Climate Dynamics

, Volume 48, Issue 9–10, pp 3325–3339 | Cite as

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

  • Michael R. GroseEmail author
  • Robert Colman
  • Jonas Bhend
  • Aurel F. Moise


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.


Climate change Temperature projections Risk management Climate sensitivity 



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.


  1. Bodman RW, Rayner PJ, Jones RN (2016) How do carbon cycle uncertainties affect IPCC temperature projections? Atmos Sci Lett 17:236–242CrossRefGoogle Scholar
  2. Bony S et al (2006) How well do we understand and evaluate climate change feedback processes? J Clim 19:3445–3482CrossRefGoogle 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 4:95–108CrossRefGoogle 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, CambridgeGoogle Scholar
  5. CSIRO and Bureau of Meteorology (2015) Climate change in Australia, technical report, Melbourne Australia.
  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 2:775–779CrossRefGoogle 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, CambridgeGoogle Scholar
  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 27:511–526CrossRefGoogle Scholar
  9. Good P et al (2015) Nonlinear regional warming with increasing CO2 concentrations. Nat Clim Change 5:138–142CrossRefGoogle Scholar
  10. Gregory J, Webb M (2008) Tropospheric adjustment induces a cloud component in CO2 forcing. J Clim 21:58–71CrossRefGoogle 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 130–163CrossRefGoogle Scholar
  12. Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90:1095–1107CrossRefGoogle Scholar
  13. Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37:407–418CrossRefGoogle Scholar
  14. Hibbard KA, Meehl GA, Cox PM, Friedlingstein P (2007) A strategy for climate change stabilization experiments. Eos Trans Am Geophys Union 88:217–221CrossRefGoogle 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 373:20140429CrossRefGoogle 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 24:1034–1052CrossRefGoogle 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, CambridgeGoogle 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 1:735–743CrossRefGoogle 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 373:20150146CrossRefGoogle Scholar
  21. Knutti R, Meehl GA, Allen MR, Stainforth DA (2006) Constraining climate sensitivity from the seasonal cycle in surface temperature. J Clim 19:4224–4233CrossRefGoogle Scholar
  22. Marvel K, Schmidt GA, Miller RL, Nazarenko LS (2015) Implications for climate sensitivity from the response to individual forcings. Nat Clim Change 6:386–389CrossRefGoogle Scholar
  23. Masson D, Knutti R (2013) Predictor screening, calibration, and observational constraints in climate model ensembles: an illustration using climate sensitivity. J Clim 26:887–898CrossRefGoogle 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 365:1993–2028CrossRefGoogle Scholar
  25. Pagani M, Liu Z, LaRiviere J, Ravelo AC (2010) High Earth-system climate sensitivity determined from Pliocene carbon dioxide concentrations. Nature Geosci 3:27–30CrossRefGoogle Scholar
  26. Rijsberman FR, Swart RJ (1990) Targets and indicators of climatic change. Stockholm Environment Institute, StockholmGoogle Scholar
  27. Roe GH, Baker MB (2007) Why is climate sensitivity so unpredictable? Science 318:629–632CrossRefGoogle 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 30:175–190CrossRefGoogle Scholar
  29. Sherwood SC, Bony S, Dufresne J-L (2014) Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505:37–42CrossRefGoogle 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 373:20140426CrossRefGoogle Scholar
  31. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498CrossRefGoogle 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 365:2053–2075CrossRefGoogle Scholar
  33. Trewin B (2013) A daily homogenized temperature data set for Australia. Int J Climatol 33:1510–1529CrossRefGoogle Scholar
  34. van Vuuren D et al (2011) The representative concentration pathways: an overview. Clim Change 109:5–31CrossRefGoogle Scholar
  35. Watterson IG (2008) Calculation of probability density functions for temperature and precipitation change under global warming. J Geophys Res Atmos 113:13CrossRefGoogle Scholar
  36. Zhou T, Chen X (2015) Uncertainty in the 2°C warming threshold related to climate sensitivity and climate feedback. J Meteorol Res 29:884–895CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Michael R. Grose
    • 1
    Email author
  • Robert Colman
    • 2
  • Jonas Bhend
    • 3
  • Aurel F. Moise
    • 2
  1. 1.CSIRO Oceans and AtmosphereHobartAustralia
  2. 2.Australian Bureau of Meteorology, Research and DevelopmentDocklandsAustralia
  3. 3.Federal Office of Meteorology and ClimatologyMeteoSwissZurichSwitzerland

Personalised recommendations