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.

The importance of including variability in climate change projections used for adaptation

Nature Climate Change volume 5, pages 931–936 (2015)Cite this article

Subjects

This article has been updated

Abstract

Our understanding of mankind’s influence on the climate is largely based on computer simulations1,2. Model output is typically averaged over several decades3 so that the anthropogenic climate change signal stands out from the largely unpredictable ‘noise’ of climate variability. Similar averaging periods (30-year) are used for regional climate projections4,5,6 to inform adaptation. According to two such projections, UKCIP02 (ref. 4) and UKCP09 (ref. 6), the UK will experience ‘hotter drier summers and warmer wetter winters’7,8 in the future. This message is about a typical rather than any individual future season, and these projections should not be compared directly to observed weather as this neglects the sizeable contribution from year-to-year climate variability. Therefore, despite the apparent contradiction with the messages, it is a fallacy to suggest the recent cold UK winters like 2009/2010 disprove human-made climate change9. Nevertheless, such claims understandably cause public confusion and doubt10. Here we include year-to-year variability to provide projections for individual seasons. This approach has two advantages. First, it allows fair comparisons with recent weather events, for instance showing that recent cold winters are within projected ranges. Second, it allows the projections to be expressed in terms of the extreme hot, cold, wet or dry seasons that impact society, providing a better idea of adaptation needs.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

Rent or Buy

All prices are NET prices.

Figure 1: Projections for individual seasons in response to historical forcings followed by the A1B scenario.
Figure 2: Comparison of 1-year PDFs with 30-year PDFs for anomalies in the four England/Wales variables relative to 1961–1990.
Figure 3: Time series of probabilities for seasons that become less likely under climate change.
Figure 4: Time series of probabilities for extreme seasons that become more likely under climate change.

Change history

  • 06 August 2015

    In the version of this Letter originally published, the use of UK versus England/Wales was inconsistent. The text has been amended to clarify throughout. These errors have been corrected in all versions of the Letter.

References

  1. 1

    Manabe, S. & Wetherald, R. T. Thermal equilibrium of the atmosphere with a given distribution of relative humidity. J. Atmos. Sci. 24, 241–259 (1967).

    CAS  Article  Google Scholar 

  2. 2

    Hansen, J. et al. Climate impact of increasing atmospheric carbon dioxide. Science 213, 957–966 (1981).

    CAS  Article  Google Scholar 

  3. 3

    Stott, P. A. & Tett, S. F. B. Scale-dependent detection of climate change. J. Clim. 11, 3282–3294 (1998).

    Article  Google Scholar 

  4. 4

    Hulme, M. et al. Climate-Change Scenarios for the United Kingdom: The UKCIP02 Scientific Report (Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, 2002)

  5. 5

    Christensen, J. H. et al. in Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) Ch. 11, 847–940 (IPCC, Cambridge Univ. Press, 2007).

    Google Scholar 

  6. 6

    Murphy, J. M. et al. UK Climate Projections Science Report: Climate Change Projections (Met Office Hadley Centre, 2009).

    Google Scholar 

  7. 7

    Adapting to climate change in the UK, POST Note July 2006 Number 267 (POST, 2006); http://www.parliament.uk/briefing-papers/POST-PN-267.pdf

  8. 8

    Adapting to Climate Change: UK Climate Projections 2009 (DEFRA, 2011); https://www.gov.uk/government/publications/adapting-to-climate-change-uk-climate-projections-2009

  9. 9

    Brown, M. Snow chaos: And they still claim it is global warming. Daily Express (6 January 2010)

  10. 10

    Spence, A., Venables, D., Pidgeon, N. F., Poortinga, W. & Demski, C. Public Perceptions of Climate Change and Energy Futures in Britain. Summary Findings of a Survey Conducted in January–March 2010 (Technical Report) (Cardiff Univ., 2010); http://www.understanding-risk.org

  11. 11

    Räisänen, J. & Ruokolainen, L. Estimating present climate in a warming world: A model-based approach. Clim. Dynam. 31, 573–585 (2008).

    Article  Google Scholar 

  12. 12

    Räisänen, J. & Ylhäisi, J. S. Cold months in a warming climate. Geophys. Res. Lett. 38, L22704 (2011).

    Article  Google Scholar 

  13. 13

    Meehl, G. A. et al. The WCRP CMIP3 multi-model dataset: A new era in climate change research. Bull. Am. Meteorol. Soc. 88, 1383–1394 (2007).

