Climate Dynamics

, Volume 40, Issue 11–12, pp 2937–2972 | Cite as

Probabilistic projections of transient climate change

  • Glen R. Harris
  • David M. H. Sexton
  • Ben B. B. Booth
  • Mat Collins
  • James M. Murphy


This paper describes a Bayesian methodology for prediction of multivariate probability distribution functions (PDFs) for transient regional climate change. The approach is based upon PDFs for the equilibrium response to doubled carbon dioxide, derived from a comprehensive sampling of uncertainties in modelling of surface and atmospheric processes, and constrained by multiannual mean observations of recent climate. These PDFs are sampled and scaled by global mean temperature predicted by a Simple Climate Model (SCM), in order to emulate corresponding transient responses. The sampled projections are then reweighted, based upon the likelihood that they correctly replicate observed historical changes in surface temperature, and combined to provide PDFs for 20 year averages of regional temperature and precipitation changes to the end of the twenty-first century, for the A1B emissions scenario. The PDFs also account for modelling uncertainties associated with aerosol forcing, ocean heat uptake and the terrestrial carbon cycle, sampled using SCM configurations calibrated to the response of perturbed physics ensembles generated using the Hadley Centre climate model HadCM3, and other international climate model simulations. Weighting the projections using observational metrics of recent mean climate is found to be as effective at constraining the future transient response as metrics based on historical trends. The spread in global temperature response due to modelling uncertainty in the carbon cycle feedbacks is determined to be about 65–80 % of the spread arising from uncertainties in modelling atmospheric, oceanic and aerosol processes of the climate system. Early twenty-first century aerosol forcing is found to be extremely unlikely to be less than −1.7 W m−2. Our technique provides a rigorous and formal method of combining several lines of evidence used in the previous IPCC expert assessment of the Transient Climate Response. The 10th, 50th and 90th percentiles of our observationally constrained PDF for the Transient Climate Response are 1.6, 2.0 and 2.4 °C respectively, compared with the 10–90 % range of 1.0–3.0 °C assessed by the IPCC.


Probabilistic climate projections Uncertainty Perturbed physics ensembles Transient climate response Carbon cycle uncertainty Aerosol forcing Bayesian Observational constraints 

Supplementary material

382_2012_1647_MOESM1_ESM.pdf (619 kb)
Supplementary material 1 (PDF 619 kb)


