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Broad range of 2050 warming from an observationally constrained large climate model ensemble


Incomplete understanding of three aspects of the climate system—equilibrium climate sensitivity, rate of ocean heat uptake and historical aerosol forcing—and the physical processes underlying them lead to uncertainties in our assessment of the global-mean temperature evolution in the twenty-first century1,2. Explorations of these uncertainties have so far relied on scaling approaches3,4, large ensembles of simplified climate models1,2, or small ensembles of complex coupled atmosphere–ocean general circulation models5,6 which under-represent uncertainties in key climate system properties derived from independent sources7,8,9. Here we present results from a multi-thousand-member perturbed-physics ensemble of transient coupled atmosphere–ocean general circulation model simulations. We find that model versions that reproduce observed surface temperature changes over the past 50 years show global-mean temperature increases of 1.4–3 K by 2050, relative to 1961–1990, under a mid-range forcing scenario. This range of warming is broadly consistent with the expert assessment provided by the Intergovernmental Panel on Climate Change Fourth Assessment Report10, but extends towards larger warming than observed in ensembles-of-opportunity5 typically used for climate impact assessments. From our simulations, we conclude that warming by the middle of the twenty-first century that is stronger than earlier estimates is consistent with recent observed temperature changes and a mid-range ‘no mitigation’ scenario for greenhouse-gas emissions.

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Figure 1: Evolution of uncertainties in reconstructed global-mean temperature projections under SRES A1B in the HadCM3L ensemble.
Figure 2: Goodness-of-fit to recent temperature changes as a function of global-mean warming.
Figure 3: Surface temperature anomaly fields relative to 1961–1990 for 2001–2010 hindcast and 2041–2060 forecast for a low-response ensemble member, A (ΔT2050=1.4 K), and high-response ensemble member, B (ΔT2050=3 K) labelled in Fig. 2.

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  1. Forest, C. E., Stone, P. H., Sokolov, A. P., Allen, M. R. & Webster, M. D. Quantifying uncertainties in climate system properties with the use of recent climate observations. Science 295, 113–116 (2002).

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Stott, P. A. et al. Observational constraints on past attributable warming and predictions of future global warming. J. Clim. 19, 3055–3069 (2006).

    Article  Google Scholar 

  4. Harris, G. R. et al. Frequency distributions of transient regional climate change from perturbed-physics ensembles of general circulation model simulations. Clim. Dynam. 27, 357–375 (2006).

    Article  Google Scholar 

  5. 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 

  6. Collins, M. et al. Climate model errors, feedbacks and forcings: A comparison of perturbed-physics and multi-model ensembles. Clim. Dynam. 36, 1737–1766 (2010).

    Article  Google Scholar 

  7. Kiehl, J. Twentieth century climate model response and climate sensitivity. Geophys. Res. Lett. 34, L22710 (2007).

    Article  Google Scholar 

  8. Knutti, R. Why are climate models reproducing the observed global surface warming so well? Geophys. Res. Lett. 35, L18704 (2008).

    Article  Google Scholar 

  9. Huybers, P. Compensation between model feedbacks and curtailment of climate sensitivity. J. Clim. 23, 3009–3018 (2010).

    Article  Google Scholar 

  10. 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 

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

    Article  Google Scholar 

  12. Boé, J., Hall, A. & Qu, X. Deep ocean heat uptake as a major source of spread in transient climate change simulations. Geophys. Res. Lett. 36, L22701 (2009).

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Milly, P. C. D., Dunne, K. A. & Vecchia, V. Global pattern of trends in stream flow and water availability in a changing climate. Nature 428, 347–350 (2005).

    Article  Google Scholar 

  15. Tebaldi, C. & Sansó, B. Joint projections of temperature and precipitation change from multiple climate models: A hierarchical Bayesian approach. J. R. Stat. Soc. A 172, 83–106 (2009).

    Article  Google Scholar 

  16. Murphy, J. M. et al. Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430, 768–772 (2004).

    Article  Google Scholar 

  17. Jackson, C. S., Sen, M. K., Huerta, G., Deng, Y. & Bowman, K. P. Error reduction and convergence in climate prediction. J. Clim. 21, 6698–6709 (2008).

    Article  Google Scholar 

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

    Google Scholar 

  19. 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 1950. J. Geophys. Res. 111, D12106 (2006).

    Article  Google Scholar 

  20. 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 

  21. Weigel, A. P., Knutti, R., Liniger, M. & Appenzeller, C. Risks of model weighting in multimodel climate projections. J. Clim. 23, 4175–4191 (2010).

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Easterling, D. R. & Wehner, M. F. Is the climate warming or cooling? Geophys. Res. Lett. 36, L08706 (2009).

    Article  Google Scholar 

  24. Solomon, S. et al. Contributions of stratospheric water vapor to decadal changes in the rate of global warming. Science 327, 1219–1223 (2010).

    Article  Google Scholar 

  25. Lockwood, M. Solar change and climate: An update in the light of the current exceptional solar minimum. Phil. Trans. R. Soc. Lond. A 466, 303–329 (2010).

    Google Scholar 

  26. Stone, D. A. & Allen, M. R. Attribution of global surface warming without dynamical models. Geophys. Res. Lett. 32, L18711 (2005).

    Google Scholar 

  27. Betts, R. A. et al. When could global warming reach 4 °C. Phil. Trans. R. Soc. Lond. A 369, 67–84 (2011).

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Hall, A. & Qu, X. Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys. Res. Lett. 33, L03502 (2006).

    Google Scholar 

  30. Frame, D. J. et al. The BBC climate change experiment: Design of the coupled model ensemble. Phil. Trans. R. Soc. Lond. A 367, 855–870 (2009).

    Article  Google Scholar 

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We thank all participants in the experiments, as well as the academic institutions and the individuals who have helped make the experiment possible, particularly D. Anderson for developing the Berkeley Open Infrastructure for Network Computing. We also thank the Natural Environment Research Council (NERC), the European Union FP6 WATCH and ENSEMBLES projects, the Oxford Martin School, the Smith School of Enterprise and the Environment and Microsoft Research for support and J. Renouf and co-workers at the BBC for their documentaries explaining and promoting this experiment. D.J.R. was supported by a NERC PhD studentship with a CASE award from the Centre for Ecology & Hydrology (CEH) Wallingford.

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All authors contributed to the design and implementation of the experiment. D.J.R. performed the analysis and wrote the paper, with significant contributions from D.J.F., M.R.A. and N.M. All authors commented on the paper.

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Correspondence to Daniel J. Rowlands.

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Rowlands, D., Frame, D., Ackerley, D. et al. Broad range of 2050 warming from an observationally constrained large climate model ensemble. Nature Geosci 5, 256–260 (2012).

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