Climatic Change

, Volume 122, Issue 3, pp 459–471 | Cite as

Pattern scaling: Its strengths and limitations, and an update on the latest model simulations

  • Claudia TebaldiEmail author
  • Julie M. Arblaster


We review the ideas behind the pattern scaling technique, and focus on its value and limitations given its use for impact assessment and within integrated assessment models. We present estimates of patterns for temperature and precipitation change from the latest transient simulations available from the Coupled Model Inter-comparison Project Phase 5 (CMIP5), focusing on multi-model mean patterns, and characterizing the sources of variability of these patterns across models and scenarios. The patterns are compared to those obtained from the previous set of experiments, under CMIP3. We estimate the significance of the emerging differences between CMIP3 and CMIP5 results through a bootstrap exercise, while also taking into account the fundamental differences in scenario and model ensemble composition. All in all, the robustness of the geographical features in patterns of temperature and precipitation, when computed as multi-model means, is confirmed by this comparison. The intensity of the change (in both the warmer and cooler areas with respect to global temperature change, and the drier and wetter regions) is overall heightened per degree of global warming in the ensemble mean of the new simulations. The presence of stabilized scenarios in the new set of simulations allows investigation of the performance of the technique once the system has gotten close to equilibrium. Overall, the well established validity of the technique in approximating the forced signal of change under increasing concentrations of greenhouse gases is confirmed.


Atlantic Meridional Overturning Circulation Precipitation Change Radiative Forcings Internal Variability Multimodel Ensemble 
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.



We thank the editors, Dr. Tom Wigley, Dr. Reto Knutti and two anonymous reviewers for their comments and suggestions.

We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison, and the Working Group on Coupled Modeling of the World Climate Research Programme (WCRP) for their roles in making available the WCRP CMIP3 and CMIP5 multimodel datasets. Support of these datasets is provided by the Office of Science, US Department of Energy (DOE). Portions of this study were supported by the Office of Science, Biological, and Environmental Research, US DOE (Grant DE-SC0004956 and Cooperative Agreement No. DE-FC0297ER62402). The National Center for Atmospheric Research is funded by the National Science Foundation.

Supplementary material

10584_2013_1032_MOESM1_ESM.docx (3 mb)
ESM 1 (DOCX 3083 kb)


  1. Annan JD, Hargreaves JC (2011) Understanding the CMIP3 multi-model ensemble. J Clim 24:4529–4538CrossRefGoogle Scholar
  2. Bouttes N, Gregory JM, Lowe JA (2013) The reversibility of sea level rise. J Clim 26:2502–2513CrossRefGoogle Scholar
  3. Cabre MF, Solman SA, Nunez MN (2010) Creating regional climate change scenarios over southern South America for the 2020’s and 2050’s using the pattern scaling technique: Validity and limitations. Clim Change 98(3–4):449–469CrossRefGoogle Scholar
  4. Chadwick R, Good P (2013) Understanding non-linear tropical precipitation responses to CO2 forcings. Geophys Res Lett 10:1029Google Scholar
  5. Chadwick R, Wu P, Good P, Andrews T (2013) Asymmetries in tropical rainfall and circulation patterns in idealised CO2 removal experiments. Clim Dynam 40:295–316CrossRefGoogle Scholar
  6. Deser C, Phillips A, Bourdette V, Teng H (2012a) Uncertainty in climate change projections: the role of internal variability. Clim Dynam 38:527–546CrossRefGoogle Scholar
  7. Deser C, Knutti R, Solomon S, Phillips AS (2012b) Communication of the role of natural variability in future North American climate. Nat Clim Chang 2:775–779CrossRefGoogle Scholar
  8. Dessai S, Lu XF, Hulme M (2005) Limited sensitivity analysis of regional climate change probabilities for the 21st century. J Geophys Res Atmos 110(D19), D19108CrossRefGoogle Scholar
  9. Fischer EM, Schär C (2009) Future changes in daily summer temperature variability: Driving processes and role for temperature extremes. Clim Dynam 33:917–935CrossRefGoogle Scholar
  10. Fordham DA, Wigley TML, Brook BW (2011) Multi-model climate projections for biodiversity risk assessments. Ecol Appl 21(8):3317–3331. doi: 10.1890/11-0314.1 CrossRefGoogle Scholar
  11. Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: Recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27(12):1547–1578CrossRefGoogle Scholar
  12. Giorgi F (2008) A simple equation for regional climate change and associated uncertainty. J Clim 21(7):1589–1604. doi: 10.1175/2007jcli1763.1 CrossRefGoogle Scholar
  13. Good P et al (2012) A step-response approach for predicting and understanding non-linear precipitation changes. Clim Dynam 39:2789–2803CrossRefGoogle Scholar
  14. Good P, Gregory JM, Lowe JA, Andrews T (2013) Abrupt CO2 experiments as tools for predicting and understanding CMIP5 representative concentration pathway projections. Clim Dynam 40:1041–1053CrossRefGoogle Scholar
  15. Harris GR, Collins M, Sexton DMH, Murphy JM, Booth BBB (2010) Probabilistic projections for 21st century European climate. Nat Hazards Earth Syst Sci 10(9):2009–2020. doi: 10.5194/nhess-10-2009-2010 CrossRefGoogle Scholar
  16. 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 Dynam 27(4):357–375CrossRefGoogle Scholar
  17. Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90(8):1095–1107CrossRefGoogle Scholar
  18. Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dynam 37(1–2):407–418CrossRefGoogle Scholar
  19. Holden PB, Edwards NR (2010) Dimensionally reduced emulation of an AOGCM for application to integrated assessment modelling. Geophys Res Lett 37Google Scholar
  20. IPCC (2013) Climate Change 2013. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
  21. Ishizaki Y, Shiogama H, Emori S, Yokohata T, Nozawa T, Ogura T, Abe M, Yoshimori M, Takahashi K (2012) Temperature scaling pattern dependence on representative concentration pathway emission scenarios. Climatic Change, In revisionGoogle Scholar
  22. Knutti R, Sedláček J (2013) Robustness and uncertainties in the new C 1 MIP5 climate model projections. Nat Clim Chang 3:369–373CrossRefGoogle Scholar
  23. Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim 23:2739–2758CrossRefGoogle Scholar
  24. Knutti R et al (2008) A review of uncertainties in global temperature projections over the twenty-first century. J Clim 21:2651–2663CrossRefGoogle Scholar
  25. Knutti R, Masson D, Gettelman A (2013) Climate model genealogy: Generation CMIP5 and how we got there. Geophys Res Lett 40:1194–1199CrossRefGoogle Scholar
  26. Lustenberger A, Knutti R, Fischer EM (2013) The potential of pattern scaling for projecting temperature-related extreme indices. Int J Climatol. doi: 10.1002/joc.3659 Google Scholar
  27. Mahlstein I, Knutti R, Solomon S, Portmann RW (2011) Early onset of significant local warming in low latitude countries. Environ Res Lett 6:034009CrossRefGoogle Scholar
  28. Mahlstein I, Portmann RW, Daniel JS, Solomon S, Knutti R (2012) Perceptible changes in regional precipitation in a future climate. Geophys Res Lett 39, L05701Google Scholar
  29. Manabe S, Wetherald RT (1980) Distribution of climate change resulting from an increase in CO2 content of the atmosphere. J Atmos Sci 37(1):99–118CrossRefGoogle Scholar
  30. Masson D, Knutti R (2011) Climate model genealogy. Geophys Res Lett 38, L08703CrossRefGoogle Scholar
  31. May W (2008) Climatic changes associated with a global “2 °C-stabilization” scenario simulated by the ECHAM5/MPI-OM coupled climate model. Clim Dynam 31(2–3):283–313CrossRefGoogle Scholar
