Climate Dynamics

, Volume 37, Issue 9–10, pp 1975–2003 | Cite as

Climate change under aggressive mitigation: the ENSEMBLES multi-model experiment

  • T. C. JohnsEmail author
  • J.-F. Royer
  • I. Höschel
  • H. Huebener
  • E. Roeckner
  • E. Manzini
  • W. May
  • J.-L. Dufresne
  • O. H. Otterå
  • D. P. van Vuuren
  • D. Salas y Melia
  • M. A. Giorgetta
  • S. Denvil
  • S. Yang
  • P. G. Fogli
  • J. Körper
  • J. F. Tjiputra
  • E. Stehfest
  • C. D. Hewitt


We present results from multiple comprehensive models used to simulate an aggressive mitigation scenario based on detailed results of an Integrated Assessment Model. The experiment employs ten global climate and Earth System models (GCMs and ESMs) and pioneers elements of the long-term experimental design for the forthcoming 5th Intergovernmental Panel on Climate Change assessment. Atmospheric carbon-dioxide concentrations pathways rather than carbon emissions are specified in all models, including five ESMs that contain interactive carbon cycles. Specified forcings also include minor greenhouse gas concentration pathways, ozone concentration, aerosols (via concentrations or precursor emissions) and land use change (in five models). The new aggressive mitigation scenario (E1), constructed using an integrated assessment model (IMAGE 2.4) with reduced fossil fuel use for energy production aimed at stabilizing global warming below 2 K, is studied alongside the medium-high non-mitigation scenario SRES A1B. Resulting twenty-first century global mean warming and precipitation changes for A1B are broadly consistent with previous studies. In E1 twenty-first century global warming remains below 2 K in most models, but global mean precipitation changes are higher than in A1B up to 2065 and consistently higher per degree of warming. The spread in global temperature and precipitation responses is partly attributable to inter-model variations in aerosol loading and representations of aerosol-related radiative forcing effects. Our study illustrates that the benefits of mitigation will not be realised in temperature terms until several decades after emissions reductions begin, and may vary considerably between regions. A subset of the models containing integrated carbon cycles agree that land and ocean sinks remove roughly half of present day anthropogenic carbon emissions from the atmosphere, and that anthropogenic carbon emissions must decrease by at least 50% by 2050 relative to 1990, with further large reductions needed beyond that to achieve the E1 concentrations pathway. Negative allowable anthropogenic carbon emissions at and beyond 2100 cannot be ruled out for the E1 scenario. There is self-consistency between the multi-model ensemble of allowable anthropogenic carbon emissions and the E1 scenario emissions from IMAGE 2.4.


Climate Climate change Carbon cycle Projections Mitigation Stabilization Allowable emissions Emissions reduction Earth system model Multi-model ENSEMBLES CMIP5 



We gratefully acknowledge the ENSEMBLES project, funded by the European Commission’s 6th Framework Programme (FP6) through contract GOCE-CT-2003-505539. We also thank Nathalie de Noblet, Björg Rognerud and Olivier Boucher for their help in constructing the essential scenario forcing datasets, and Jason Lowe and two anonymous reviewers for comments which helped to improve this paper. TCJ and CDH were supported by the Joint DECC and Defra Integrated Climate Programme, DECC/Defra (GA01101). The development of BCM-C was supported by the CARBOOCEAN integrated project under FP6 (grant number 511176) and the Research Council of Norway through the NorClim project. Finally we express our deep gratitude to numerous hard-working members of our respective modeling teams.