  1. Ackerley D, Booth BBB, Knight SHE, Highwood EJ, Frame DJ, Allen MR, Rowell DP (2011) Sensitivity of twentieth-century Sahel rainfall to sulfate aerosol and CO2 forcing. J Clim 24:4999–5014. doi:10.1175/JCLI-D-11-00019.1 CrossRefGoogle Scholar
  2. Allen MR, Stott PA, Mitchell JFB, Schnur R, Delworth TL (2000) Quantifying the uncertainty in forecasts of anthropogenic climate change. Nature 407:617–620CrossRefGoogle Scholar
  3. Annan JD, Hargreaves JC (2011) On the generation and interpretation of probabilistic estimates of climate sensitivity. Clim Chang 104:423–436. doi:10.1007/s10584-009-9715-y CrossRefGoogle Scholar
  4. Barnett DN, Brown SJ, Murphy JM, Sexton DMH, Webb MJ (2006) Quantifying uncertainty in changes in extreme event frequency in response to doubled CO2 using a large ensemble of GCM simulations. Clim Dyn 26:489–511CrossRefGoogle Scholar
  5. Boer GJ, Yu B (2003) Dynamical aspects of climate sensitivity. Geophys Res Lett 30:1135. doi:10.1029/2002GL016549 CrossRefGoogle Scholar
  6. Booth BBB, Jones CD, Collins M, Totterdell IJ, Cox PM, Sitch S, Huntingford C, Betts RA, Harris GR, Lloyd J (2012) High sensitivity of future global warming to land carbon cycle processes. Environ Res Lett 7:024002. doi:10.1088/1748-9326/7/2/024002 CrossRefGoogle Scholar
  7. Braganza K, Karoly DJ, Hirst AC, Mann ME, Stott P, Stouffer RJ, Tett SFB (2003) Simple indices of global climate variability and change: part I—variability and correlation structure. Clim Dyn 20:491–502Google Scholar
  8. Brierley CM, Collins M, Thorpe AJ (2010) The impact of perturbations to ocean-model parameters on climate and climate change in a coupled model. Clim Dyn 34:325–343CrossRefGoogle Scholar
  9. Burnham KP, Anderson DA (1998) Model selection and inference: a practical information-theoretic approach. Springer, New YorkCrossRefGoogle Scholar
  10. Canadell JG, Le Quéré C, Raupach MR, Field CB, Buitenhuis ET, Ciais P, Conway TJ, Gillett NP, Houghton RA, Marland G (2007) Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proc Natl Acad Sci USA 104:18866–18870CrossRefGoogle Scholar
  11. Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli RK, Kwon W-T, Laprise R, Magaña-Rueda V, Mearns L, Menéndez CG, Räisänen J, Rinke A, Sarr A, Whetton P (2007) Regional 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
  12. Collins M, Brierley CM, MacVean M, Booth BBB, Harris GR (2007) The sensitivity of the rate of transient climate change to ocean physics perturbations. J Clim 20:2315–2320CrossRefGoogle Scholar
  13. Collins M, Booth BBB, Bhaskaran B, Harris GR, Murphy JM, Sexton DMH, Webb MJ (2011) Climate model errors, feedbacks and forcings. A comparison of perturbed physics and multi-model ensembles. Clim Dyn 36:1737–1766. doi:10.1007/s00382-010-0808-0 CrossRefGoogle Scholar
  14. Cox PM, Betts RA, Jones CD, Spall SA, Totterdell I (2000) Acceleration of global warming due to carbon cycle feedbacks in a coupled climate model. Nature 408:184–187. doi:10.1038/35041539 CrossRefGoogle Scholar
  15. Ferrise R, Moriondo M, Bindi M (2011) Probabilistic assessments of climate change impacts on durum wheat in the Mediterranean region. Nat Hazards Earth Syst Sci 11:1293–1302. doi:10.5194/nhess-11-1293-2011 CrossRefGoogle Scholar
  16. Feulner G, Rahmstorf S (2010) On the effect of a new grand minimum of solar activity on the future climate on Earth. Geophys Res Lett 37:L05707. doi:10.1029/2010GL042710 CrossRefGoogle Scholar
  17. Forster P, Gregory JM (2006) The climate sensitivity and its components diagnosed from earth radiation budget data. J Clim 19:39–52CrossRefGoogle Scholar
  18. Forster P, Ramaswamy V, Artaxo P, Berntsen T, Betts R, Fahey DW, Haywood J, Lean J, Lowe DC, Myhre G, Nganga J, Prinn R, Raga G, Schulz M, Van Dorland R (2007) Changes in atmospheric constituents and in radiative forcing. 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
  19. Friedlingstein P, Cox P, Betts R, Bopp L, von Bloh W, Brovkin V, Cadule P, Doney S, Eby M, Fung I, Bala G, John J, Jones C, Joos F, Kato T, Kawamiya M, Knorr W, Lindsay K, Matthews HD, Raddatz T, Rayner P, Reick C, Roeckner E, Schnitzler K-G, Schnur R, Strassmann K, Weaver AJ, Yoshikawa C, Zeng N (2006) Climate-carbon cycle feedback analysis: results from the C4MIP model intercomparison. J Clim 19:3337–3353CrossRefGoogle Scholar
