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

Journal name:
Nature Geoscience
Volume:
5,
Pages:
256–260
Year published:
DOI:
doi:10.1038/ngeo1430
Received
Accepted
Published online

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 1.4–3K 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.

At a glance

Figures

  1. Evolution of uncertainties in reconstructed global-mean temperature projections under SRES A1B in the HadCM3L ensemble.
    Figure 1: Evolution of uncertainties in reconstructed global-mean temperature projections under SRES A1B in the HadCM3L ensemble.

    Blue colouring indicates goodness-of-fit between observations and ensemble members, plotted in order of increasing agreement (light to dark blue). Black line, the evolution of observations, and thick blue lines the ‘likely’ range (66% confidence interval) from the ensemble. Red bars show the IPCC-AR4 expert ‘likely’ range around 2050 and 2080. All temperatures are relative to the corresponding 1961–1990 mean. For consistency and to account for the observational mask, global-means are reconstructed from Giorgi and ocean region averages (0.2K less on average).

  2. Goodness-of-fit to recent temperature changes as a function of global-mean warming.
    Figure 2: Goodness-of-fit to recent temperature changes as a function of global-mean warming.

    a, 2001–2010 reconstructed hindcast; b, 2041–2060 forecast under SRES A1B for global-mean temperature, both as anomalies from 1961 to 1990. Coloured triangles, members of the HadCM3L perturbed-physics ensemble, with colours denoting the corresponding slab model estimated equilibrium climate sensitivity. D symbols, standard physics model configurations differing in natural forcing scenario and scaling on anthropogenic sulphate emissions. Black crosses, realizations of model error and corresponding temperature changes arising from simulations of internal variability, with the horizontal line denoting the 66th percentile of the error distribution. Vertical dotted lines, the range of the HadCM3L ensemble with errors lower than this percentile corresponding to a ‘likely’ range (66% confidence interval). Grey triangles, simulations with global annual mean flux adjustments outside ±5Wm−2. Black vertical bar and grey band in a, observations and ‘likely’ range. Horizontal bar in b, the expert IPCC-AR4 ‘likely’ range. Black filled circles CMIP-3 simulations, black open circles QUMP HadCM3 simulations. Arrowed larger triangles refer to models highlighted in Fig. 3.

  3. Surface temperature anomaly fields relative to 1961-1990 for 2001-2010 hindcast and 2041-2060 forecast for a low-response ensemble member, A ([Delta]T2050=1.4[thinsp]K), and high-response ensemble member, B ([Delta]T2050=3[thinsp]K) labelled in Fig. 2.
    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=1.4K), and high-response ensemble member, B (ΔT2050=3K) labelled in Fig. 2.

    a, Observed 2001–2010 anomaly; b,d model A anomaly for 2001–2010 and 2041–2060; c,e model B anomaly. White regions in ac indicate missing data, defined as >40% of yearly data missing over 1961–1990 or 2001–2010. The same mask is applied in b and c. Note the factor of two difference in colour-scale between ac and d,e.

References

  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, 113116 (2002).
  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, 719723 (2002).
  3. Stott, P. A. et al. Observational constraints on past attributable warming and predictions of future global warming. J. Clim. 19, 30553069 (2006).
  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, 357375 (2006).
  5. Meehl, G. A. et al. The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Am. Meteorol. Soc. 88, 13831394 (2007).
  6. Collins, M. et al. Climate model errors, feedbacks and forcings: A comparison of perturbed-physics and multi-model ensembles. Clim. Dynam. 36, 17371766 (2010).
  7. Kiehl, J. Twentieth century climate model response and climate sensitivity. Geophys. Res. Lett. 34, L22710 (2007).
  8. Knutti, R. Why are climate models reproducing the observed global surface warming so well? Geophys. Res. Lett. 35, L18704 (2008).
  9. Huybers, P. Compensation between model feedbacks and curtailment of climate sensitivity. J. Clim. 23, 30093018 (2010).
  10. Knutti, R. et al. A review of uncertainties in global temperature projections over the twenty-first century. J. Clim. 21, 26512663 (2008).
  11. Forest, C. E., Stone, P. H. & Sokolov, A. P. Constraining climate model parameters from observed 20th century changes. Tellus A 60, 911920 (2008).
  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).
  13. Friedlingstein, P. et al. Climate-carbon cycle feedback analysis: Results from the C4MIP model intercomparison. J. Clim. 19, 33373353 (2006).
  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, 347350 (2005).
  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, 83106 (2009).
  16. Murphy, J. M. et al. Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430, 768772 (2004).
  17. Jackson, C. S., Sen, M. K., Huerta, G., Deng, Y. & Bowman, K. P. Error reduction and convergence in climate prediction. J. Clim. 21, 66986709 (2008).
  18. Nakicenovic, N. & Swart, R. Special Report on Emissions Scenarios (Cambridge Univ. Press, 2000).
  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).
  20. Knutti, R., Furrer, R., Tebaldi, C., Cermak, J. & Meehl, G. A. Challenges in combining projections from multiple climate models. J. Clim. 23, 27392758 (2010).
  21. Weigel, A. P., Knutti, R., Liniger, M. & Appenzeller, C. Risks of model weighting in multimodel climate projections. J. Clim. 23, 41754191 (2010).
  22. Frame, D. J. et al. Constraining climate forecasts: The role of prior assumptions. Geophys. Res. Lett. 32, L09702 (2005).
  23. Easterling, D. R. & Wehner, M. F. Is the climate warming or cooling? Geophys. Res. Lett. 36, L08706 (2009).
  24. Solomon, S. et al. Contributions of stratospheric water vapor to decadal changes in the rate of global warming. Science 327, 12191223 (2010).
  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, 303329 (2010).
  26. Stone, D. A. & Allen, M. R. Attribution of global surface warming without dynamical models. Geophys. Res. Lett. 32, L18711 (2005).
  27. Betts, R. A. et al. When could global warming reach 4°C. Phil. Trans. R. Soc. Lond. A 369, 6784 (2011).
  28. Desaii, S., Hulme, M., Lempert, R. & Pielke, R.Jr Do we need better predictions to adapt to a changing climate? Eos 90, 111112 (2009).
  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).
  30. Frame, D. J. et al. The climateprediction.net BBC climate change experiment: Design of the coupled model ensemble. Phil. Trans. R. Soc. Lond. A 367, 855870 (2009).

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Author information

Affiliations

  1. Atmospheric, Oceanic & Planetary Physics, Department of Physics, University of Oxford, Parks Road, Oxford OX1 3PU, UK

    • Daniel J. Rowlands,
    • David J. Frame,
    • Tolu Aina,
    • Carl Christensen,
    • Nicholas Faull,
    • Benjamin S. Grandey,
    • Edward Gryspeerdt,
    • William J. Ingram,
    • Neil Massey,
    • Suzanne M. Rosier,
    • Kuniko Yamazaki,
    • Y. Hiro Yamazaki &
    • Myles R. Allen
  2. School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK

    • Daniel J. Rowlands,
    • David J. Frame,
    • Ana Lopez &
    • Myles R. Allen
  3. Centre for the Analysis of Time Series, London School of Economics, London WC2A 2AE, UK

    • Daniel J. Rowlands,
    • Ana Lopez &
    • Leonard A. Smith
  4. Smith School of Enterprise and the Environment, Hayes House, 75 George Street, Oxford OX1 2BQ, UK

    • David J. Frame,
    • Neil Massey &
    • Myles R. Allen
  5. Climate Change Research Institute, School of Geography, Environment and Earth Sciences, Victoria University of Wellington, Wellington 6012, New Zealand

    • David J. Frame
  6. Monash Weather and Climate, Monash University, Clayton, Victoria 3800, Australia

    • Duncan Ackerley
  7. Department of Meteorology, University of Reading, Earley Gate, Reading, RG6 6BB, UK

    • Duncan Ackerley &
    • Eleanor J. Highwood
  8. Oxford e-Research Centre, Keble Road, Oxford OX1 3QG, UK

    • Tolu Aina &
    • Milo Thurston
  9. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PU, UK

    • Ben B. B. Booth &
    • William J. Ingram
  10. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QJ, UK

    • Matthew Collins
  11. Department of Meteorology, Earth and Environmental Systems Institute, Pennsylvania State University, University Park, Pennsylvania 16802, USA

    • Chris E. Forest
  12. Royal Meteorological Society, Reading, RG1 7LL, UK

    • Sylvia Knight
  13. BBC Science, BBC White City, 201 Wood Lane, London W12 7TS, UK

    • Frances McNamara
  14. Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK

    • Nicolai Meinshausen
  15. Abdus Salam International Center for Theoretical Physics, Trieste 34151, Italy

    • Claudio Piani
  16. The American University of Paris, Paris 75007, France

    • Claudio Piani
  17. NIWA Wellington, 301 Evans Bay Parade, Hataitai, Wellington 6021, New Zealand

    • Suzanne M. Rosier
  18. National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, Colorado 80305, USA

    • Benjamin M. Sanderson
  19. Pembroke College, Oxford University of Oxford, Oxford OX1 1DW, UK

    • Leonard A. Smith
  20. Climate Systems Analysis Group, University of Cape Town, Private Bag X3, Rondebosch, Cape Town, South Africa

    • Dáithí A. Stone
  21. School of Geography, Politics and Sociology, Newcastle University, Newcastle on Tyne, NE1 7RU, UK

    • Y. Hiro Yamazaki

Contributions

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.

Competing financial interests

The authors declare no competing financial interests.

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