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Nonlinear regional warming with increasing CO2 concentrations

Nature Climate Change volume 5, pages 138–142 (2015)Cite this article

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A Corrigendum to this article was published on 25 February 2015

This article has been updated

Abstract

When considering adaptation measures and global climate mitigation goals, stakeholders need regional-scale climate projections, including the range of plausible warming rates. To assist these stakeholders, it is important to understand whether some locations may see disproportionately high or low warming from additional forcing above targets such as 2 K (ref. 1). There is a need to narrow uncertainty2 in this nonlinear warming, which requires understanding how climate changes as forcings increase from medium to high levels. However, quantifying and understanding regional nonlinear processes is challenging. Here we show that regional-scale warming can be strongly superlinear to successive CO2 doublings, using five different climate models. Ensemble-mean warming is superlinear over most land locations. Further, the inter-model spread tends to be amplified at higher forcing levels, as nonlinearities grow—especially when considering changes per kelvin of global warming. Regional nonlinearities in surface warming arise from nonlinearities in global-mean radiative balance, the Atlantic meridional overturning circulation, surface snow/ice cover and evapotranspiration. For robust adaptation and mitigation advice, therefore, potentially avoidable climate change (the difference between business-as-usual and mitigation scenarios) and unavoidable climate change (change under strong mitigation scenarios) may need different analysis methods.

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Figure 1: Regional nonlinearity in the transient-forced 1pctCO2 experiment.
Figure 2: Mechanisms of nonlinear regional warming in HadGEM2-ES.
Figure 3: Doubling ratio of ensemble-mean warming.
Figure 4: Multi-model mechanisms of temperature nonlinearity.

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Change history

  • 28 January 2015

    In the version of this Letter originally published, the fourth author affiliation should have read '4Institute for Atmospheric and Climate Science, Department of Environmental Sciences, ETH Zurich, CH-8092 Zurich, Switzerland.' This error has been corrected in the online versions of the Letter.

References

  1. Arnell, N. W. et al. A global assessment of the effects of climate policy on the impacts of climate change. Nature Clim. Change 3, 512–519 (2013).

    Article  Google Scholar 

  2. Oppenheimer, M. Defining dangerous anthropogenic interference: The role of science, the limits of science. Risk Anal. 25, 1399–1407 (2005).

    Article  Google Scholar 

  3. Gosling, S. N. et al. A review of recent developments in climate change science. Part II: The global-scale impacts of climate change. Prog. Phys. Geogr. 35, 443–464 (2011).

    Article  Google Scholar 

  4. Todd, M. C. et al. Uncertainty in climate change impacts on basin-scale freshwater resources—preface to the special issue: The QUEST-GSI methodology and synthesis of results. Hydrol. Earth Syst. Sci. 15, 1035–1046 (2011).

    Article  Google Scholar 

  5. Van Vuuren, D. P. et al. How well do integrated assessment models simulate climate change? Climatic Change 104, 255–285 (2011).

    Article  Google Scholar 

  6. Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).

    Article  CAS  Google Scholar 

  7. Huntingford, C. et al. Simulated resilience of tropical rainforests to CO2-induced climate change. Nature Geosci. 6, 268–273 (2013).

    Article  CAS  Google Scholar 

  8. Chadwick, R., Wu, P. L., Good, P. & Andrews, T. Asymmetries in tropical rainfall and circulation patterns in idealised CO2 removal experiments. Clim. Dynam. 40, 295–316 (2013).

    Article  Google Scholar 

  9. Li, S. & Jarvis, A. Long run surface temperature dynamics of an A-OGCM: The HadCM3 4 × CO2 forcing experiment revisited. Clim. Dynam. 33, 817–825 (2009).

    Article  Google Scholar 

  10. Manabe, S., Bryan, K. & Spelman, M. J. Transient-response of a global ocean atmosphere model to a doubling of atmospheric carbon-dioxide. J. Phys. Oceanogr. 20, 722–749 (1990).

    Article  Google Scholar 

  11. Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change 109, 213–241 (2011).

    Article  CAS  Google Scholar 

  12. Good, P., Gregory, J. M. & Lowe, J. A. A step-response simple climate model to reconstruct and interpret AOGCM projections. Geophys. Res. Lett. 38, L01703 (2011).

    Article  Google Scholar 

  13. Good, P., Gregory, J. M., Lowe, J. A. & Andrews, T. Abrupt CO2 experiments as tools for predicting and understanding CMIP5 representative concentration pathway projections. Clim. Dynam. 40, 1041–1053 (2013).

    Article  Google Scholar 

  14. Andrews, T. & Ringer, M. A. Cloud feedbacks, rapid adjustments, and the forcing-response relationship in a transient CO2 reversibility scenario. J. Clim. 27, 1799–1818 (2014).

    Article  Google Scholar 

  15. Jonko, A. K., Shell, K. M., Sanderson, B. M. & Danabasoglu, G. Climate feedbacks in CCSM3 under changing CO2 forcing. Part II: Variation of climate feedbacks and sensitivity with forcing. J. Clim. 26, 2784–2795 (2013).

    Article  Google Scholar 

  16. Colman, R. & McAvaney, B. Climate feedbacks under a very broad range of forcing. Geophys. Res. Lett. 36, L01702 (2009).

    Article  Google Scholar 

  17. Hansen, J. et al. Efficacy of climate forcings. J. Geophys. Res. 110, D18104 (2005).

    Article  Google Scholar 

  18. Ishizaki, Y. et al. Temperature scaling pattern dependence on representative concentration pathway emission scenarios. Climatic Change 112, 535–546 (2012).

    Article  Google Scholar 

  19. Drijfhout, S. S., Weber, S. L. & van der Swaluw, E. The stability of the MOC as diagnosed from model projections for pre-industrial, present and future climates. Clim. Dynam. 37, 1575–1586 (2011).

    Article  Google Scholar 

  20. Hall, A. The role of surface albedo feedback in climate. J. Clim. 17, 1550–1568 (2004).

    Article  Google Scholar 

  21. Eisenman, I. Factors controlling the bifurcation structure of sea ice retreat. J. Geophys. Res. 117, D01111 (2012).

    Article  Google Scholar 

  22. Seneviratne, S. I. et al. Investigating soil moisture-climate interactions in a changing climate: A review. Earth-Sci. Rev. 99, 125–161 (2010).

    Article  CAS  Google Scholar 

  23. Seneviratne, S. I., Luthi, D., Litschi, M. & Schar, C. Land-atmosphere coupling and climate change in Europe. Nature 443, 205–209 (2006).

    Article  CAS  Google Scholar 

  24. Field, C. B., Jackson, R. B. & Mooney, H. A. Stomatal responses to increased CO2—implications from the plant to the global-scale. Plant Cell Environ. 18, 1214–1225 (1995).

    Article  Google Scholar 

  25. Chadwick, R. & Good, P. Understanding non-linear tropical precipitation responses to CO2 forcing. Geophys. Res. Lett. 40, 4911–4915 (2013).

    Article  Google Scholar 

  26. Good, P. et al. A step-response approach for predicting and understanding non-linear precipitation changes. Clim. Dynam. 39, 2789–2803 (2012).

    Article  Google Scholar 

  27. Collins, W. J. et al. Development and evaluation of an Earth-System model—HadGEM2. Geosci. Model Dev. 4, 1051–1075 (2011).

    Article  Google Scholar 

  28. Martin, G. M. et al. The HadGEM2 family of Met Office Unified Model climate configurations. Geosci. Model Dev. 4, 723–757 (2011).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). N.B. and J.M.G. received financial support from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007–2013), ERC Grant Agreement 247220, project ‘Seachange’. Simulations by J.L.D. were performed as part of the ANR ClimaConf project (grant no. ANR-10-CEPL-0003). H.S. was supported by the SOUSEI program from the Ministry of Education, Culture, Sports, Science and Technology of Japan and the Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment of Japan. N.S. was supported by the Swiss National Science Foundation.

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Authors and Affiliations

  1. Met Office Hadley Centre, Exeter EX1 3PB, UK

    Peter Good, Jason A. Lowe, Timothy Andrews, Andrew Wiltshire, Robin Chadwick, Jeff K. Ridley, Matthew B. Menary & Jonathan M. Gregory

  2. NCAS-Climate, University of Reading, Reading RG6 6AH, UK

    Nathaelle Bouttes & Jonathan M. Gregory

  3. Laboratoire de Météorologie Dynamique, Institut Pierre Simon Laplace, 75005 Paris, France

    Jean Louis Dufresne

  4. Department of Environmental Sciences, Institute for Atmospheric and Climate Science, ETH Zurich, CH-8092 Zurich, Switzerland

    Nathalie Schaller

  5. Atmospheric, Oceanic and Planetary Physics, University of Oxford, Parks Road, Oxford OX1 3PU, UK

    Nathalie Schaller

  6. Climate Risk Assessment Section, Centre for Global Environmental Research, National Institute for Environmental Studies, Tsukuba 305-8506, Japan

    Hideo Shiogama

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Contributions

P.G. conceived the study and wrote the paper. All authors contributed to the scientific interpretation and the paper. T.A., M.B.M., J.L.D., J.M.G., N.S. and H.S. performed experiments.

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Correspondence to Peter Good.

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The authors declare no competing financial interests.

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Good, P., Lowe, J., Andrews, T. et al. Nonlinear regional warming with increasing CO2 concentrations. Nature Clim Change 5, 138–142 (2015). https://doi.org/10.1038/nclimate2498

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