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Uncertainty in temperature projections reduced using carbon cycle and climate observations

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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Figure 1: Time series of global-mean temperature change for selected SRES marker scenarios.
Figure 2: Probability of exceeding 2 °C global-mean temperature change relative to pre-industrial for A1FI, A1B and A2 emission scenarios.

References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Meinshausen, M., Raper, S. C. B. & Wigley, T. M. L. 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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  5. Brohan, P., Kennedy, J. J., Harris, I., Tett, S. F. B. & Jones, P. D. Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850. J. Geophys. Res. 111, D12106 (2006).

    Article  Google Scholar 

  6. Nakicenovic, N. & Swart, R. IPCC Special Report on Emissions Scenarios (Cambridge Univ. Press, 2000).

    Google Scholar 

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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Forest, C. E., Stone, P. H. & Sokolov, A. P. Constraining climate model parameters from observed 20th century changes. Tellus 60A, 911–920 (2008).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  12. Ricciuto, D. M., Davis, K. J. & Keller, K. A Bayesian calibration of a simple carbon cycle model: The role of observations in estimating and reducing uncertainty. Glob. Biogeochem. Cycles 22, GB2030 (2008).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  14. Rogelj, J., Meinshausen, M. & Knutti, R. Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nature Clim. Change 2, 248–253 (2012).

    Article  Google Scholar 

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

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

    Article  CAS  Google Scholar 

  17. Bodman, R. W., Karoly, D. J., Wijffels, S. E. & Enting, I. G. Observational constraints on parameter estimates for a simple climate model. Aust. Met. Ocean. J. 62, 277–286 (2012).

    Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  20. Schneider, S. H. & Lane, J. 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).

    Google Scholar 

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

    Article  Google Scholar 

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

    Book  Google Scholar 

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

    Article  Google Scholar 

  24. Rayner, P. J., Koffi, E., Scholze, M., Kaminski, T. & Dufresne, J-L. Constraining predictions of the carbon cycle using data. Phil. Trans. R. Soc. 369, 1955–1966 (2011).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  26. Mosegaard, K. & Sambridge, M. Monte Carlo analysis of inverse problems. Inverse Prob. 18, R29–R54 (2002).

    Article  Google Scholar 

Download references

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).

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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.

Corresponding author

Correspondence to Roger W. Bodman.

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The authors declare no competing financial interests.

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Bodman, R., Rayner, P. & Karoly, D. Uncertainty in temperature projections reduced using carbon cycle and climate observations. Nature Clim Change 3, 725–729 (2013). https://doi.org/10.1038/nclimate1903

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