constraint on the uncertainties in multi-decadal forecasts. Here we assess the range of warming rates over the coming 50 years that are consistent with the observed near-surface temperature record as well as with the overall patterns of response predicted by several general circulation models. We expect global mean temperatures in the decade 2036–46 to be 1–2.5 K warmer than in pre-industrial times under a ‘business as usual’ emission scenario. This range is relatively robust to errors in the models' climate sensitivity, rate of oceanic heat uptake or global response to sulphate aerosols as long as these errors are persistent over time. Substantial changes in the current balance of greenhouse warming and sulphate aerosol cooling would, however, increase the uncertainty. Unlike 50-year warming rates, the final equilibrium warming after the atmospheric composition stabilizes remains very uncertain, despite the evidence provided by the emerging signal.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Predicted anthropogenic warming by about 2040 under the IS92a scenario before and after reconciling model simulations with the recent climate record.
Figure 2: Transfer functions relating past and future climate change in a simple climate model.
Figure 3: Global temperatures under IS92a consistent with the recent climate record.
Figure 4: Forecast anthropogenic warming 1996 to 2046 under IS92a.

References

  1. 1

    Morgan, M. G. & Keith, D. W. Subjective judgements by climate experts. Environ. Policy Anal. 29, 468– 476 (1995).

    Google Scholar 

  2. 2

    Hansen, J. et al. Climate response times: dependence on climate sensitivity and ocean mixing. Science 229, 857– 859 (1985).

    ADS  CAS  Article  Google Scholar 

  3. 3

    Raper, S. C. B., Wigley, T. M. L. & Warrick, R. A. in Rising Sea Level and Subsiding Coastal Areas (eds Millman, J. D. & Haq, B. U.) 11–45 (Kluwer Academic, Norwell, Massachusetts, 1996).

    Google Scholar 

  4. 4

    Wigley, T. M. L., Jones, P. D. & Raper, S. C. B. The observed global warming record: What does it tell us? Proc. Natl Acad. Sci. 94, 8314 –8320 (1997).

    ADS  CAS  Article  Google Scholar 

  5. 5

    Forest, C. E., Allen, M. R., Stone, P. H. & Sokolov, A. P. Constraining uncertainties in climate models using climate change detection techniques. Geophys. Res. Lett. 27, 569– 572 (2000).

    ADS  CAS  Article  Google Scholar 

  6. 6

    Meehl, G. A., Boer, G. J., Covey, C., Latif, M. & Stouffer, R. J. The Coupled Model Intercomparison project (CMIP). Bull. Am. Meteorol. Soc. (in the press).

  7. 7

    Santer, B. D. et al. A search for human influences on the thermal structure of the atmosphere. Nature 382, 39– 46 (1996).

    ADS  CAS  Article  Google Scholar 

  8. 8

    Hegerl, G. C. et al. Detecting greenhouse gas-induced climate change with an optimal fingerprint method. J. Clim. 9, 2281– 2306 (1996).

    ADS  Article  Google Scholar 

  9. 9

    Hegerl, G. et al. On multi-fingerprint detection and attribution of greenhouse gas and aerosol forced climate change. Clim. Dyn. 13 , 613–634 (1997).

    Article  Google Scholar 

  10. 10

    North, G. R. & Stevens, M. J. Detecting climate signals in the surface temperature record. J. Clim. 11, 563–577 (1998).

    ADS  Article  Google Scholar 

  11. 11

    Tett, S. F. B., Stott, P. A., Allen, M. R., Ingram, W. J. & Mitchell, J. F. B. Causes of twentieth century temperature change near the Earth's surface. Nature 399, 569–572 (1999).

    ADS  CAS  Article  Google Scholar 

  12. 12

    Johns, T. C. The Second Hadley Centre coupled ocean-atmosphere GCM: model description, spin-up and validation. Clim. Dyn. 13, 103 –134 (1997).

    Article  Google Scholar 

  13. 13

    Voss, R., Sausen, R. & Cubasch, U. Periodically synchronously coupled integrations with the atmosphere-ocean general circulation model ECHAM3/LSG. Clim. Dyn. 14, 249–266 ( 1998).

    Article  Google Scholar 

  14. 14

    Röckner, E., Bengtsson, L., Feichter, J., Lelieveld, J. & Rodhe, H. Transient climate change simulations with a coupled atmosphere-ocean gcm including the tropospheric sulfur cycle. J. Clim. 12, 3004–3032 (1999).

    ADS  Article  Google Scholar 

  15. 15

    Knutson, T. R., Delworth, T. L., Dixon, K. W. & Stouffer, R. J. Model assessment of regional surface temperature trends (1949–1997). J. Geophys. Res. 104, 30981– 30996 (1999).

