Climatic Change

, Volume 112, Issue 2, pp 217–231 | Cite as

The benefits of quantifying climate model uncertainty in climate change impacts assessment: an example with heat-related mortality change estimates

  • Simon N. GoslingEmail author
  • Glenn R. McGregor
  • Jason A. Lowe


The majority of climate change impacts assessments account for climate change uncertainty by adopting the scenario-based approach. This typically involves assessing the impacts for a small number of emissions scenarios but neglecting the role of climate model physics uncertainty. Perturbed physics ensemble (PPE) climate simulations offer a unique opportunity to explore this uncertainty. Furthermore, PPEs mean it is now possible to make risk-based impacts estimates because they allow for a range of estimates to be presented to decision-makers, which spans the range of climate model physics uncertainty inherent from a given climate model and emissions scenario, due to uncertainty associated with the understanding of physical processes in the climate model. This is generally not possible with the scenario-based approach. Here, we present the first application of a PPE to estimate the impact of climate change on heat-related mortality. By using the estimated impacts of climate change on heat-related mortality in six cities, we demonstrate the benefits of quantifying climate model physics uncertainty in climate change impacts assessment over the more common scenario-based approach. We also show that the impacts are more sensitive to climate model physics uncertainty than they are to emissions scenario uncertainty, and least sensitive to whether the climate change projections are from a global climate model or a regional climate model. The results demonstrate the importance of presenting model uncertainties in climate change impacts assessments if the impacts are to be placed within a climate risk management framework.


Regional Climate Model Emission Scenario Ensemble Member Generalize Extreme Value Climate Change Impact Assessment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was supported with PhD funding from the UK Natural Environment Research Council (NERC) and a Cooperative Awards in Sciences of the Environment (CASE) award from the UK Met Office while the lead author was a PhD candidate at King’s College London, Department of Geography. Jason Lowe was supported by the Joint DECC and Defra Integrated Climate Programme - DECC/Defra (GA01101). Three anonymous reviewers are thanked for taking the time to read and comment on an earlier version of the manuscript.


