Skip to main content

Advertisement

Log in

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

  • Published:
Climatic Change Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. MAGICC/SCENGEN user manual, p. 42, http://www.cgd.ucar.edu/cas/wigley/magicc/UserMan5.3.v2.pdf

  2. The reader is referred to the IPCC Good Practice Guidance Paper on Multimodel Ensembles (http://www.ipcc-wg2.gov/meetings/EMs/IPCC_EM_MME_GoodPracticeGuidancePaper.pdf) for discussions and recommendations in interpreting multi-model results.

References

  • Annan JD, Hargreaves JC (2011) Understanding the CMIP3 multi-model ensemble. J Clim 24:4529–4538

    Article  Google Scholar 

  • Bouttes N, Gregory JM, Lowe JA (2013) The reversibility of sea level rise. J Clim 26:2502–2513

    Article  Google Scholar 

  • 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):449–469

    Article  Google Scholar 

  • Chadwick R, Good P (2013) Understanding non-linear tropical precipitation responses to CO2 forcings. Geophys Res Lett 10:1029

    Google Scholar 

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

    Article  Google Scholar 

  • Deser C, Phillips A, Bourdette V, Teng H (2012a) Uncertainty in climate change projections: the role of internal variability. Clim Dynam 38:527–546

    Article  Google Scholar 

  • Deser C, Knutti R, Solomon S, Phillips AS (2012b) Communication of the role of natural variability in future North American climate. Nat Clim Chang 2:775–779

    Article  Google Scholar 

  • 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), D19108

    Article  Google Scholar 

  • Fischer EM, Schär C (2009) Future changes in daily summer temperature variability: Driving processes and role for temperature extremes. Clim Dynam 33:917–935

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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):1547–1578

    Article  Google Scholar 

  • Giorgi F (2008) A simple equation for regional climate change and associated uncertainty. J Clim 21(7):1589–1604. doi:10.1175/2007jcli1763.1

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 40:1041–1053

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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):357–375

    Article  Google Scholar 

  • Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90(8):1095–1107

    Article  Google Scholar 

  • Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dynam 37(1–2):407–418

    Article  Google Scholar 

  • Holden PB, Edwards NR (2010) Dimensionally reduced emulation of an AOGCM for application to integrated assessment modelling. Geophys Res Lett 37

  • 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, USA

  • 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 revision

  • Knutti R, Sedláček J (2013) Robustness and uncertainties in the new C 1 MIP5 climate model projections. Nat Clim Chang 3:369–373

    Article  Google Scholar 

  • Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim 23:2739–2758

    Article  Google Scholar 

  • Knutti R et al (2008) A review of uncertainties in global temperature projections over the twenty-first century. J Clim 21:2651–2663

    Article  Google Scholar 

  • Knutti R, Masson D, Gettelman A (2013) Climate model genealogy: Generation CMIP5 and how we got there. Geophys Res Lett 40:1194–1199

    Article  Google Scholar 

  • 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 

  • Mahlstein I, Knutti R, Solomon S, Portmann RW (2011) Early onset of significant local warming in low latitude countries. Environ Res Lett 6:034009

    Article  Google Scholar 

  • Mahlstein I, Portmann RW, Daniel JS, Solomon S, Knutti R (2012) Perceptible changes in regional precipitation in a future climate. Geophys Res Lett 39, L05701

    Google Scholar 

  • Manabe S, Wetherald RT (1980) Distribution of climate change resulting from an increase in CO2 content of the atmosphere. J Atmos Sci 37(1):99–118

    Article  Google Scholar 

  • Masson D, Knutti R (2011) Climate model genealogy. Geophys Res Lett 38, L08703

    Article  Google Scholar 

  • 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):283–313

    Article  Google Scholar 

  • 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):619–644

    Article  Google Scholar 

  • 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 17:3721–3727

    Article  Google Scholar 

  • 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):1383–1394

    Article  Google Scholar 

  • Mitchell JFB, Johns TC, Eagles M, Ingram WJ, Davis RA (1999) Towards the construction of climate change scenarios. Clim Chang 41(3–4):547–581

    Article  Google Scholar 

  • Mitchell TD (2003) Pattern scaling - an examination of the accuracy of the technique for describing future climates. Clim Chang 60(3):217–242

    Article  Google Scholar 

  • 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):747–756

    Article  Google Scholar 

  • 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):1993–2028

    Article  Google Scholar 

  • 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 599

    Google Scholar 

  • 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):6110–6115

    Article  Google Scholar 

  • 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):461–472

    Google Scholar 

  • 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):193–208

    Article  Google Scholar 

  • Sanderson BM, Knutti R (2012) On the interpretation of constrained climate model ensembles. Geophys Res Lett 39, L16708

    Article  Google Scholar 

  • 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):85–98

    Article  Google Scholar 

  • Santer BD, Wigley TML, Schlesinger ME, Mitchell JFB (1990) Developing climate scenarios from equilibrium GCM results, Hamburg, Germany

  • 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):167–193

    Article  Google Scholar 

  • Seneviratne SI, Lüthi D, Litschi M, Schär C (2006) Land-atmosphere coupling and climate change in Europe. Nature 443:205–209

    Article  Google Scholar 

  • 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):321–329

    Article  Google Scholar 

  • 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 Climatology

  • 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):1704–1709

    Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) A summary of the CMIP5 experiment design. Bull Am Meteorol Soc 93:485–498

    Article  Google Scholar 

  • Tebaldi C, Arblaster JM, Knutti R (2011) Mapping model agreement on future climate projections. Geophys Res Lett 38, L23701

    Article  Google Scholar 

  • Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc A 365:2053–2075

    Article  Google Scholar 

  • 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):5–31

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 61:221–230

    Google Scholar 

  • 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):L12705

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Claudia Tebaldi.

Additional information

This article is part of the Special Issue on “A Framework for the Development of New Socio-economic Scenarios for Climate Change Research” edited by Nebojsa Nakicenovic, Robert Lempert, and Anthony Janetos.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 3083 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tebaldi, C., Arblaster, J.M. Pattern scaling: Its strengths and limitations, and an update on the latest model simulations. Climatic Change 122, 459–471 (2014). https://doi.org/10.1007/s10584-013-1032-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10584-013-1032-9

Keywords

Navigation