  20. Fronzek S, Carter TR, Luoto M (2011) Evaluating sources of uncertainty in modelling the impact of probabilistic climate change on sub-arctic palsa mires. Nat Hazards Earth Syst Sci 11:2981–2995. doi:10.5194/nhess-11-2981-2011 CrossRefGoogle Scholar
  21. Furrer R, Sain SR, Nychka D, Meehl GA (2007) Multivariate Bayesian analysis of atmosphere-ocean general circulation models. Environ Ecol Stat 13:249-266Google Scholar
  22. Giorgi F, Francisco R (2000) Uncertainties in regional climate change predictions. A regional analysis of ensemble simulations with the HadCM2 GCM. Clim Dyn 16:169–182CrossRefGoogle Scholar
  23. Giorgi F, Mearns LO (2003) Probability of regional climate change based on the reliability ensemble averaging (REA) method. Geophys Res Lett 30:1629. doi:10.1029/2003GL017130 Google Scholar
  24. 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 transport in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16:147–168CrossRefGoogle Scholar
  25. Greene AM, Goddard L, Lall U (2006) Probabilistic multimodel regional temperature change projections. J Clim 19:4326–4343. doi:10.1175/JCLI3864.1 CrossRefGoogle Scholar
  26. 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
  27. Harris GR, Collins M, Sexton DMH, Murphy JM, Booth BBB (2010) Probabilistic projections for 21st century European climate. Nat Hazards Earth Syst Sci 10:2009–2020CrossRefGoogle Scholar
  28. Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37:407–418. doi:10.1007/s00382-010-0810-6 CrossRefGoogle Scholar
  29. Haywood J, Schulz M (2007) Causes of the reduction in uncertainty in the anthropogenic radiative forcing of climate between IPCC (2001) and IPCC (2007). Geophys Res Lett 34:L20701. doi:10.1029/2007GL030749 CrossRefGoogle Scholar
  30. Huntingford C, Cox PM (2000) An analogue model to derive additional climate change scenarios from existing GCM simulations. Clim Dyn 16:575–586CrossRefGoogle Scholar
  31. Huntingford C, Lowe JA, Booth BBB, Jones CD, Harris GR, Gohar LK, Meir P (2009) Contributions of carbon cycle uncertainty to future climate projection spread. Tellus 61B:355–360Google Scholar
  32. 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
  33. Johns TC, Gregory JM, Ingram WJ, Johnson CE, Jones A, Lowe JA, Mitchell JFB, Roberts DL, Sexton DMH, Stevenson DS, Tett SFB, Woodage MJ (2003) Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emissions scenarios. Clim Dyn 20:583–612Google Scholar
  34. Jones A, Roberts DL, Woodage MJ, Johnson CE (2001) Indirect sulphate aerosol forcing in a climate model with an interactive sulphur cycle. J Geophys Res 106:20293–20310CrossRefGoogle Scholar
  35. Jones CD, Cox PM, Huntingford C (2006) Climate-carbon cycle feedbacks under stabilization: uncertainty and observational constraints. Tellus 58B:603–613Google Scholar
  36. Jones GS, Lockwood M, Stott PA (2012) What influence will future solar activity changes over the 21st century have on projected global near surface temperature changes? J Geophys Res. doi:10.1029/2011JD017013 Google Scholar
  37. Joos F, Prentice IC, Sitch S, Meyer R, Hooss G, Plattner G-K, Gerber S, Hasselmann K (2001) Global warming feedbacks on terrestrial carbon uptake under the Intergovernmental Panel on Climate Change (IPCC) emission scenarios. Global Biogeochem Cycles 15:891–907CrossRefGoogle Scholar
  38. Kiehl JT (2007) Twentieth century climate model response and climate sensitivity. Geophys Res Lett 34. doi:10.1029/2007GL031383
  39. Knight J, Kennedy JJ, Folland C, Harris G, Jones GS, Palmer M, Parker D, Scaife A, Stott P (2009) Do global temperature trends over the last decade falsify climate predictions? [in “State of the Climate in 2008”]. Bull Am Meteorol Soc 90:S22–S23Google Scholar
  40. Knutti R, Allen MR, Friedlingstein P, Gregory JM, Hegerl GC, Meehl GA, Meinshausen M, Murphy JM, Plattner G-K, Raper SCB, Stocker TF, Stott PA, Teng H, Wigley TML (2008) A review of uncertainties in global temperature projections over the twenty-first century. J Clim 21:2651–2663CrossRefGoogle Scholar