  1. Alessandri A (2006) Effects of land surface and vegetation processes on the climate simulated by an atmospheric general circulation model. Ph.D Thesis in Geophysics, Bologna University Alma Mater Studiorum, 114 ppGoogle Scholar
  2. Andrews T, Forster PM, Boucher O, Bellouin N, Jones A (2010) Precipitation, radiative forcing and global temperature change. Geophys Res Lett 37:L14701. doi: 10.1029/2010GL043991 CrossRefGoogle Scholar
  3. Assmann KM, Bentsen M, Segschneider J, Heinze C (2010) An isopycnic ocean carbon cycle model. Geosci Model Dev 3:143–167CrossRefGoogle Scholar
  4. Aumont O, Maier-Reimer E, Blain S, Monfray P (2003) An ecosystem model of the global ocean including Fe, Si, P colimitations. Glob Biogeochem Cycles 17:1060. doi: 10.1029/2001GB001745 CrossRefGoogle Scholar
  5. Bellouin N et al (2007) Improved representation of aerosols for HadGEM2. Hadley Centre Technical Note No. 73, Met Office, Exeter, 42 ppGoogle Scholar
  6. Bleck R, Smith LT (1990) A wind-driven isopycnic coordinate model of the North and equatorial Atlantic Ocean. 1: model development and supporting experiments. J Geophys Res Oceans 95:3273–3285CrossRefGoogle Scholar
  7. Bleck R, Rooth C, Hu D, Smith LT (1992) Salinity-driven thermocline transients in a wind- and thermohaline-forced isopycnic coordinate model of the North Atlantic. J Phys Oceanogr 22:1486–1505CrossRefGoogle Scholar
  8. Bony S, Emanuel KA (2001) A parameterization of the cloudiness associated with cumulus convection: evaluation using TOGA COARE data. J Atmos Sci 58:3158–3183CrossRefGoogle Scholar
  9. Boucher O, Lohmann U (1995) The sulfate-CCN-cloud albedo effect: a sensitivity study with two general circulation models. Tellus (Ser B) 47:281–300CrossRefGoogle Scholar
  10. Boucher O, Pham M (2002) History of sulfate aerosol radiative forcings. Geophys Res Lett 29:1308. doi: 10.1029/2001GL014048 CrossRefGoogle Scholar
  11. Bougeault P (1985) A simple parameterization of the large-scale effects of cumulus convection. Mon Wea Rev 113:2108–2121CrossRefGoogle Scholar
  12. Cadule P, Bopp L, Friedlingstein P (2009) A revised estimate of the processes contributing to global warming due to climate-carbon feedback. Geophys Res Lett 36:L14705. doi: 10.1029/2009GLO38681
  13. Cariolle D, Lasserre-Bigorry A, Royer JF, Geleyn JF (1990) A general circulation model simulation of the springtime Antarctic ozone decrease and its impact on mid-latitudes. J Geophys Res Atmos 95:1883–1898CrossRefGoogle Scholar
  14. Champeaux JL, Masson V, Chauvin R (2005) ECOCLIMAP: a global database of land surface parameters at 1 km resolution. Meteorol Appl 12:29–32CrossRefGoogle Scholar
  15. Clarke L, Edmonds J, Krey V, Richels R, Rose S, Tavoni M (2010) International climate policy architectures: overview of the EMF 22 international scenarios. Energy Econ 31(suppl 2):S64–S81Google Scholar
  16. Collins WJ, Bellouin N, Doutriaux-Boucher M, Gedney N, Hinton T, Jones CD, Liddicoat S, Martin G, O’Connor F, Rae J, Senior C, Totterdell I, Woodward S, Reichler T, Kim J (2008) Evaluation of the HadGEM2 model. Met Office Hadley Centre Technical Note No. 74, Met Office, Exeter, 47 ppGoogle Scholar
  17. Copenhagen Accord (2009) United Nations framework convention on climate change, CoP 15,
  18. Cox PM, Betts RA, Jones CD, Spall SA, Totterdell IJ (2000) Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408:184–187CrossRefGoogle Scholar