  32. May W (2012) Assessing the strength of regional changes in near-surface climate associated with a global warming of 2°C. Clim Chang 110(3–4):619–644CrossRefGoogle Scholar
  33. Meehl GA, Washington WM, Ammann CM, Arblaster JM, Wigley TML, Tebaldi C (2004) Combinations of natural and anthropogenic forcings in twentieth-century climate. J Clim 17:3721–3727CrossRefGoogle Scholar
  34. Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007) The WCRP CMIP3 multimodel dataset - a new era in climate change research. Bull Am Meteorol Soc 88(9):1383–1394CrossRefGoogle Scholar
  35. Mitchell JFB, Johns TC, Eagles M, Ingram WJ, Davis RA (1999) Towards the construction of climate change scenarios. Clim Chang 41(3–4):547–581CrossRefGoogle Scholar
  36. Mitchell TD (2003) Pattern scaling - an examination of the accuracy of the technique for describing future climates. Clim Chang 60(3):217–242CrossRefGoogle Scholar
  37. Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Wilbanks TJ (2010) The next generation of scenarios for climate change research and assessment. Nature 463(7282):747–756CrossRefGoogle Scholar
  38. 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 Math Phys Eng Sci 365(1857):1993–2028CrossRefGoogle Scholar
  39. Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grubler A, Jung T, Kram T, La E, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Riahi K, Roehrl A, Rogner H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Victor NS, Dadi Z (2000) Special report on emissions scenarios: A special report of Working Group III of the intergovernmental panel on climate change. C.U. Press, Cambridge, p 599Google Scholar
  40. Neelin JD, Munnich M, Su H, Meyerson JE, Holloway CE (2006) Tropical drying trends in global warming models and observations. Proc Natl Acad Sci U S A 103(16):6110–6115CrossRefGoogle Scholar
  41. Raisanen J, Ruokolainen L (2006) Probabilistic forecasts of near-term climate change based on a resampling ensemble technique. Tellus Ser A Dyn Meteorol Oceanogr 58(4):461–472Google Scholar
  42. Ruosteenoja K, Tuomenvirta H, Jylha 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(1):193–208CrossRefGoogle Scholar
  43. Sanderson BM, Knutti R (2012) On the interpretation of constrained climate model ensembles. Geophys Res Lett 39, L16708CrossRefGoogle Scholar
  44. Sanderson MG, Hemming DL, Betts RA (2011) Regional temperature and precipitation changes under high-end (> = 4 °C) global warming. Philos Trans R Soc A Math Phys Eng Sci 369(1934):85–98CrossRefGoogle Scholar
  45. Santer BD, Wigley TML, Schlesinger ME, Mitchell JFB (1990) Developing climate scenarios from equilibrium GCM results, Hamburg, GermanyGoogle Scholar
  46. Schlesinger ME, Malyshev S, Rozanov EV, Yang FL, Andronova NG, De Vries B, Grubler A, Jiang KJ, Masui T, Morita T, Penner J, Pepper W, Sankovski A, Zhang Y (2000) Geographical distributions of temperature change for scenarios of greenhouse gas and sulfur dioxide emissions. Technol Forecast Soc Chang 65(2):167–193CrossRefGoogle Scholar
  47. Seneviratne SI, Lüthi D, Litschi M, Schär C (2006) Land-atmosphere coupling and climate change in Europe. Nature 443:205–209CrossRefGoogle Scholar
  48. Shiogama H, Hanasaki N, Masutomi Y, Nagashima T, Ogura T, Takahashi K, Hijioka Y, Takemura T, Nozawa T, Emori S (2010) Emission scenario dependencies in climate change assessments of the hydrological cycle. Clim Chang 99(1–2):321–329CrossRefGoogle Scholar
  49. Shiogama H, Stone DA, Nagashima T, Nozawa T, Emori S (2012) On the linear additivity of climate forcing-response relationships at global and continental scales. International Journal of ClimatologyGoogle Scholar
  50. Solomon S, Plattner GK, Knutti R, Friedlingstein, P (2009) Irreversible climate change due to carbon dioxide emissions. Proc Natl Acad Sci U S A, 106(6):1704–1709Google Scholar
  51. Taylor KE, Stouffer RJ, Meehl GA (2012) A summary of the CMIP5 experiment design. Bull Am Meteorol Soc 93:485–498CrossRefGoogle Scholar
  52. Tebaldi C, Arblaster JM, Knutti R (2011) Mapping model agreement on future climate projections. Geophys Res Lett 38, L23701CrossRefGoogle Scholar
  53. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc A 365:2053–2075CrossRefGoogle Scholar
  54. van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque JF, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: An overview. Clim Chang 109(1–2):5–31CrossRefGoogle Scholar
  55. Watterson IG (2008) Calculation of probability density functions for temperature and precipitation change under global warming. J Geophys Res Atmos 113(D12), D12106. doi: 10.1029/2007JD009254 CrossRefGoogle Scholar
  56. Watterson IG, Whetton PH (2011) Joint PDFs for Australian climate in future decades and an idealized application to wheat crop yield. Aust Meteorol Oceanogr J 61:221–230Google Scholar
  57. Wu P, Wood R, Ridley J, Lowe J (2010) Temporary acceleration of the hydrological cycle in response to a CO2 rampdown. Geophys Res Lett 37(12):L12705CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Climate and Global Dynamics, National Center for Atmospheric Research (NCAR)BoulderUSA
  2. 2.NCAR and Bureau of MeteorologyMelbourneAustralia

Personalised recommendations