  41. Knutti R, Furrer R, Tebaldi C, Cernak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim 23:2739–2758CrossRefGoogle Scholar
  42. Lambert FH, Harris GR, Collins M, Murphy JM, Sexton DMH, Booth BBB (2012) Interactions between perturbations to different Earth system components simulated by a fully-coupled climate model. Clim Dyn. doi:10.1007/s00382-012-1618-3
  43. Li Z, Li C, Chen H, Tsay S-C, Holben B, Huang J, Li B, Maring H, Qian Y, Shi G, Xia X, Yin Y, Zheng Y, Zhuan G (2011) East Asian studies of tropospheric aerosols and their impact on regional climate (EAST-AIRC): an overview. J Geophys Res 116:D00K34. doi:10.1029/2010JD015257 CrossRefGoogle Scholar
  44. Masarie KA, Tans PP (1995) Extension and integration of atmospheric carbon dioxide data into a globally consistent measurement record. J Geopys Res 100:11593–11610 (and: Thomas Conway and Pieter Tans, NOAA/ESRL, Scholar
  45. McAvaney BJ, Le Treut H (2003) The cloud feedback intercomparison project: (CFMIP). CLIVAR exchanges 26—supplementary contributionsGoogle Scholar
  46. McKay MD, Conover WJ, Beckman RJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239–245Google Scholar
  47. McSweeney CF, Jones RG, Booth BBB (2012): Selecting ensemble members to provide regional climate change information. J Clim. doi:10.1175/JCLI-D-11-00526.1
  48. Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007a) The WCRP CMIP3 multi-model dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394CrossRefGoogle Scholar
  49. 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
  50. Menne MJ, Kennedy JJ (2010) Global surface temperatures [in “State of the Climate in 2009”]. Bull Am Meteorol Soc 91(7):S24–S25Google Scholar
  51. Mitchell JFB, Johns TC, Eagles M, Ingram WJ, Davis RA (1999) Towards the construction of climate change scenarios. Clim Chang 41:547–581. doi:10.1023/A:1005466909820 CrossRefGoogle Scholar
  52. 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
  53. 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
  54. 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, Exeter. Available at:
  55. Nakicenovic NJ, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grubler A, Jung TY, Kram T, La Rovere EL, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Raihi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Rooijen S, Victor N, Dadi Z (2000) Emissions scenarios. a special report of working group III of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 599Google Scholar
  56. Pittock AB, Jones RN, Mitchell CD (2001) Probabilities will help us plan for climate change. Nature 413:249CrossRefGoogle Scholar
  57. Pope VD, Gallani ML, Rowntree PR, Stratton RA (2000) The impact of new physical parameterizations in the Hadley Centre climate model: HadAM3. Clim Dyn 16:123–146Google Scholar
  58. Power SB, Delage F, Colman R, Moise A (2012) Consensus on twenty-first-century rainfall projections in climate models more widespread than previously thought. J Clim 25:3792–3809. doi:10.1175/JCLI-D-11-00354.1 CrossRefGoogle Scholar
  59. Räisänen J (2001) CO2-induced climate change in CMIP2 experiments: quantification of agreement and role of internal variability. J Clim 14:2088–2104. doi:10.1175/1520-0442(2001)014<2088:CICCIC>2.0.CO;2 Google Scholar
  60. Randall DA, Wood RA, Bony S, Colman R, Fichefet T, JFyfe J, Kattsov V, Pitman A, JShukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Cilmate models and their evaluation. 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
  61. Raper SCB, Gregory JM, Stouffer RJ (2002) The role of climate sensitivity and ocean heat uptake on AOGCM transient temperature response. J Clim 15:124–130. doi:10.1175/1520-0442(2002)015<0124:TROCSA>2.0.CO;2 Google Scholar
  62. Rougier JC (2007) Probabilistic inference for future climate using an ensemble of climate model evaluations. Clim Chang 81:247–264CrossRefGoogle Scholar
  63. Scaife AA, Spangehl T, Fereday D, Cubasch U, Langematz U, Akiyoshi H, Bekki S, Braesicke P, Butchart N, Chipperfield M, Gettelman A, Hardiman S, Michou M, Rozanov E, Shepherd TG (2012) Climate change and stratosphere–troposphere interaction. Clim Dyn 38:2089–2097. doi:10.1007/s00382-011-1080-7 CrossRefGoogle Scholar
  64. Sexton DMH, Murphy JM (2012) Multivariate prediction using imperfect climate models part II: robustness of methodological choices and consequences for climate sensitivity. Clim Dyn 38:2543–2558. doi:10.1007/s00382-011-1209-8 CrossRefGoogle Scholar
  65. Sexton DMH, Murphy JM, Collins M, Webb MJ (2012) Multivariate prediction using imperfect climate models part I: outline of methodology. Clim Dyn 38:2513–2542. doi:10.1007/s00382-011-1208-9 CrossRefGoogle Scholar
  66. Shine K, Derwent R, Wuebbles D, Morcette JJ (1990) Radiative forcing of climate. In: Houghton J, Jenkins G, Ephraums J (eds) Climate change. The IPCC scientific assessment. Cambridge University Press, Cambridge, pp 45–68Google Scholar
  67. Smith DM, Eade R, Dunstone NJ, Fereday D, Murphy JM, Pohlmann H, Scaife AA (2010) Skilful multi-year predictions of Atlantic hurricane frequency. Nat Geosci. doi:10.1038/NGEO1004 Google Scholar
  68. Solomon S, Qin D, Manning M, Alley RB, Berntsen T, Bindoff NL, Chen Z, Chidthaisong A, Gregory JM, Hegerl GC, Heimann M, Hewitson B, Hoskins BJ, Joos F, Jouzel J, Kattsov V, Lohmann U, Matsuno T, Molina M, Nicholls N, Overpeck J, Raga G, Ramaswamy V, Ren J, Rusticucci M, Somerville R, Stocker TF, Whetton P, Wood RA, Wratt D (2007) Technical summary. 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
  69. Stainforth DA, Aina T, Christensen C, Collins M, 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 433:403–406. doi:10.1038/nature03301 Google Scholar
  70. Stott PA, Jones GS (2012) Observed 21st century temperatures further constrain likely rates of future warming. Atmos Sci Lett 13:151–156. doi:10.1002/asl.383 CrossRefGoogle Scholar
  71. Stott PA, Mitchell JFB, Allen MR, Delworth TL, Gregory JM, Meehl GA, Santer BD (2006) Observational constraints on past attributable warming and predictions of future global warming. J Clim 19:3055–3069CrossRefGoogle Scholar
  72. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. doi:10.1175/BAMS-D-11-00094.1 CrossRefGoogle Scholar
  73. Tebaldi C, Knutti R (2007) The use of the multimodel ensemble in probabilistic climate projections. Philos Trans R Soc A 365:2053–2075CrossRefGoogle Scholar
  74. Tebaldi C, Sansó B (2009) Joint projections of temperature and precipitation change from multiple climate models: a hierarchical Bayesian approach. J R Stat Soc A 172:83–106CrossRefGoogle Scholar
  75. Tebaldi C, Smith RL, Nychka D, Mearns LO (2005) Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multimodel ensembles. J Clim 18:1524–1540. doi:10.1175/JCLI3363.1 CrossRefGoogle Scholar
  76. van der Linden P, Mitchell JFB (eds) (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, ExeterGoogle Scholar
  77. Watterson IG (2008) Calculation of probability density functions for temperature and precipitation change under global warming. J Geophys Res 113:D12106. doi:10.1029/2007JD009254 CrossRefGoogle Scholar
  78. Watterson IG, Whetton PH (2011) Distributions of decadal means of temperature and precipitation change under global warming. J Geophys Res 116:D07101. doi:10.1029/2010JD014502 CrossRefGoogle Scholar
  79. Webb MJ, Senior CA, Sexton DMH, Ingram WI, Williams KD, Ringer MA, McAvaney BJ, Colman R, Soden BJ, Gudgel R, Knutson T, Emori S, Ogura T, Tsushima Y, Andronova N, Li B, Musat I, Bony S, Taylor KE (2006) On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Clim Dyn 27:17–38CrossRefGoogle Scholar
  80. Williams KD, Senior CA, Mitchell JFB (2001) Transient climate change in the Hadley Centre models: the role of physical processes. J Clim 14:2659–2674CrossRefGoogle Scholar

Copyright information

© Crown Copyright 2013

Authors and Affiliations

  • Glen R. Harris
    • 1
  • David M. H. Sexton
    • 1
  • Ben B. B. Booth
    • 1
  • Mat Collins
    • 1
    • 2
  • James M. Murphy
    • 1
  1. 1.Met Office Hadley CentreExeterUK
  2. 2.College of Engineering, Maths and Physical SciencesUniversity of ExeterExeterUK

Personalised recommendations