  19. Crowley TJ, Baum SK, Kim KY, Hegerl GC, Hyde WT (2003) Modeling ocean heat content changes during the last millennium. Geophys Res Lett 30(18):1932. doi: 10.1029/2003GL017801 CrossRefGoogle Scholar
  20. de Noblet-Ducoudré N, Peterschmitt J-Y (2007) Designing historical and future land-cover maps at the global scale for climate studies. Technical Report available from
  21. Den Elzen MGJ, van Vuuren DP (2007) Peaking profiles for achieving long-term temperature targets with more likelihood at lower costs. Proc Natl Acad Sci USA 104:17931–17936CrossRefGoogle Scholar
  22. Déqué M (1999) Documentation ARPEGE-CLIMAT. Tech report Centre National de Recherches Météorologiques, Météo-France, ToulouseGoogle Scholar
  23. Déqué M, Dreveton C, Braun A, Cariolle D (1994) The ARPEGE/IFS atmosphere model: a contribution to the French community climate modelling. Clim Dyn 10:249–266CrossRefGoogle Scholar
  24. Derbyshire SH, Maidens AV, Milton SF, Stratton RA, Willett MR (2010) Adaptive detrainment in a convective parametrization. Q J Roy Meteor Soc (submitted)Google Scholar
  25. Douville H, Salas-Mélia D, Tyteca S (2006) On the tropical origin of uncertainties in the global land precipitation response to global warming. Clim Dyn 26:367–385CrossRefGoogle Scholar
  26. Dufresne JL, Quaas J, Boucher O, Denvil S, Fairhead L (2005) Contrasts in the effects on climate of anthropogenic sulfate aerosols between the 20th and the 21st century. Geophys Res Lett 32:L21703. doi: 10.1029/2005GL023619 CrossRefGoogle Scholar
  27. Edenhofer O, Knopf B, Barker T, Baumstark L, Bellevrat E, Chateau B, Criqui P, Isaac M, Kitous A, Kypreos S, Leimbach M, Lessmann K, Magné B, Scrieciu S, Turton H, van Vuuren DP (2010) The economics of low stabilization: model comparison of mitigation strategies and costs. Energy J 31:11–48Google Scholar
  28. Emanuel KA (1991) A scheme for representing cumulus convection in large-scale models. J Atmos Sci 48:2313–2335CrossRefGoogle Scholar
  29. Emanuel KA (1993) A cumulus representation based on the episodic mixing model: the importance of mixing and microphysics in predicting humidity. The Representation of Cumulus Convection in Numerical Models. Meteor Monogr No 46, Am Meteor Soc, pp 185–194Google Scholar
  30. Feichter J, Kjellström E, Rodhe H, Dentener F, Lelieveld J, Roelofs GJ (1996) Simulation of the tropospheric sulfur cycle in a global climate model. Atmos Environ 30:1693–1707CrossRefGoogle Scholar
  31. Feichter J, Roeckner E, Lohmann U, Liepert B (2004) Nonlinear aspects of the climate response to greenhouse gas and aerosol forcing. J Clim 17:2384–2398CrossRefGoogle Scholar
  32. Fogli PG et al (2009) INGV-CMCC Carbon: a carbon cycle earth system model, CMCC RP0061 (
  33. 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 KG, 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
  34. Furevik T, Bentsen M, Drange H, Kindem IKT, Kvamsto NG, Sorteberg A (2003) Description and evaluation of the Bergen climate model: ARPEGE coupled with MICOM. Clim Dyn 21:27–51CrossRefGoogle Scholar
  35. Gibelin AL, Déqué M (2003) Anthropogenic climate change over the Mediterranean region simulated by a global variable resolution model. Clim Dyn 20:327–339Google Scholar
  36. 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
  37. Gregory D, Allen S (1991) The effect of convective scale downdrafts upon NWP and climate simulations. In: Preprints, 9th conference on numerical weather prediction, Denver. Amer Meteor Soc, pp 122–123Google Scholar
  38. Gregory D, Rowntree PR (1990) A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon Wea Rev 118:1483–1506CrossRefGoogle Scholar
  39. Gregory D, Kershaw R, Inness PM (1997) Parametrization of momentum transport by convection. II: tests in single-column and general circulation models. Q J R Meteor Soc 123:1153–1183CrossRefGoogle Scholar
  40. Hansen J et al (2005) Efficacy of climate forcings. J Geophys Res 110:D18104. doi: 10.1029/2005JD005776 CrossRefGoogle Scholar
  41. Heimann M, Reichstein M (2008) Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451:289–292CrossRefGoogle Scholar
  42. Hewitt CD, Griggs DJ (2004) Ensembles-based predictions of climate change and their impacts (ENSEMBLES). EOS Trans AGU 85:566CrossRefGoogle Scholar
  43. Hibbard KA, Meehl GA, Cox PM, Friedlingstein P (2007) A strategy for climate change stabilization experiments. EOS Trans AGU 88:217–221CrossRefGoogle Scholar
  44. Hourdin F, Musat I, Bony S, Braconnot P, Codron F, Dufresne JL, Fairhead L, Filiberti MA, Friedlingstein P, Grandpeix JY, Krinner G, Levan P, Li ZX, Lott F (2006) The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection. Clim Dyn 27:787–813CrossRefGoogle Scholar
  45. Huebener H, Cubasch U, Langematz U, Spangehl T, Niehorster F, Fast I, Kunze M (2007) Ensemble climate simulations using a fully coupled ocean-troposphere-stratosphere general circulation model. Phil Trans R Soc Lond A 365:2089–2101CrossRefGoogle Scholar
  46. IPCC (2001) Climate change 2001: the scientific basis. In: Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (eds) Contribution of working group I to the 3rd assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, 881 ppGoogle Scholar
  47. Johns TC, Durman CF, Banks HT, Roberts MJ, McLaren AJ, Ridley JK, Senior CA, Williams KD, Jones A, Rickard GJ, Cusack S, Ingram WJ, Crucifix M, Sexton DMH, Joshi MM, Dong BW, Spencer H, Hill RSR, Gregory JM, Keen AB, Pardaens AK, Lowe JA, Bodas-Salcedo A, Stark S, Searl Y (2006) The new Hadley Centre Climate Model (HadGEM1): evaluation of coupled simulations. J Clim 19:1327–1353CrossRefGoogle Scholar
  48. 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 Atmos 106:20293–20310. doi: 10.1029/2000JD000089 CrossRefGoogle Scholar
  49. Jungclaus JH, Keenlyside N, Botzet M, Haak H, Luo JJ, Latif M, Marotzke J, Mikolajewicz U, Roeckner E (2006) Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM. J Clim 19:3952–3972CrossRefGoogle Scholar
  50. Kiehl JT, Schneider TL, Portmann RW, Solomon S (1999) Climate forcing due to tropospheric and stratospheric ozone. J Geophys Res Atmos 104:31239–31254CrossRefGoogle Scholar
  51. Klein Goldewijk K (2001) Estimating global land use change over the past 300 years: the HYDE database. Glob Biogeochem Cycles 15:417–433CrossRefGoogle Scholar
  52. Krinner G, Viovy N, de Noblet-Ducoudré N, Ogée J, Polcher J, Friedlingstein P, Ciais P, Sitch S, Prentice IC (2005) A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob Biogeochem Cycles 19:GB1015. doi: 10.1029/2003GB002199 CrossRefGoogle Scholar
  53. Le Quéré C, Raupach MR, Canadell JG, Marland G, Bopp L, Ciais P, Conway TJ, Doney SC, Feely R, Foster P, Friedlingstein P, Gurney K, Houghton RA, House JI, Huntingford C, Levy PE, Lomas MR, Majkut J, Metzl N, Ometto JP, Peters GP, Prentice IC, Randerson JT, Running SW, Sarmiento JL, Schuster U, Sitch S, Takahashi T, Viovy N, van der Werf GR, Woodward FI (2009) Trends in the sources and sinks of carbon dioxide. Nat Geosci 2:831–836CrossRefGoogle Scholar
  54. Legutke S, Voss R (1999) The Hamburg atmosphere—ocean coupled climate circulation model ECHO-G. DKRZ Technical report no. 18. Deutsches Klimarechenzentrum, HamburgGoogle Scholar
  55. Lowe JA, Hewitt CD, van Vuuren DP, Johns TC, Stehfest E, Royer JF, van der Linden PJ (2009) New study for climate modeling, analyses, and scenarios. EOS Trans AGU 90:181–182CrossRefGoogle Scholar
  56. Madec G, Delecluse P, Imbard I, Levy C (1999) OPA 8.1 ocean general circulation model reference manual. Note du Pôle de modélisation No. 11, Inst. Pierre-Simon Laplace (IPSL), France, 91 ppGoogle Scholar
  57. Maier-Reimer E (1993) Geochemical cycles in an ocean general circulation model: Preindustrial tracer distributions. Glob Biogeochem Cycles 7:645–677CrossRefGoogle Scholar
  58. Maier-Reimer E, Kriest I, Segschneider J, Wetzel P (2005) The Hamburg ocean carbon cycle model HAMOCC5.1—Technical description release 1.1. Reports on Earth System Science 14, ISSN 1614-1199 [available from Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany,], 50 pp
  59. Major Economies Forum (2009) Declaration of the leaders of the Major Economies Forum on Energy and Climate. Major Economies Forum on Energy and Climate, L’AquilaGoogle Scholar
  60. Manzini E, McFarlane NA (1998) The effect of varying the source spectrum of a gravity wave parameterization in a middle atmosphere general circulation model. J Geophys Res Atmos 103:31523–31539. doi: 10.1029/98JD02274 CrossRefGoogle Scholar
  61. Marsland SJ, Haak H, Jungclaus JH, Latif M, Röske F (2003) The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates. Ocean Model 5:91–127CrossRefGoogle Scholar
  62. Marti O, Braconnot P, Dufresne J-L, Bellier J, Benshila R, Bony S, Brockmann P, Cadule P, Caubel A, Codron F, de Noblet N, Denvil S, Fairhead L, Fichefet T, Foujols M-A, Friedlingstein P, Goosse H, Grandpeix J-Y, Guilyardi E, Hourdin F, Idelkadi A, Kageyama M, Krinner G, Lévy C, Madec G, Mignot J, Musat I, Swingedouw D, Talandier C (2010) Key features of the IPSL ocean atmosphere model and its sensitivity to atmospheric resolution. Clim Dyn 34:1–26CrossRefGoogle Scholar
  63. Martin GM, Ringer MA, Pope VD, Jones A, Dearden C, Hinton TJ (2006) The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model (HadGEM1). Part I: model description and global climatology. J Clim 19:1274–1301CrossRefGoogle Scholar
  64. Matthews HD, Caldeira K (2008) Stabilizing climate requires near-zero emissions. Geophys Res Lett 35:L04705. doi: 10.1029/2007GL032388 CrossRefGoogle Scholar
  65. May W (2008) Climatic changes associated with a global “2°C-stabilization” scenario simulated by the ECHAM5/MPI-OM coupled climate model. Clim Dyn 31:283–313CrossRefGoogle Scholar
  66. Meehl GA, Stocker TF et al (2007) 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 1 to the 4th scientific assessment report of the IPCC. Cambridge University Press, CambridgeGoogle Scholar
  67. Meinshausen M, Hare B, Wigley TML, van Vuuren D, Den Elzen MGJ, Swart R (2006) Multi-gas emissions pathways to meet climate targets. Clim Change 75:151–194CrossRefGoogle Scholar
  68. Ming Y, Ramaswamy V, Persad G (2010) Two opposing effects of absorbing aerosols on global-mean precipitation. Geophys Res Lett 37:L13701. doi: 10.1029/2010GL042895 CrossRefGoogle Scholar
  69. Mitchell JFB, Wilson CA, Cunnington WM (1987) On CO2 climate sensitivity and model dependence of results. Q J R Meteor Soc 113:293–332CrossRefGoogle Scholar
  70. MNP (2006) Integrated modelling of global environmental change. In: Bouwman AF, Kram T, Klein Goldewijk K (eds) An overview of IMAGE 2.4. Netherlands Environmental Assessment Agency (MNP), BilthovenGoogle Scholar
  71. 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 463:747–756. doi: 10.1038/nature08823 CrossRefGoogle Scholar
  72. Nakicenovic N, Swart R (eds) (2000) Special report on emissions scenarios—a special report of working group III of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, 599 ppGoogle Scholar
  73. Nordeng TE (1994) Extended versions of the convective parametrization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics. ECMWF Rep 206, ECMWF, ReadingGoogle Scholar
  74. Otterå OH (2008) Simulating the effects of the 1991 Mount Pinatubo volcanic eruption using the ARPEGE atmosphere general circulation model. Adv Atmos Sci 25:213–226CrossRefGoogle Scholar
  75. Otterå OH, Bentsen M, Bethke I, Kvamstø NG (2009) Simulated pre-industrial climate in Bergen climate model: model description and large-scale circulation features. Geosci Model Dev 2:197–212CrossRefGoogle Scholar
  76. Otterå OH, Bentsen M, Drange H, Suo L (2010) External forcing as a metronome for Atlantic multidecadal variability. Nature Geosci 3:688–694. doi: 10.1038/NGEO955 CrossRefGoogle Scholar
  77. Pitman AJ et al (2009) Uncertainties in climate responses to past land cover change: first results from the LUCID intercomparison study. Geophys Res Lett 36:L14814. doi: 10.1029/2009GL039076 CrossRefGoogle Scholar
  78. Plattner GK, Knutti R, Joos F, Stocker TF, von Bloh W, Brovkin V, Cameron D, Driesschaert E, Dutkiewicz S, Eby M, Edwards NR, Fichefet T, Hargreaves JC, Jones CD, Loutre MF, Matthews HD, Mouchet A, Müller SA, Nawrath S, Price A, Sokolov A, Strassmann KM, Weaver AJ (2008) Long-term climate commitments projected with climate-carbon cycle models. J Clim 21:2721–2751CrossRefGoogle Scholar
  79. Pope VD, Gallani ML, Rowntree PR, Stratton RA (2000) The impact of new physical parametrizations in the Hadley Centre climate model: HadAM3. Clim Dyn 16:123–146CrossRefGoogle Scholar
  80. Quaas J, Boucher O (2005) Constraining the first aerosol indirect radiative forcing in the LMDZ GCM using POLDER and MODIS satellite data. Geophys Res Lett 32:L17814. doi: 10.1029/2005GL023850 CrossRefGoogle Scholar
  81. Raddatz TJ, Reick CH, Knorr W, Kattge J, Roeckner E, Schnur R, Schnitzler KG, Wetzel P, Jungclaus J (2007) Will the tropical land biosphere dominate the climate-carbon cycle feedback during the twenty-first century? Clim Dyn 29:565–574CrossRefGoogle Scholar
  82. Ramankutty N, Foley JA (1999) Estimating historical changes in land cover: North American croplands from 1850 to 1992. Global Ecol Biogeogr 8:381–396CrossRefGoogle Scholar
  83. Randall DA., Wood RA et al (2007) Climate 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 4th assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  84. Riahi K, Gruebler A, Nakicenovic N (2007) Scenarios of long-term socio-economic and environmental development under climate stabilization. Technol Forecast Soc Change 74(7):887–935CrossRefGoogle Scholar
  85. Roeckner E, Arpe K, Bengtsson L, Christoph M, Claussen M, Dümenil L, Esch M, Giorgetta M, Schlese U, Schulzweida U (1996) The atmospheric general circulation model ECHAM4: Model description and simulation of present-day climate. Max Planck Institut für Meteorologie, Report No. 218, HamburgGoogle Scholar
  86. Roeckner E, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kornblueh L, Manzini E, Schlese U, Schulzweida U (2006) Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model. J Clim 19:3771–3791CrossRefGoogle Scholar
  87. Roeckner E, Giorgetta MA, Crueger T, Esch M, Pongratz J (2010) Historical and future anthropogenic emission pathways derived from coupled climate—carbon cycle simulations. Clim Change. doi: 10.1007/s10584-010-9886-6 Google Scholar
  88. Rongming H, Planton S, Déque M, Marquet P, Braun A (2001) Why is the climate forcing of sulfate aerosols so uncertain? Adv Atm Sc 18(6):1103–1120CrossRefGoogle Scholar
  89. Royer JF, Cariolle D, Chauvin F, Déqué M, Douville H, Hu RM, Planton S, Rascol A, Ricard JL, Salas y Mélia D, Sevault F, Simon P, Somot S, Tyteca S, Terray L, Valcke S (2002) Simulation des changements climatiques au cours du 21-ème siècle incluant l’ozone stratosphérique (Simulation of climate changes during the 21-st century including stratospheric ozone). C R Geosci 334:147–154CrossRefGoogle Scholar
  90. Salas-Mélia D (2002) A global coupled sea ice-ocean model. Ocean Model 4:137–172CrossRefGoogle Scholar
  91. Salas-Mélia D, Chauvin F, Déqué M, Douville H, Guérémy JF, Marquet P, Planton S, Royer J-F, Tyteca S (2005) Description and validation of CNRM-CM3 global coupled climate model. Note de Centre du GMGEC N°103, Décembre 2005 (available from:
  92. Sato M, Hansen JE, McCormick MP, Pollack JB (1993) Stratospheric aerosol optical-depths, 1850–1990. J Geophys Res Atmos 98:22987–22994CrossRefGoogle Scholar
  93. Sitch S, Smith B, Prentice IC, Arneth A, Bondeau A, Cramer W, Kaplan JO, Levis S, Lucht W, Sykes MT, Thonicke K, Venevsky S (2003) Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob Change Biol 9:161–185CrossRefGoogle Scholar
  94. Sokolov AP, Stone PH, Forest CE, Prinn R, Sarofim MC, Webster M, Paltsev S, Schlosser CA, Kicklighter D, Dutkiewicz S, Reilly J, Wang C, Felzer B, Mellilo JM, Jacoby HD (2009) Probabilistic forecast for 21st century climate based on uncertainties in emissions (without policy) and climate parameters. J Clim 22:5175–5204CrossRefGoogle Scholar
  95. Solanki SK, Krivova NA (2003) Can solar variability explain global warming since 1970? J Geophys Res 108(A5):1200. doi: 10.1029/2002JA009753 CrossRefGoogle Scholar
  96. Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) (2007) In: Climate Change 2007: the physical basis. Contribution of working group I to the 4th assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, 996 ppGoogle Scholar
  97. Sovde OA, Gauss M, Smyshlyaev SP, Isaksen ISA (2008) Evaluation of the chemical transport model Oslo CTM2 with focus on Arctic winter ozone depletion. J Geophys Res Atmos 113:D09304CrossRefGoogle Scholar
  98. Stott PA, Jones GS, Lowe JA, Thorne PW, Durman CF, Johns TC, Thelen J-C (2006) Transient simulations with the HadGEM1 climate model: causes of past warming and future climate change. J Clim 19:2763–2782CrossRefGoogle Scholar
  99. Taylor KE, Stouffer RJ, Meehl GA (2009) A Summary of the CMIP5 experiment design. Available from
  100. Terray L, Thual O (1995) OASIS : le couplage océan-atmosphère. La Météorol 10:50–61Google Scholar
  101. Terray L, Thual O, Belamari S, Déqué M, Dandin P, Delecluse P, Lévy C (1995) Climatology and interannual variability simulated by the ARPEGE-OPA coupled model. Clim Dyn 11:487–505CrossRefGoogle Scholar
  102. Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large scale models. Mon Wea Rev 117:1779–1800CrossRefGoogle Scholar
  103. Timmermann R, Goosse H, Madec G, Fichefet T, Ethe C, Dulière V (2005) On the representation of high latitude processes in the ORCA-LIM global coupled sea ice-ocean model. Ocean Model 8:175–201CrossRefGoogle Scholar
  104. Tjiputra JF, Assmann K, Bentsen M, Bethke I, Otterå OH, Sturm C, Heinze C (2010) Bergen Earth system model (BCM-C): model description and regional climate-carbon cycle feedbacks assessment. Geosci Model Dev 3:123–141CrossRefGoogle Scholar
  105. Valcke S (2006) OASIS3 User Guide (prism_2-5), PRISM Report No 2, 6th edn. CERFACS, Tolouse, 64 ppGoogle Scholar
  106. van Vuuren D, Riahi K (2008) Do recent emission trends imply higher emissions forever? Clim Change 91:237–248CrossRefGoogle Scholar
  107. van Vuuren DP, den Elzen MGJ, Lucas PL, Eickhout B, Strengers BJ, van Ruijven B, Wonink S, van Houdt R (2007) Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs. Clim Change 81:119–159. doi: 10.1007/s/10584-006-9172-9 Google Scholar
  108. van Vuuren DP, Meinshausen M, Plattner G-K, Joos F, Strassmann KM, Smith SJ, Wigley TML, Raper SCB, Riahi K, de la Chesnaye F, den Elzen MGJ, Fujino J, Jiang K, Nakicenovic N, Paltsev S, Reilly JM (2008) Temperature increase of 21st century mitigation scenarios. Proc Natl Acad Sci 105(40):15258–15262. doi: 10.1073/pnas.0711129105 CrossRefGoogle Scholar
  109. Vichi M, Masina S, Navarra A (2007) A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part II: numerical simulations. J Mar Syst 64:110–134CrossRefGoogle Scholar
  110. Vichi M, Manzini E, Fogli PG Alessandri A, Patara L, Scoccimarro E, Masina S, Navarra A (2011) Global and regional ocean carbon uptake and climate change: Sensitivity to an aggressive mitigation scenario. Clim Dyn (in revision)Google Scholar
  111. Wolff JO, Maier-Reimer E, Legutke S (1997) The Hamburg ocean primitive equation model. DKRZ Technical report no. 13. Deutsches Klimarechenzentrum, HamburgGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • T. C. Johns
    • 1
    Email author
  • J.-F. Royer
    • 2
  • I. Höschel
    • 3
  • H. Huebener
    • 4
  • E. Roeckner
    • 5
  • E. Manzini
    • 5
    • 6
    • 7
  • W. May
    • 8
  • J.-L. Dufresne
    • 9
  • O. H. Otterå
    • 10
    • 12
    • 11
  • D. P. van Vuuren
    • 14
    • 13
  • D. Salas y Melia
    • 2
  • M. A. Giorgetta
    • 5
  • S. Denvil
    • 15
  • S. Yang
    • 8
  • P. G. Fogli
    • 7
  • J. Körper
    • 3
  • J. F. Tjiputra
    • 12
    • 16
  • E. Stehfest
    • 14
  • C. D. Hewitt
    • 1
  1. 1.Hadley CentreMet OfficeExeterUK
  2. 2.Centre National de Recherches Météorologiques-Groupe d’Etude de l’Atmosphère Météorologique (CNRM-GAME Meteo-France CNRS)ToulouseFrance
  3. 3.Institute for MeteorologyFreie Universität BerlinBerlinGermany
  4. 4.Hessian Agency for the Environment and GeologyWiesbadenGermany
  5. 5.Max Planck Institute for MeteorologyHamburgGermany
  6. 6.Istituto Nazionale di Geofisica e VulcanologiaBolognaItaly
  7. 7.Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC)BolognaItaly
  8. 8.Danish Climate CentreDanish Meteorological InstituteCopenhagenDenmark
  9. 9.UMR 8539 CNRS, ENS, UPMC, Ecole PolytechniqueLaboratoire de Météorologie Dynamique (LMD/IPSL)Paris Cedex 05France
  10. 10.Nansen Environmental and Remote Sensing CenterBergenNorway
  11. 11.Uni. Bjerknes CentreBergenNorway
  12. 12.Bjerknes Centre for Climate ResearchBergenNorway
  13. 13.Utrecht UniversityUtrechtThe Netherlands
  14. 14.Planbureau voor de Leefomgeving (PBL)BilthovenThe Netherlands
  15. 15.FR 636 CNRS, UVSQ, UPMCInstitut Pierre Simon Laplace (IPSL)Paris Cedex 05France
  16. 16.Department of GeophysicsUniversity of BergenBergenNorway

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