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Uncertainty in temperature projections reduced using carbon cycle and climate observations

Nature Climate Change volume 3, pages 725729 (2013) | Download Citation

Abstract

The future behaviour of the carbon cycle is a major contributor to uncertainty in temperature projections for the twenty-first century1,2. Using a simplified climate model3, we show that, for a given emission scenario, it is the second most important contributor to this uncertainty after climate sensitivity, followed by aerosol impacts. Historical measurements of carbon dioxide concentrations4 have been used along with global temperature observations5 to help reduce this uncertainty. This results in an increased probability of exceeding a 2 °C global–mean temperature increase by 2100 while reducing the probability of surpassing a 6 °C threshold for non-mitigation scenarios such as the Special Report on Emissions Scenarios A1B and A1FI scenarios6, as compared with projections from the Fourth Assessment Report7 of the Intergovernmental Panel on Climate Change. Climate sensitivity, the response of the carbon cycle and aerosol effects remain highly uncertain but historical observations of temperature and carbon dioxide imply a trade–off between them so that temperature projections are more certain than they would be considering each factor in isolation. As well as pointing out the promise from the formal use of observational constraints in climate projection, this also highlights the need for an holistic view of uncertainty.

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References

  1. 1.

    et al. High sensitivity of future global warming to land carbon cycle processes. Environ. Res. Lett. 7, 024002 (2012).

  2. 2.

    et al. Climate-carbon cycle feedback analysis: Results from the C4MIP model intercomparison. J. Clim. 19, 3337–3353 (2006).

  3. 3.

    , & Emulating coupled atmosphere–ocean and carbon cycle models with a simpler model, MAGICC6–Part 1: Model description and calibration. Atmos. Chem. Phys. 11, 1417–1456 (2011).

  4. 4.

    & Extension and integration of atmospheric carbon dioxide data into a globally consistent measurement record. J. Geophys. Res. 100, 11593–11610 (1995).

  5. 5.

    , , , & Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850. J. Geophys. Res. 111, D12106 (2006).

  6. 6.

    & IPCC Special Report on Emissions Scenarios (Cambridge Univ. Press, 2000).

  7. 7.

    IPCC Climate Change 2007: The Physical Science Basis. Contribution of Working Group I of the Fourth Assessment Report (Cambridge Univ. Press, 2007).

  8. 8.

    et al. The WCRP CMIP3 multi-model dataset: A new era in climate change research. Bull. Am. Meteorol. Soc. 88, 1383–1394 (2007).

  9. 9.

    et al. Climate model errors, feedbacks and forcings: A comparison of perturbed physics and multi-model ensembles. Clim. Dynam. 36, 1737–1766 (2011).

  10. 10.

    , & Constraining climate model parameters from observed 20th century changes. Tellus 60A, 911–920 (2008).

  11. 11.

    et al. Greenhouse-gas emission targets for limiting global warming to 2 °C. Nature 458, 1158–1162 (2009).

  12. 12.

    , & A Bayesian calibration of a simple carbon cycle model: The role of observations in estimating and reducing uncertainty. Glob. Biogeochem. Cycles 22, GB2030 (2008).

  13. 13.

    et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109, 213–241 (2011).

  14. 14.

    , & Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nature Clim. Change 2, 248–253 (2012).

  15. 15.

    Estimating Uncertainties in Future Global Warming Using a Simple Climate Model PhD thesis, Univ. Melbourne (2011).

  16. 16.

    et al. Improved estimates of upper-ocean warming and multi-decadal sea-level rise. Nature 453, 1090–1093 (2008).

  17. 17.

    , , & Observational constraints on parameter estimates for a simple climate model. Aust. Met. Ocean. J. 62, 277–286 (2012).

  18. 18.

    & Correlation between climate sensitivity and aerosol forcing and its implication for the climate trap. Climatic Change 109, 815–825 (2011).

  19. 19.

    A slippery slope: How much global warming constitutes dangerous anthropogenic interference. Climatic Change 68, 269–279 (2005).

  20. 20.

    & in Avoiding Dangerous Climate Change (eds Schellnhuber, H. J., Cramer, W., Nakicenovic, N., Wigley, T. M. L. & Yohe, G.) Ch. 2 (Cambridge Univ. Press, 2006).

  21. 21.

    et al. A review of uncertainties in global temperature projections over the twenty-first century. J. Clim. 21, 2651–2662 (2008).

  22. 22.

    Inverse Problem Theory and Methods for Model Parameter Estimation (Society for Industrial and Applied Mathematics, 2005).

  23. 23.

    et al. Constraining climate forecasts: The role of prior assumptions. Geophys. Res. Lett. 32, L09702 (2005).

  24. 24.

    , , , & Constraining predictions of the carbon cycle using data. Phil. Trans. R. Soc. 369, 1955–1966 (2011).

  25. 25.

    & Inversion of terrestrial ecosystem model parameter values against eddy covariance measurements by Monte Carlo sampling. Glob. Change Biol. 11, 1333–1351 (2005).

  26. 26.

    & Monte Carlo analysis of inverse problems. Inverse Prob. 18, R29–R54 (2002).

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Acknowledgements

We thank M. Meinshausen and J. Kattge for supplying the MAGICC and MCMH code respectively. This research was supported by the Australian Research Council through the Discovery Projects funding scheme (project number FF0668679), Australian Research Council Centre of Excellence for Climate System Science (grant CE 110001028) and an ARC Professorial Fellowship (DP1096309).

Author information

Affiliations

  1. The University of Melbourne, Melbourne 3010, Australia

    • Roger W. Bodman
    • , Peter J. Rayner
    •  & David J. Karoly
  2. Victoria University, Melbourne 3000, Australia

    • Roger W. Bodman

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Contributions

R.W.B, P.J.R. and D.J.K. designed the research. R.W.B. carried out the analysis, with P.J.R. adding the linear uncertainty analysis of the posterior covariance. R.W.B. wrote the paper. All authors discussed the results and edited the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Roger W. Bodman.

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DOI

https://doi.org/10.1038/nclimate1903

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