Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Inhomogeneous forcing and transient climate sensitivity

Abstract

Understanding climate sensitivity is critical to projecting climate change in response to a given forcing scenario. Recent analyses1,2,3 have suggested that transient climate sensitivity is at the low end of the present model range taking into account the reduced warming rates during the past 10–15 years during which forcing has increased markedly4. In contrast, comparisons of modelled feedback processes with observations indicate that the most realistic models have higher sensitivities5,6. Here I analyse results from recent climate modelling intercomparison projects to demonstrate that transient climate sensitivity to historical aerosols and ozone is substantially greater than the transient climate sensitivity to CO2. This enhanced sensitivity is primarily caused by more of the forcing being located at Northern Hemisphere middle to high latitudes where it triggers more rapid land responses and stronger feedbacks. I find that accounting for this enhancement largely reconciles the two sets of results, and I conclude that the lowest end of the range of transient climate response to CO2 in present models and assessments7 (<1.3 °C) is very unlikely.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Comparison of transient climate response for well-mixed greenhouse gas forcing and for aerosol + ozone + land-use forcing.
Figure 2: Ratios of regional temperature responses to well-mixed greenhouse gas and inhomogeneous forcings in CMIP5 simulations.
Figure 3: Global mean temperature change estimates based on anthropogenic forcings obtained from a multi-model analysis11.

Similar content being viewed by others

References

  1. Ring, M. J., Lindner, D., Cross, E. F. & Schlesinger, M. E. Causes of the global warming observed since the 19th century. Atmos. Clim. Sci. 2, 401–415 (2012).

    Google Scholar 

  2. Otto, A. et al. Energy budget constraints on climate response. Nature Geosci. 6, 415–416 (2013).

    Article  CAS  Google Scholar 

  3. The Economist, Climate Science: A Sensitive Matter (The Economist Group, 2012).

    Google Scholar 

  4. Myhre, G. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 8 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  5. Fasullo, J. & Trenberth, K. A Less cloudy future: The role of subtropical subsidence in climate sensitivity. Science 338, 792–794 (2012).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  7. Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 12 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  8. Hegerl, G. C. et al. in Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) Ch. 9 (IPCC, Cambridge Univ. Press, 2007).

    Google Scholar 

  9. Boucher, O. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 7 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  10. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  Google Scholar 

  11. Shindell, D. T. et al. Radiative forcing in the ACCMIP historical and future climate simulations. Atmos. Chem. Phys. 13, 2939–2974 (2013).

    Article  CAS  Google Scholar 

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

    Google Scholar 

  13. Forster, P. M. et al. Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models. J. Geophys. Res. 118, 1139–1150 (2013).

    Google Scholar 

  14. Hansen, J., Sato, M. & Ruedy, R. Radiative forcing and climate response. J. Geophys. Res. 102, 6831–6864 (1997).

    Article  CAS  Google Scholar 

  15. Forster, P. M. d. F., Blackburn, M., Glover, R. & Shine, K. P. An examination of climate sensitivity for idealised climate change experiments in an intermediate general circulation model. Clim. Dynam. 16, 833–849 (2000).

    Article  Google Scholar 

  16. Shindell, D. & Faluvegi, G. Climate response to regional radiative forcing during the 20th century. Nature Geosci. 2, 294–300 (2009).

    Article  CAS  Google Scholar 

  17. Joshi, M. et al. A comparison of climate response to different radiative forcings in three general circulation models: towards an improved metric of climate change. Clim. Dynam. 20, 843–854 (2003).

    Article  Google Scholar 

  18. Stuber, N., Ponater, M. & Sausen, R. Why radiative forcing might fail as a predictor of climate change. Clim. Dynam. 24, 497–510 (2005).

    Article  Google Scholar 

  19. Boer, G. & Yu, B. Climate sensitivity and response. Clim. Dynam. 20, 415–429 (2003).

    Article  Google Scholar 

  20. Shindell, D. et al. Spatial scales of climate response to inhomogeneous radiative forcing. J. Geophys. Res. 115, D19110 (2010).

    Article  Google Scholar 

  21. Boucher, O. & Reddy, M. S. Climate trade-off between black carbon and carbon dioxide emissions. Energy Policy 36, 193–200 (2008).

    Article  Google Scholar 

  22. Morice, C., Kennedy, J., Rayner, N. & Jones, P. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. J. Geophys. Res. 117, D08101 (2012).

    Article  Google Scholar 

  23. Cowtan, K. & Way, R. G. Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Q. J. R. Meteorol. Soc. doi: 10.1002/qj.2297 (2014).

    Article  Google Scholar 

  24. Van Vuuren, D. P. et al. The representative concentration pathways: An overview. Climatic Change 109, 5–31 (2011).

    Article  Google Scholar 

  25. Armour, K., Bitz, C. & Roe, G. Time-varying climate sensitivity from regional feedbacks. J. Clim. 26, 4518–4534 (2013).

    Article  Google Scholar 

  26. 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 

  27. Smith, S. J. & Mizrahi, A. Near-term climate mitigation by short-lived forcers. Proc. Natl Acad. Sci. USA 110, 14202–14206 (2013).

    Article  CAS  Google Scholar 

  28. Shindell, D. et al. Simultaneously mitigating near-term climate change and improving human health and food security. Science 335, 183–189 (2012).

    Article  CAS  Google Scholar 

  29. Rohling, E. et al. Making sense of palaeoclimate sensitivity. Nature 491, 683–691 (2012).

    Article  CAS  Google Scholar 

  30. Shindell, D. T. et al. Interactive ozone and methane chemistry in GISS-E2 historical and future climate simulations. Atmos. Chem. Phys. 13, 2653–2689 (2013).

    Article  Google Scholar 

Download references

Acknowledgements

I acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling and the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison, and I thank the climate modelling groups from CMIP and the Atmospheric Chemistry and Climate Model Intercomparison Project (listed in Supplementary Table 1) for making available their model output. I thank G. Faluvegi and G. Milly for assistance with data analysis and US taxpayers and D. Considine for their support through NASA’s Modeling, Analysis and Prediction Program.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Drew T. Shindell.

Ethics declarations

Competing interests

The author declares no competing financial interests.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shindell, D. Inhomogeneous forcing and transient climate sensitivity. Nature Clim Change 4, 274–277 (2014). https://doi.org/10.1038/nclimate2136

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nclimate2136

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing