Skip to main content

Limits to global and Australian temperature change this century based on expert judgment of climate sensitivity

Abstract

The projected warming of surface air temperature at the global and regional scale by the end of the century is directly related to emissions and Earth’s climate sensitivity. Projections are typically produced using an ensemble of climate models such as CMIP5, however the range of climate sensitivity in models doesn’t cover the entire range considered plausible by expert judgment. Of particular interest from a risk-management perspective is the lower impact outcome associated with low climate sensitivity and the low-probability, high-impact outcomes associated with the top of the range. Here we scale climate model output to the limits of expert judgment of climate sensitivity to explore these limits. This scaling indicates an expanded range of projected change for each emissions pathway, including a much higher upper bound for both the globe and Australia. We find the possibility of exceeding a warming of 2 °C since pre-industrial is projected under high emissions for every model even scaled to the lowest estimate of sensitivity, and is possible under low emissions under most estimates of sensitivity. Although these are not quantitative projections, the results may be useful to inform thinking about the limits to change until the sensitivity can be more reliably constrained, or this expanded range of possibilities can be explored in a more formal way. When viewing climate projections, accounting for these low-probability but high-impact outcomes in a risk management approach can complement the focus on the likely range of projections. They can also highlight the scale of the potential reduction in range of projections, should tight constraints on climate sensitivity be established by future research.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. Bodman RW, Rayner PJ, Jones RN (2016) How do carbon cycle uncertainties affect IPCC temperature projections? Atmos Sci Lett 17:236–242

    Article  Google Scholar 

  2. Bony S et al (2006) How well do we understand and evaluate climate change feedback processes? J Clim 19:3445–3482

    Article  Google Scholar 

  3. Booth BBB, Bernie D, McNeall D, Hawkins E, Caesar J, Boulton C, Friedlingstein P, Sexton DMH (2013) Scenario and modelling uncertainty in global mean temperature change derived from emission-driven global climate models. Earth Syst Dynam 4:95–108

    Article  Google Scholar 

  4. Collins M, et al. (2013). Chapter 12: long-term climate change: projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner G-K et al. (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge

  5. CSIRO and Bureau of Meteorology (2015) Climate change in Australia, technical report, Melbourne Australia. www.climatechangeinaustralia.gov.au

  6. Deser C, Knutti R, Solomon S, Phillips AS (2012) Communication of the role of natural variability in future North American climate. Nat Clim Change 2:775–779

    Article  Google Scholar 

  7. Flato GM, Marozke J, et al. (2013) Chapter 9: evaluation of climate models. In: Stocker TF, Qin D, Plattner G, et al. (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge

  8. Friedlingstein P, Meinshausen M, Arora VK, Jones CD, Anav A, Liddicoat SK, Knutti R (2014) Uncertainties in CMIP5 Climate projections due to carbon cycle feedbacks. J Clim 27:511–526

    Article  Google Scholar 

  9. Good P et al (2015) Nonlinear regional warming with increasing CO2 concentrations. Nat Clim Change 5:138–142

    Article  Google Scholar 

  10. Gregory J, Webb M (2008) Tropospheric adjustment induces a cloud component in CO2 forcing. J Clim 21:58–71

    Article  Google Scholar 

  11. Hansen J, Lacis A, Rind D, Russell G, Stone P, Fung I, Ruedy R, Lerner J (1984) Climate sensitivity: analysis of feedback mechanisms. In: Hansen J, Takahashi T (eds) Climate processes and climate sensitivity, vol 29. American Geophysical Union, Washington, pp 130–163

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  13. Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37:407–418

    Article  Google Scholar 

  14. Hibbard KA, Meehl GA, Cox PM, Friedlingstein P (2007) A strategy for climate change stabilization experiments. Eos Trans Am Geophys Union 88:217–221

    Article  Google Scholar 

  15. Hope C (2015) The $10 trillion value of better information about the transient climate response. Philos Trans R Soc Lond A Math, Phys Eng Sci 373:20140429

    Article  Google Scholar 

  16. Huber M, Mahlstein I, Wild M, Fasullo J, Knutti R (2010) Constraints on climate sensitivity from radiation patterns in climate models. J Clim 24:1034–1052