    Article  Google Scholar 

  14. 14

    Sexton, D. M. H., Murphy, J. M., Collins, M. & Webb, M. J. Multivariate prediction using imperfect climate models part I: Outline of methodology. Clim. Dynam. 38, 2513–2542 (2012).

    Article  Google Scholar 

  15. 15

    Harris, G. R., Sexton, D. M. H., Booth, B. B. B., Collins, M. & Murphy, J. M. Probabilistic projections of transient climate change. Clim. Dynam. 40, 2937–2972 (2013).

    Article  Google Scholar 

  16. 16

    Rougier, J. Probabilistic inference for future climate using an ensemble of climate model evaluations. Climatic Change 81, 247–264 (2007).

    Article  Google Scholar 

  17. 17

    Stott, P. A., Stone, D. A. & Allen, M. R. Human contribution to the European heatwave of 2003. Nature 432, 610–614 (2004).

    CAS  Article  Google Scholar 

  18. 18

    Pall, P. et al. Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature 470, 382–386 (2011).

    CAS  Article  Google Scholar 

  19. 19

    Cattiaux, J. et al. Winter 2010 in Europe: A cold extreme in a warming climate. Geophys. Res. Lett. 37, L20704 (2010).

    Article  Google Scholar 

  20. 20

    Pidgeon, N. & Fischhoff, B. The role of social and decision sciences in communicating uncertain climate risks. Nature Clim. Change 1, 35–41 (2011).

    Article  Google Scholar 

  21. 21

    Knutti, R. The end of model democracy? Climatic Change 102, 395–404 (2010).

    Article  Google Scholar 

  22. 22

    Sexton, D. M. H. & Murphy, J. M. Multivariate prediction using imperfect climate models part II: Robustness of methodological choices and consequences for climate sensitivity. Clim. Dynam. 38, 2543–2558 (2012).

    Article  Google Scholar 

  23. 23

    Hulme, M., Dessai, S., Lorenzoni, I. & Nelson, D. R. Unstable climates: Exploring the statistical and social constructions of ’normal’ climate. Geoforum 40, 197–206 (2009).

    Article  Google Scholar 

  24. 24

    Scaife, A. A. et al. Climate change and stratosphere–troposphere interaction. Clim. Dynam. 38, 2089–2097 (2012).

    Article  Google Scholar 

  25. 25

    Scaife, A. A. et al. Improved Atlantic winter blocking in a climate model. Geophys. Res. Lett. 38, L23703 (2011).

    Article  Google Scholar 

  26. 26

    Bony, S. & Dufresne, J.-L. Marine boundary layer clouds at the heart of cloud feedback uncertainties in climate models. Geophys. Res. Lett. 32, L20806 (2005).

    Article  Google Scholar 

  27. 27

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

    Article  Google Scholar 

  28. 28

    Perry, M. C. & Hollis, D. M. The generation of monthly gridded datasets for a range of climatic variables over the UK. Int. J. Clim. 25, 1041–1054 (2005).

    Article  Google Scholar 

  29. 29

    Nakicenovic, N. J. et al. Special Report on Emissions Scenarios (eds Nakicenovic, N. & Swart, R.) (IPCC, Cambridge Univ. Press, 2000).

    Google Scholar 

Download references

Acknowledgements

We would like to thank L. Kendon for encouraging us to think about this issue. We would also like to thank S. Belcher, B. Booth, S. Brown, K. Humphrey, V. Pope, A. Scaife, R. Street and colleagues at the Isaac Newton Workshop on ‘Mathematical and statistical approaches to climate modelling and prediction’ and, in particular, J. Murphy for comments. This work was supported by the Met Office Hadley Centre Climate Programme—DECC/Defra (GA01101). We acknowledge the international modelling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modelling (GCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis activity, and the IPCC WG1 TSU for technical support. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science, US Department of Energy.

Author information

Affiliations

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

    David M. H. Sexton & Glen R. Harris

Authors
  1. David M. H. Sexton
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Glen R. Harris
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.M.H.S. and G.R.H. both conceived the method. D.M.H.S. coded up the solution by modifying the original code of G.R.H. and D.M.H.S. which was used to produce UKCP09. D.M.H.S. drafted the initial version of the manuscript and Supplementary Information and made the plots. Both authors discussed the results and implications, and commented on the manuscript at all stages.

Corresponding author

Correspondence to David M. H. Sexton.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sexton, D., Harris, G. The importance of including variability in climate change projections used for adaptation. Nature Clim Change 5, 931–936 (2015). https://doi.org/10.1038/nclimate2705

Download citation

Further reading

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