  1. Ballester J, Giorgi F, Rodo X (2010) Changes in European temperature extremes can be predicted from changes in PDF central statistics. Clim Chang 98:277–284CrossRefGoogle Scholar
  2. Beniston M (2004) The 2003 heat wave in Europe: a shape of things to come? An analysis based on Swiss climatological data and model simulations. Geophysical Research Letters 31: doi: 10.1029/2003GL018857
  3. Bergot M, Cloppet E, Pérarnaud V, Déqué M, Marçais B, Desprez-Loustau ML (2004) Simulation of potential range expansion of oak disease caused by Phytophthora cinnamomi under climate change. Glob Chang Biol 10:1539–1552CrossRefGoogle Scholar
  4. Collins M, Booth BBB, Harris GR, Murphy JM, Sexton DMH, Webb MJ (2006) Towards quantifying uncertainty in transient climate change. Clim Dyn 27:127–147CrossRefGoogle Scholar
  5. Déqué M (2007) Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: model results and statistical correction according to observed values. Glob Planet Chang 57:16–26CrossRefGoogle Scholar
  6. Dessai S (2003) Heat stress and mortality in Lisbon Part II. An assessment of the potential impacts of climate change. Int J Biometeorol 48:37–44CrossRefGoogle Scholar
  7. Donaldson GC, Kovats RS, Keatinge WR, McMicheal AJ (2001) Heat- and cold related mortality and morbidity and climate change. In: Maynard RL (ed) Health effects of climate change in the UK. Department of Health, London, pp 70–80Google Scholar
  8. Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16:147–168CrossRefGoogle Scholar
  9. Gosling SN, McGregor GR, Páldy A (2007) Climate change and heat-related mortality in six cities Part 1: model construction and validation. Int J Biometeorol 51:525–540CrossRefGoogle Scholar
  10. Gosling SN, Lowe JA, McGregor GR, Pelling M, Malamud BD (2009a) Associations between elevated atmospheric temperature and human mortality: a critical review of the literature. Clim Chang 92:299–341CrossRefGoogle Scholar
  11. Gosling SN, McGregor GR, Lowe JA (2009b) Climate change and heat-related mortality in six cities Part 2: climate model evaluation and projected impacts from changes in the mean and variability of temperature with climate change. Int J Biometeorol 53:31–51CrossRefGoogle Scholar
  12. Gosling SN, Lowe JA, McGregor GR (2009c) Projected impacts on heat-related mortality from changes in the mean and variability of temperature with climate change. IOP Conf Ser Earth Environ Sci 6:142010. doi: 10.1088/1755-1307/6/14/142010 CrossRefGoogle Scholar
  13. Gosling SN, Taylor RG, Arnell NW, Todd MC (2011a) A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models. Hydrol Earth Syst Sci 15:279–294CrossRefGoogle Scholar
  14. Gosling SN, Warren R, Arnell NW, Good P, Caesar J, Bernie D, Lowe JA, van der Linden P, O’Hanley JR, Smith SM (2011b) A review of recent developments in climate change science. Part II: the global-scale impacts of climate change. Prog Phys Geogr 35:443–464CrossRefGoogle Scholar
  15. IPCC (2007) Summary for policymakers. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate Change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  16. Kay AL, Davies HN, Bell VA, Jones RG (2009) Comparison of uncertainty sources for climate change impacts: flood frequency in England. Clim Chang 92:41–63CrossRefGoogle Scholar
  17. Kovats RS, Ebi K, Menne B (2003) Methods of assessing human health vulnerability and public health adaptation to climate change. Health and Global Environmental Change Series, No. 1. World Health Organization, Health Canada, United Nations Environment Programme, World Meteorological Organization, CopenhagenGoogle Scholar
  18. Kueppers LM, Snyder MA, Sloan LC, Zavaleta ES, Fulfrost B (2005) Modeled regional climate change and California endemic oak ranges. Proc Natl Acad Sci USA 102:16281–16285CrossRefGoogle Scholar
  19. Lowe JA, Howard TP, Pardaens A, Tinker J, Holt J, Wakelin S, Milne G, Leake J, Wolf J, Horsburgh K, Reeder T, Jenkins G, Ridley J, Dye S, Bradley S (2009) UK Climate Projections science report: Marine and coastal projections. Met Office Hadley Centre, ExeterGoogle Scholar
  20. Mastrandrea MD, Schneider SH (2004) Probabilistic integrated assessment of “Dangerous” climate change. Science 304:571–575CrossRefGoogle Scholar
  21. McGregor GR, Stephenson D, Ferro C (2005) Projected changes in extreme weather and climate events in Europe. In: Kirch W, Menne B (eds) Extreme weather events & public health responses. Springer, Berlin, pp 13–23CrossRefGoogle Scholar
  22. McGuffie K, Henderson-Sellers A (2005) A climate modelling primer (Third Edition). Print ISBN: 9780470857502 Online ISBN: 9780470857618, John Wiley & Sons, Ltd. doi: 10.1002/0470857617
  23. McMichael AJ, Woodruff R, Whetton P, Hennessy K, Nicholls N, Hales S, Woodward A, Kjellstrom T (2003) Human health and climate change in Oceania: Risk assessment 2002. Department of Health and Ageing, Canberra, Commonwealth of Australia, pp 128Google Scholar
  24. Mearns LO, Easterling W, Hays C, Marx D (2001) Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios: Part I. The uncertainty due to spatial scale. Clim Chang 51:131–172CrossRefGoogle Scholar
  25. Meehl GA, Tebaldi C (2004) More intense, more frequent, and longer lasting heatwaves in the 21st Century. Science 305:994–997CrossRefGoogle Scholar
  26. Meehl GA, Covey C, Delworth T, Latif M, McAvney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007a) The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394CrossRefGoogle Scholar
  27. Meehl GA, Stocker TF, Collins WD, Friedlingstein P, Gaye AT, Gregory JM, Kitoh A, Knutti R, Murphy JM, Noda A, Raper SCB, Watterson IG, Weaver AJ, Zhao Z-C (2007b) Global climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate Change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  28. Mitchell TD (2003) Pattern scaling: an examination of the accuracy of the technique for describing future climates. Clim Chang 60:217–242CrossRefGoogle Scholar
  29. Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M, Stainforth DA (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768–772CrossRefGoogle Scholar
  30. 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 365:1993–2028CrossRefGoogle Scholar
  31. Murphy JM, Sexton DMH, Jenkins GJ, Boorman PM, Booth BBB, Brown CC, Clark RT, Collins M, Harris GR, Kendon EJ, Betts RA, Brown SJ, Howard TP, Humphrey KA, McCarthy MP, McDonald RE, Stephens A, Wallace C, Warren R, Wilby R, Wood RA (2009) UK climate projections science report: Climate change projections. Met Office Hadley Centre, ExeterGoogle Scholar
  32. Nakićenović N, Swart R (eds) (2000) Special report on emission scenarios. Cambridge University Press, CambridgeGoogle Scholar
  33. New M, Lopez A, Dessai S, Wilby R (2007) Challenges in using probabilistic climate change information for impact assessments: an example from the water sector. Philos Trans R Soc 365:2117–2131CrossRefGoogle Scholar
  34. Pope VD, Gallani ML, Rowntree PR, Stratton RA (1999) The impact of new physical parametrizations in the Hadley Centre climate model-HadAM3. Clim Dyn 16:123–146CrossRefGoogle Scholar
  35. Prudhomme C, Jakob D, Svensson C (2003) Uncertainty and climate change impact on the flood regime of small UK catchments. J Hydrol 277:1–23CrossRefGoogle Scholar
  36. Schär C, Vidale PL, Lüthi D, Frei C, Häberli C, Liniger MA, Appenzeller C (2004) The role of increasing temperature variability in European summer heatwaves. Nature 427:332–336CrossRefGoogle Scholar
  37. Stainforth A, Aina T, Christensen C, Collins M, Faull N, Frame DJ, Kettleborough JA, Knight S, Martin A, Murphy JM, Piani C, Sexton D, Smith LA, Spicer RA, Thorpe AJ, Allen MR (2005) Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433:403–406CrossRefGoogle Scholar
  38. Tsvetsinkskaya EA, Mearns LO, Mavromatis T, Gao W, McDaniel L, Downtown MW (2003) The effect of spatial scale of climatic change scenarios on simulated maize, winter wheat, and rice production in the Southeastern United States. Clim Chang 60:37–71CrossRefGoogle Scholar
  39. Webb MJ, Senior CA, Williams KD, Sexton MDH, Ringer MA, McAvaney BJ, Colman R, Soden BJ, Andronova NG, Emori S, Tsushima Y, Ogura T, Musat I, Bony S, Taylor K (2006) On uncertainty in feedback mechanisms controlling climate sensitivity in two GCM ensembles. Clim Dyn 27:17–38CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Simon N. Gosling
    • 1
    Email author
  • Glenn R. McGregor
    • 2
  • Jason A. Lowe
    • 3
  1. 1.School of GeographyThe University of NottinghamNottinghamUK
  2. 2.School of EnvironmentThe University of AucklandAucklandNew Zealand
  3. 3.The Met Office Hadley CentreExeterUK

Personalised recommendations