Climatic Change

, Volume 122, Issue 4, pp 555–566 | Cite as

Robustness of pattern scaled climate change scenarios for adaptation decision support

  • Ana LopezEmail author
  • Emma B. Suckling
  • Leonard A. Smith


Pattern scaling offers the promise of exploring spatial details of the climate system response to anthropogenic climate forcings without their full simulation by state-of-the-art Global Climate Models. The circumstances in which pattern scaling methods are capable of delivering on this promise are explored by quantifying its performance in an idealized setting. Given a large ensemble that is assumed to sample the full range of variability and provide quantitative decision-relevant information, the soundness of applying the pattern scaling methodology to generate decision relevant climate scenarios is explored. Pattern scaling is not expected to reproduce its target exactly, of course, and its generic limitations have been well documented since it was first proposed. In this work, using as a particular example the quantification of the risk of heat waves in Southern Europe, it is shown that the magnitude of the error in the pattern scaled estimates can be significant enough to disqualify the use of this approach in quantitative decision-support. This suggests that future application of pattern scaling in climate science should provide decision makers not just a restatement of the assumptions made, but also evidence that the methodology is adequate for purpose in practice for the case under consideration.


Heat Wave Time Slice Internal Variability Energy Balance Model Pattern Scaling 
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 research was supported by the LSE’s Grantham Research Institute on Climate Change and the Environment and the ESRC Centre for Climate Change Economics and Policy, funded by the Economic and Social Research Council and Munich Re. EBS acknowledges support from the NERC EQUIP project (NE/H003479/1). LAS gratefully acknowledges the continuing support of Pembroke College, Oxford. We thank all the member of the public who participate in the project, and the team for their technical support.

Supplementary material

10584_2013_1022_MOESM1_ESM.pdf (227 kb)
(PDF 226 KB)


  1. Ashfaq M, Skinner C, Diffenbaugh N (2010) Influence of sst biases on future climate change projections. Clim Dyn. doi: 10.1007/s00382-010-0875-2
  2. Compo GP, Sardeshmukh P (2010) Removing enso related variations from the climate record. J Clim 23:1957–1978CrossRefGoogle Scholar
  3. CSIRO (2007) climate change in Australia: technical report. Csiro technical reportGoogle Scholar
  4. Desser C, Walsh J, Timlin M (2000) Arctic sea ice variability in the context of recent atmospheric circulation trends. J Clim 13:617–633CrossRefGoogle Scholar
  5. Fletcher C, Kushner P, Hall A, Qu X (2009) Circulation responses to snow albedo feedback in climate change. Geophys Res Lett 36(L09):702Google Scholar
  6. Frame DJ, Aina T, Christensen C, Faull N, Knight S, Piani C, Rosier S, Yamazaki K, Yamazaki Y, Allen M (2009) The bbc climate change experiment: design of the coupled model ensemble. Phil Trans R Soc 367:855–870CrossRefGoogle Scholar
  7. Frieler K, Meinshausen M, Mengel M (2012) A scaling approach to probabilistic assessment of regional climate change. J Clim. doi: 10.1175/JCLI-D-11-00199.1
  8. Ghil M (2012) Climate variability: nonlinear and random effects. In: North FZGR, Pyle J (eds) Encyclopedia of Atmospheric Sciences. Elsevier, pp 1–6Google Scholar
  9. Giorgi F, Francisco R (2000) Evaluating uncertainties in the prediction of regional climate change. Geophys Res Lett 27:1295–1298CrossRefGoogle Scholar
  10. Good P, Barring C, Giannakopoulos T, Palutikof J (2006) Non-linear regional relationships between climate extremes and annual mean temperatures in model projections for 1961-2099 over europe. Clim Res 31:19–34CrossRefGoogle Scholar
  11. Hall A, Qu X, Neelin J (2008) Improving predictions of summer climate change in the united states. Geophys Res Lett 35(L01):702Google Scholar
  12. Hallegate S (2009) Strategies to adapt to an uncertain climate change. Glob Environ Chang 19:240–247CrossRefGoogle Scholar
  13. Harris GR, Sexton DMH, Booth BBB, Collins M, Murphy JM, Webb MJ (2006) Frequency distributions of transient regional climate change from perturbed physics ensembles of general circulation model simulations. Clim Dyn 27:357–375CrossRefGoogle Scholar
  14. Holland M, Bitz C (2003) Polar amplification of climate change in coupled models. Clim Dyn 21:221–232CrossRefGoogle Scholar
  15. Huntingford C, Booth B, Sitch S, Gedney N, Lowe J, Liddicoat S, Mercado L, Best M, Weedon G, Fisher RA, Good P, Zelazowski P, Spessa AC, Jones DC (2010) Imogen: an intermediate complexity model to evalaute terrestrial impacts of a changing climate. Geosci Model Dev 3:1161–1184CrossRefGoogle Scholar
  16. Jones CD, Palmer JR (1998) Spinup methods for HADCM3L. Hadley Centre for climate prediction and research, Meteorological Office, BracknelGoogle Scholar
  17. Knopf B, Held H, Schellnhuber HJ (2005) Forced versus coupled dynamics in earth system modelling and prediction. Nonlinear Process Geophys 12:311CrossRefGoogle Scholar
  18. Lawrence P, Chase T (2010) Investigating the climate impacts of global land cover change in the community climate system model. Int J Climatol 30:2066–2087CrossRefGoogle Scholar
  19. Macadam I, Pitman A, Whetton P, Abramowitz G (2010) Ranking climate models by performance using actual values and anomalies: implications for climate change impacts assessments. Geophys Res Lett 37(L16):704Google Scholar
  20. Mitchell JFB, Johns M, Eagles M, Ingram W, Davis R (1999) Towards the construction of climate change scenarios. Clim Change 41:547–581CrossRefGoogle Scholar
  21. Mitchell TD (2003) Pattern scaling. An examination of the accuracy of the technique for describing future climates. Clim Change 60:217–242CrossRefGoogle Scholar
  22. Mitchell TD, Carter T, Jones P, Hulme M, New M (2004) A comprehensive set of high-resolution grids of monthly climate for europe and the globe: the observed record (1901-2000) and 16 scenarios (2001–2100). Tyndall centre for climate change research working paper 55Google Scholar
  23. Moss R, Babiker M, Brinkman S, Calvo E, Carter T, Edmonds J, Elgizouli I, Emori S, Erda L, Hibbard K, Jones R, Kainuma M, Kelleher J, Lamarque JF, Manning M, Matthews B, Meehl J, Meyer L, Mitchell J, Nakicenovic N, ONeill B, Pichs R, Riahi K, Rose S, Runci P, Stouffer R, van Vuuren D, Weyant J, Wilbanks T, van Ypersele JP, Zurek M (2008) Towards new scenarios for analysis of emissions, climate change, impacts, and response strategies. Intergovernmental panel on climate changeGoogle Scholar
  24. Moss R, Edmonds J, Hibbard K, Manning M, Rose S, van Vuuren D, Carter T, Emori S, Kainuma M, Kram T, Meehl G, Mitchell J, Nakicenovic N, Riahi K, Smith S, Thomson RSA, Weyant J, Wilbanks T (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756CrossRefGoogle Scholar
  25. Murphy J (2009) Uk climate projections science report: climate change projections. Met office hadley centre technical reportGoogle Scholar
  26. 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
  27. Nakicenovic N, Swart R (2000) Special report on emissions scenario. Cambridge University Press, CambridgeGoogle Scholar
  28. Osborn T (2009) A user guide for climgen: a flexible tool for generating monthly climate data sets and scenarios. Climatic research unit. University of East Anglia, Norwich. Available online at Google Scholar
  29. Petoukov V, Semenov V (2010) A link between reduced barents-kara sea ice and cold winter extremes over northern continents. J Geophys Res 115:D2111CrossRefGoogle Scholar
  30. Ranger N, Millner A, Dietz S, Fankhauser S, Lopez A, Ruta G (2009) Adaptation in the uk: a decision making process. Grantham/cccep policy briefGoogle Scholar
  31. Rowlands DJ, Frame DJ, Ackerley D, Aina T, Booth BBB, Christensen C, Collins M, Faull N, Forest CE, Grandey BS, Gryspeerdt E, Highwood EJ, Ingram WJ, Knight S, Lopez A, Massey N, McNamara F, Meinshausen N, Piani C, Rosier SM, Sanderson BM, Smith LA, Stone DA, Thurston M, Yamazaki K, Yamazaki YH, Allen MR (2012) Broad range of 2050 warming from an observationally constrained large climate model ensemble. Nat Geosci 5:256–260CrossRefGoogle Scholar
  32. Ruosteenoja K, Tuomenvirta H, Jylh K (2007) Gcm-based regional temperature and precipitation change estimates for europe under four sres scenarios applying a super-ensemble pattern-scaling method. Clim Chang 81:193–208CrossRefGoogle Scholar
  33. Schaer C, Vidale P, Luthi D, Frei C, Haberlu C, Liniger M, Appenzeller C (2004) The role of increasing temperature variability in european summer heat waves. Nature 427:332–336CrossRefGoogle Scholar
  34. Solomon S, DQ (2007) Climate Change 2007: the physical science basis. Contribution of working group 1 to the fourth assessment report of the intergovernmental panel on climate changeGoogle Scholar
  35. Stott P, Stone DA, Allen M (2004) Human contribution to the european heatwave of 2003. Nature 432:610–614CrossRefGoogle Scholar
  36. The climgen model. Tech. rep. Available online at
  37. Todd MC, Taylor RG, Osborn TJ, Kingston DG, Arnell NW, Gosling SN (2011) Uncertainty in climate change impacts on basin-scale freshwater resources - preface to the spetial issue: the quest-gsi methodology and synthesis of results. Hydrol Earth Syst Sci 15:1035–1046CrossRefGoogle Scholar
  38. Warren R, de la Nava Santos S, Arnell NW, Bane M, Barker T, Barton C, Ford R, Füssel HM, Hankin RKS, Klein R, Linstead C, Kohler J, Mitchell TD, Osborn TJ, Pan H, Raper SCB, Riley G, Schellnhüber HJ, Winne S, Anderson D (2008) Development and illustrative outputs of the community integrated assessment system (cias), a multi-institutional modular integrated assessment approach for modelling climate change. Environ Model Softw 23(5):592–610. doi: 10.1016/j.envsoft.2007.09.002 CrossRefGoogle Scholar
  39. Warren R, Price J, Fischlin A, de la Nava Santos S, Midgley G (2010) Increasing impacts of climate change upon ecosystems with increasing global mean temperature rise. Clim Change. doi: 10.1007/s10584-010-9923-5
  40. Warren R, RMS Y, Osborn T, de la Nava Santos S (2012) European drought regimes under mitigated and unmitigated climate change: application of the community integrated assessment system (cias). Clim Res 51:105–123CrossRefGoogle Scholar
  41. Warren R, VanDerWal J, Price J, Welbergen JA, Atkinson I, Ramirez-Villegas J, Osborn TJ, Jarvis A, Shoo LP, Williams SE, Lowe J (2013) Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nat Clim Chang 3:678–682. doi: 10.1038/NCLIMATE1887 CrossRefGoogle Scholar
  42. Watterson IG (2008) Calculation of probability density functions for temperature and precipitation change under global warming. J Geophys Res 113(D12). doi: 10.1029/2007JD009254
  43. Wilby R, Dessai S (2010) Robust adaptation to climate change. Weather 65:180–185. doi: 10.1002/wea.543 CrossRefGoogle Scholar
  44. Wilby R, Troni J, Biot Y, Tedd L, Hewitson B, Smith D, Sutton R (2009) A review of climate risk information for adaptation and development planning. Int J Climatol. doi: 10.1002/joc.1839

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Ana Lopez
    • 1
    • 2
    Email author
  • Emma B. Suckling
    • 2
  • Leonard A. Smith
    • 2
  1. 1.CCCEPLondonUK
  2. 2.Centre for the Analysis of Time SeriesLondon School of EconomicsLondonUK

Personalised recommendations