    ADS  Article  Google Scholar 

  16. 16

    Mitchell, J. F. B., Johns, T. C., Gregory, J. M. & Tett, S. F. B. Climate response to increasing levels of greenhouse gases and sulphate aerosols. Nature 376, 501–504 (1995).

    ADS  CAS  Article  Google Scholar 

  17. 17

    Sokolov, A. P. & Stone, P. H. A flexible climate model for use in integrated assessments. Clim. Dyn. 14, 291–303 (1998).

    Article  Google Scholar 

  18. 18

    Allen, M. R. Do-it-yourself climate prediction. Nature 401, 642 (1999).

    ADS  Article  Google Scholar 

  19. 19

    Hasselmann, K. On multifingerprint detection and attribution of anthropogenic climate change. Clim. Dyn. 13, 601–611 (1997).

    Article  Google Scholar 

  20. 20

    Allen, M. R. & Tett, S. F. B. Checking internal consistency in optimal fingerprinting. Clim. Dyn. 15, 419–434 (1999).

    Article  Google Scholar 

  21. 21

    Stott, P. A. Attribution of twentieth century climate change to natural and anthropogenic causes. Clim. Dyn. (in the press).

  22. 22

    Wood, R. A., Keen, A. B., Mitchell, J. F. B. & Gregory, J. M. Changing spatial structure of the thermohaline circulation in response to atmospheric CO2 forcing in a climate model. Nature 399, 572–575 (1997).

    ADS  Article  Google Scholar 

  23. 23

    Mitchell, J. F. B. & Johns, T. C. On modification of global warming by sulphate aerosols. J. Clim. 10 , 245–266 (1997).

    ADS  Article  Google Scholar 

  24. 24

    Corti, S., Molteni, F. & Palmer, T. N. Signature of recent climate change in frequencies of natural atmospheric circulation regimes. Nature 398, 799–802 (1999).

    ADS  CAS  Article  Google Scholar 

  25. 25

    Delworth, T. L. & Knutson, T. R. Simulation of early 20th century global warming. Science 287, 2246–2250 (2000).

    ADS  CAS  Article  Google Scholar 

  26. 26

    Cubasch, U., Voss, R., Hegerl, G. C., Waszkewitz, J. & Crowley, T. J. Simulation of the influence of solar radiation variations on the global climate with an ocean-atmosphere general circulation model. Clim. Dyn. 13, 757–767 (1997).

    Article  Google Scholar 

  27. 27

    Cox, P. M., Betts, R. A., Jones, C. S., Spall, S. A. & Totterdell, I. J. Acceleration of global warming due to carbon-cycle feedbacks in a 3D coupled model. Nature (submitted).

  28. 28

    Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (eds Nakićenović, N. & Swart, R.) (Cambridge Univ. Press, 2000).

  29. 29

    Smith, S. J., Wigley, T. M. L., Nakicenovic, N. & Raper, S. C. B. Climate implications of greenhouse gas emissions scenarios. Technol. Forecast. Social Change (in the press).

  30. 30

    Stott, P. A. & Tett, S. F. B. Scale-dependent detection of climate change. J. Clim. 11, 3282– 3294 (1998).

    ADS  Article  Google Scholar 

  31. 31

    Ripley, B. D. & Thompson, M. Regression techniques for the detection of analytical bias. Analyst 112, 377– 383 (1987).

    ADS  CAS  Article  Google Scholar 

  32. 32

    van Huffel, S. & Vanderwaal, J. The Total Least Squares Problem: Computational Aspects and Analysis (Society for Industrial & Applied Mathematics, Philadelphia, 1991).

    Google Scholar 

Download references

Acknowledgements

We thank T. Barnett, C. Forest, N. Gillett, K. Hasselmann, G. Hegerl, W. Ingram, G. Jones, S. Raper, B. Ripley, S. Smith, A. Sokolov, P. Stone, S. Tett, I. Tracey and A. Weaver for suggestions. This work was supported by the UK Natural Environment Research Council (M.R.A.); The UK Department of Environment, Transport and Regions (P.A.S.); the UK Meteorological Office's Research and Development Programme (J.F.B.T.); the European Commission (R.S.); and the US National Oceanic and Atmospheric Administration (T.L.D.); with additional support from the US Department of Energy and the British Council.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Myles R. Allen.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Allen, M., Stott, P., Mitchell, J. et al. Quantifying the uncertainty in forecasts of anthropogenic climate change . Nature 407, 617–620 (2000). https://doi.org/10.1038/35036559

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing