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:

Disentangling greenhouse warming and aerosol cooling to reveal Earth’s climate sensitivity

Nature Geoscience volume 9, pages 286–289 (2016)Cite this article

Subjects

Abstract

Earth’s climate sensitivity has long been subject to heated debate and has spurred renewed interest after the latest IPCC assessment report suggested a downward adjustment of its most likely range1. Recent observational studies have produced estimates of transient climate sensitivity, that is, the global mean surface temperature increase at the time of CO2 doubling, as low as 1.3 K (refs 2,3), well below the best estimate produced by global climate models (1.8 K). Here, we present an observation-based study of the time period 1964 to 2010, which does not rely on climate models. The method incorporates observations of greenhouse gas concentrations, temperature and radiation from approximately 1,300 surface sites into an energy balance framework. Statistical methods commonly applied to economic time series are then used to decompose observed temperature trends into components attributable to changes in greenhouse gas concentrations and surface radiation. We find that surface radiation trends, which have been largely explained by changes in atmospheric aerosol loading, caused a cooling that masked approximately one-third of the continental warming due to increasing greenhouse gas concentrations over the past half-century. In consequence, the method yields a higher transient climate sensitivity (2.0 ± 0.8 K) than other observational studies.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Figure 1: Radiation measurements from 1,300 surface stations.
Figure 2: Temperature trend decomposition.
Figure 3: Probably density functions for TCS valid for land.

Similar content being viewed by others

References

  1. Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 12, 1029–1136 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

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

    Google Scholar 

  3. Lewis, N. & Curry, J. The implications for climate sensitivity of AR5 forcing and heat uptake estimates. Clim. Dynam. 45, 1009–1023 (2015).

    Google Scholar 

  4. Allen, M. R. & Frame, D. J. Call off the quest. Science 318, 582–583 (2007).

    Google Scholar 

  5. Padilla, L. E., Vallis, G. K. & Rowley, C. W. Probabilistic estimates of transient climate sensitivity subject to uncertainty in forcing and natural variability. J. Clim. 24, 5521–5537 (2011).

    Google Scholar 

  6. Wild, M. Enlightening global dimming and brightening. Bull. Am. Meteorol. Soc. 93, 27–37 (2012).

    Google Scholar 

  7. Wild, M. Global dimming and brightening: a review. J. Geophys. Res. 114, D00D16 (2009).

    Google Scholar 

  8. Myhre, G. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 8, 659–740 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  9. Norris, J. R. & Wild, M. Trends in aerosol radiative effects over Europe inferred from observed cloud cover, solar “dimming” and solar “brightening”. J. Geophys. Res. 112, D08214 (2007).

    Google Scholar 

  10. Andreae, M. O., Jones, C. D. & Cox, P. M. Strong present-day aerosol cooling implies a hot future. Nature 435, 1187–1190 (2005).

    Google Scholar 

  11. Lohmann, U. et al. Total aerosol effect: radiative forcing or radiative flux perturbation? Atmos. Chem. Phys. 10, 3235–3246 (2010).

    Google Scholar 

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

    Google Scholar 

  13. Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).

    Google Scholar 

  14. Hofmann, D. J. et al. The role of carbon dioxide in climate forcing from 1979 to 2004: introduction of the Annual Greenhouse Gas Index. Tellus B 58, 614–619 (2006).

    Google Scholar 

  15. Gilgen, H. & Ohmura, A. The global energy balance archive. Bull. Am. Meteorol. Soc. 80, 831–850 (1999).

    Google Scholar 

  16. Arellano, M. & Bond, S. Some tests of specification for panel data—Monte-Carlo evidence and an application to employment equations. Rev. Econ. Stud. 58, 277–297 (1991).

    Google Scholar 

  17. Kaufmann, R. K., Kauppi, H., Mann, M. L. & Stock, J. H. Reconciling anthropogenic climate change with observed temperature 1998–2008. Proc. Natl Acad. Sci. USA 108, 11790–11793 (2011).

    Google Scholar 

  18. Bindoff, N. L. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 10, 867–952 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  19. Wild, M., Ohmura, A. & Makowski, K. Impact of global dimming and brightening on global warming. Geophys. Res. Lett. 34, L04702 (2007).

    Google Scholar 

  20. Shindell, D. T. Inhomogeneous forcing and transient climate sensitivity. Nature Clim. Change 4, 274–277 (2014).

    Google Scholar 

  21. Hartmann, D. L. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 2, 159–254 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  22. Meinshausen, M. et al. Greenhouse-gas emission targets for limiting global warming to 2 degrees C. Nature 458, 1158–U1196 (2009).

    Google Scholar 

  23. Rogelj, J. et al. Energy system transformations for limiting end-of-century warming to below 1.5 degrees C. Nature Clim. Change 5, 519–527 (2015).

    Google Scholar 

  24. Meehl, G. A., Arblaster, J. M., Fasullo, J. T., Hu, A. X. & Trenberth, K. E. Model-based evidence of deep-ocean heat uptake during surface-temperature hiatus periods. Nature Clim. Change 1, 360–364 (2011).

    Google Scholar 

  25. Santer, B. D. et al. Volcanic contribution to decadal changes in tropospheric temperature. Nature Geosci. 7, 185–189 (2014).

    Google Scholar 

  26. Skeie, R. B., Berntsen, T., Aldrin, M., Holden, M. & Myhre, G. A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series. Earth Syst. Dynam. 5, 139–175 (2014).

    Google Scholar 

  27. Kosaka, Y. & Xie, S. P. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501, 403–407 (2013).

    Google Scholar 

  28. Klimont, Z., Smith, S. J. & Cofala, J. The last decade of global anthropogenic sulfur dioxide: 2000–2011 emissions. Environ. Res. Lett. 8, 014003 (2013).

    Google Scholar 

  29. Smith, S. J. et al. Anthropogenic sulfur dioxide emissions: 1850–2005. Atmos. Chem. Phys. 11, 1101–1116 (2011).

    Google Scholar 

  30. Magnus, J. R., Melenberg, B. & Muris, C. Global warming and local dimming: the statistical evidence. J. Am. Stat. Assoc. 106, 452–464 (2011).

    Google Scholar 

  31. Phillips, P. C. B. Halbert White Jr. Memorial JFEC Lecture: pitfalls and possibilities in predictive regression. J. Financ. Economet. 13, 521–555 (2015).

    Google Scholar 

  32. Phillips, P. C. B. Time-series regression with a unit-root. Econometrica 55, 277–301 (1987).

    Google Scholar 

  33. Kaufmann, R. K., Kauppi, H. & Stock, J. H. Emissions, concentrations, temperature: a time series analysis. Climatic Change 77, 249–278 (2006).

    Google Scholar 

  34. Kaufmann, R. K., Kauppi, H. & Stock, J. H. The relationship between radiative forcing and temperature: what do statistical analyses of the instrumental temperature record measure? Climatic Change 77, 279–289 (2006).

    Google Scholar 

  35. Said, S. E. & Dickey, D. A. Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika 71, 599–607 (1984).

    Google Scholar 

  36. Phillips, P. C. B. & Perron, P. Testing for a unit-root in time-series regression. Biometrika 75, 335–346 (1988).

    Google Scholar 

  37. Kwiatkowski, D., Phillips, P. C. B. & Schmidt, P. Testing for stationarity in the components representation of a time-series. Economet. Theor. 8, 586–591 (1992).

    Google Scholar 

  38. Engle, R. F. & Granger, C. W. J. Cointegration and error correction—representation, estimation, and testing. Econometrica 55, 251–276 (1987).

    Google Scholar 

  39. Phillips, P. C. B. & Moon, H. R. Linear regression limit theory for nonstationary panel data. Econometrica 67, 1057–1111 (1999).

    Google Scholar 

  40. Phillips, P. C. B. & Ouliaris, S. Asymptotic properties of residual based tests for cointegration. Econometrica 58, 165–193 (1990).

    Google Scholar 

  41. Mackinnon, J. G. Approximate asymptotic-distribution functions for unit-root and cointegration tests. J. Bus. Econ. Stat. 12, 167–176 (1994).

    Google Scholar 

  42. Kaufmann, R. K., Kauppi, H., Mann, M. L. & Stock, J. H. Does temperature contain a stochastic trend: linking statistical results to physical mechanisms. Climatic Change 118, 729–743 (2013).

    Google Scholar 

  43. Schneider, T., Bischoff, T. & Haug, G. H. Migrations and dynamics of the intertropical convergence zone. Nature 513, 45–53 (2014).

    Google Scholar 

  44. Phillips, P. C. B. & Loretan, M. Estimating long-run economic equilibria. Rev. Econ. Stud. 58, 407–436 (1991).

    Google Scholar 

  45. Saikkonen, P. Asymptotically efficient estimation of cointegration regressions. Economet. Theor. 7, 1–21 (1991).

    Google Scholar 

  46. Stock, J. H. & Watson, M. W. A simple estimator of cointegrating vectors in higher-order integrated systems. Econometrica 61, 783–820 (1993).

    Google Scholar 

Download references

Acknowledgements

This research was supported by an interdisciplinary seed grant awarded by the Yale Climate and Energy Institute (YCEI). P.C.B.P. acknowledges research support from the NSF under Grant No. SES 1258258.

Author information

Authors and Affiliations

  1. Department of Geology and Geophysics, Yale University, New Haven, Connecticut 06511, USA

    T. Storelvmo

  2. Graduate School of Business, Nord University, 8049 Bodø, Norway

    T. Leirvik

  3. Institute for Atmospheric and Climate Science, ETH Zurich, 8092 Zurich, Switzerland

    U. Lohmann & M. Wild

  4. Department of Economics, Yale University, New Haven, Connecticut 06511, USA

    P. C. B. Phillips

Authors
  1. T. Storelvmo
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. T. Leirvik
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. U. Lohmann
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. P. C. B. Phillips
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. M. Wild
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

T.S. and P.C.B.P. designed the project. T.L. performed data quality checks and all technical analysis. M.W. and U.L. contributed data and helped with interpretation. T.S. and P.C.B.P. wrote the paper with contributions from all co-authors.

Corresponding author

Correspondence to T. Storelvmo.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Information (PDF 183 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Storelvmo, T., Leirvik, T., Lohmann, U. et al. Disentangling greenhouse warming and aerosol cooling to reveal Earth’s climate sensitivity. Nature Geosci 9, 286–289 (2016). https://doi.org/10.1038/ngeo2670

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ngeo2670

This article is cited by

Search

Advanced 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