    Article  Google Scholar 

  17. IPCC (2013) Climate change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner GK et al (eds) Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

    Google Scholar 

  18. Kirtman B et al. (2013) Near-term climate change: projections and predictability. In: Stocker TF, Qin D, Plattner G-K et al. (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 953–1028. doi:10.1017/CBO9781107415324.023

  19. Knutti R, Hegerl GC (2008) The equilibrium sensitivity of the Earth’s temperature to radiation changes. Nature Geosci 1:735–743

    Article  Google Scholar 

  20. Knutti R, Rugenstein MAA (2015) Feedbacks, climate sensitivity and the limits of linear models. Philos Trans R Soc Lond A: Math Phys Eng Sci 373:20150146

    Article  Google Scholar 

  21. Knutti R, Meehl GA, Allen MR, Stainforth DA (2006) Constraining climate sensitivity from the seasonal cycle in surface temperature. J Clim 19:4224–4233

    Article  Google Scholar 

  22. Marvel K, Schmidt GA, Miller RL, Nazarenko LS (2015) Implications for climate sensitivity from the response to individual forcings. Nat Clim Change 6:386–389

    Article  Google Scholar 

  23. Masson D, Knutti R (2013) Predictor screening, calibration, and observational constraints in climate model ensembles: an illustration using climate sensitivity. J Clim 26:887–898

    Article  Google Scholar 

  24. Murphy JM, Booth BBB, Collins M, Harris GR, Sexton DMH, Webb MJ (2007) A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles. Philos Trans R Soc A Math Phys Eng Sci 365:1993–2028

    Article  Google Scholar 

  25. Pagani M, Liu Z, LaRiviere J, Ravelo AC (2010) High Earth-system climate sensitivity determined from Pliocene carbon dioxide concentrations. Nature Geosci 3:27–30

    Article  Google Scholar 

  26. Rijsberman FR, Swart RJ (1990) Targets and indicators of climatic change. Stockholm Environment Institute, Stockholm

    Google Scholar 

  27. Roe GH, Baker MB (2007) Why is climate sensitivity so unpredictable? Science 318:629–632

    Article  Google Scholar 

  28. Sanderson B, Piani C, Ingram WJ, Stone DA, Allen MR (2008) Towards constraining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics GCM simulations. Clim Dyn 30:175–190

    Article  Google Scholar 

  29. Sherwood SC, Bony S, Dufresne J-L (2014) Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505:37–42

    Article  Google Scholar 

  30. Sutton R, Suckling E, Hawkins E (2015) What does global mean temperature tell us about local climate? Philos Trans R Soc Lond A Math Phys Eng Sci 373:20140426

    Article  Google Scholar 

  31. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498

    Article  Google Scholar 

  32. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc A Math Phys Eng Sci 365:2053–2075

    Article  Google Scholar 

  33. Trewin B (2013) A daily homogenized temperature data set for Australia. Int J Climatol 33:1510–1529

    Article  Google Scholar 

  34. van Vuuren D et al (2011) The representative concentration pathways: an overview. Clim Change 109:5–31

    Article  Google Scholar 

  35. Watterson IG (2008) Calculation of probability density functions for temperature and precipitation change under global warming. J Geophys Res Atmos 113:13

    Article  Google Scholar 

  36. Zhou T, Chen X (2015) Uncertainty in the 2°C warming threshold related to climate sensitivity and climate feedback. J Meteorol Res 29:884–895

    Article  Google Scholar 

Download references

Acknowledgments

We wish to thank Penny Whetton, Ian Watterson and James Risbey for helpful discussion and comments. 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 U.S. 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. This work has been undertaken as part of the Australian Climate Change Science Programme, funded jointly by the Department of the Environment, the Bureau of Meteorology and CSIRO.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Michael R. Grose.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Grose, M.R., Colman, R., Bhend, J. et al. Limits to global and Australian temperature change this century based on expert judgment of climate sensitivity. Clim Dyn 48, 3325–3339 (2017). https://doi.org/10.1007/s00382-016-3269-2

Download citation

Keywords

  • Climate change
  • Temperature projections
  • Risk management
  • Climate sensitivity