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Robustness and uncertainties in the new CMIP5 climate model projections

Abstract

Estimates of impacts from anthropogenic climate change rely on projections from climate models. Uncertainties in those have often been a limiting factor, in particular on local scales. A new generation of more complex models running scenarios for the upcoming Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5) is widely, and perhaps naively, expected to provide more detailed and more certain projections. Here we show that projected global temperature change from the new models is remarkably similar to that from those used in IPCC AR4 after accounting for the different underlying scenarios. The spatial patterns of temperature and precipitation change are also very consistent. Interestingly, the local model spread has not changed much despite substantial model development and a massive increase in computational capacity. Part of this model spread is irreducible owing to internal variability in the climate system, yet there is also uncertainty from model differences that can potentially be eliminated. We argue that defining progress in climate modelling in terms of narrowing uncertainties is too limited. Models improve, representing more processes in greater detail. This implies greater confidence in their projections, but convergence may remain slow. The uncertainties should not stop decisions being made.

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Figure 1: Global temperature change and uncertainty.
Figure 2: Patterns of surface warming.
Figure 3: Patterns of precipitation change.
Figure 4: Model robustness for precipitation.

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References

  1. Meehl, G. A. et al. The WCRP CMIP3 multimodel dataset—A new era in climate change research. Bull. Am. Meteorol. Soc. 88, 1383–1394 (2007).

    Article  Google Scholar 

  2. Tebaldi, C. & Knutti, R. The use of the multi-model ensemble in probabilistic climate projections. Phil. Tran. R. Soc. A 365, 2053–2075 (2007).

    Article  Google Scholar 

  3. Knutti, R., Furrer, R., Tebaldi, C., Cermak, J. & Meehl, G. A. Challenges in combining projections from multiple climate models. J. Clim. 23, 2739–2758 (2010).

    Article  Google Scholar 

  4. Knutti, R. The end of model democracy? Climatic Change 102, 395–404 (2010).

    Article  Google Scholar 

  5. Knutti, R. et al. Good Practice Guidance Paper on Assessing and Combining Multi Model Climate Projections (IPCC Working Group I Technical Support Unit, University of Bern, 2010).

  6. Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1107 (2009).

    Article  Google Scholar 

  7. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. A Summary of the CMIP5 Experiment Design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  Google Scholar 

  8. IPCC Special Report on Emissions Scenarios (eds Nakićenović, N. & Swart, R.) (Cambridge Univ. Press, 2000).

  9. Meinshausen, M. et al. The RCP Greenhouse Gas Concentrations and their extensions from 1765 to 2300. Climatic Change 109, 213–241 (2011).

    Article  CAS  Google Scholar 

  10. Andrews, T., Gregory, J. M., Webb, M. J. & Taylor, K. Forcing, feedbacks and climate sensitivity in CMIP5 atmosphere–ocean climate models. Geophys. Res. Lett. 39, L09712 (2012).

    Google Scholar 

  11. Charney, J. G. et al. Carbon Dioxide and Climate: A Scientific Assessment (National Academy of Sciences, 1979).

    Google Scholar 

  12. Knutti, R. & Hegerl, G. C. The equilibrium sensitivity of the Earth’s temperature to radiation changes. Nature Geosci. 1, 735–743 (2008).

    Article  CAS  Google Scholar 

  13. Meinshausen, M., Wigley, T. & Raper, S. Emulating atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6-Part 2: Applications. Atmos. Chem. Phys. 11, 1457–1471 (2011).

    Article  CAS  Google Scholar 

  14. Meinshausen, M., Raper, S. & Wigley, T. 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 

  15. 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 

  16. Masson, D. & Knutti, R. Climate model genealogy. Geophys. Res. Lett. 38, L08703 (2011).

    Article  Google Scholar 

  17. Knutti, R. Should we believe model predictions of future climate change? Phil. Trans. R. Soc. A 366, 4647–4664 (2008).

    Article  Google Scholar 

  18. Knutti, R. et al. A review of uncertainties in global temperature projections over the twenty-first century. J. Clim. 21, 2651–2663 (2008).

    Article  Google Scholar 

  19. IPCC Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) (Cambridge Univ. Press, 2007).

  20. Tebaldi, C., Arblaster, J. M. & Knutti, R. Mapping model agreement on future climate projections. Geophys. Res. Lett. 38, L23701 (2011).

    Article  Google Scholar 

  21. Power, S., Delage, F., Colman, R. & Moise, A. Consensus on twenty-first-century rainfall projections in climate models more widespread than previously thought. J. Clim. 25, 3792–3809 (2012).

    Article  Google Scholar 

  22. Schaller, N., Mahlstein, I., Cermak, J. & Knutti, R. Analyzing precipitation projections: A comparison of different approaches to climate model evaluation. J. Geophys. Res. 116, D10118 (2011).

    Article  Google Scholar 

  23. Mahlstein, I., Portmann, R., Daniel, J., Solomon, S. & Knutti, R. Perceptible changes in regional precipitation in a future climate. Geophys. Res. Lett. 39, L05701 (2012).

    Google Scholar 

  24. Mahlstein, I., Knutti, R., Solomon, S. & Portmann, R. Early onset of significant local warming in low latitude countries. Environ. Res. Lett. 6, 034009 (2011).

    Article  Google Scholar 

  25. Shukla, J. et al. Revolution in climate prediction is both necessary and possible: A Declaration at the World Modelling Summit for Climate Prediction. Bull. Am. Meteorol. Soc. 90, 175–178 (2009).

    Article  Google Scholar 

  26. Dessai, S., Hulme, M., Lempert, R. & Pielke, R. Do we need better predictions to adapt to a changing climate? Eos 90, 111–112 (2009).

    Article  Google Scholar 

  27. Weigel, A., Liniger, M. & Appenzeller, C. The discrete Brier and ranked probability skill scores. Mon. Weath. Rev. 135, 118–124 (2007).

    Article  Google Scholar 

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Acknowledgements

We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank U. Beyerle and T. Corti for making the CMIP5 data accessible at ETH Zurich, and M. Meinshausen for the MAGICC projections.

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Both authors designed the study and wrote the paper. J.S. performed the CMIP5 analysis.

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Correspondence to Reto Knutti.

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The authors declare no competing financial interests.

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Knutti, R., Sedláček, J. Robustness and uncertainties in the new CMIP5 climate model projections. Nature Clim Change 3, 369–373 (2013). https://doi.org/10.1038/nclimate1716

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