Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Climate Dynamics Aims and scope Submit manuscript

An Erratum to this article was published on 25 June 2011

Abstract

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.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Notes

  1. The exceptions to this are the multiple A1B and E1 scenario simulations with HadGEM2-AO (Table 1) which all use identical initial conditions taken from the same 20C3M simulation. Differences arise in these cases solely from small numerical differences in model code execution, which amplify via “weather noise” over the initial days of the simulation.

  2. To facilitate other modelling groups who may wish to replicate the ES2 simulations, most of the necessary forcing datasets for running the scenarios, along with some technical documentation, have been made publicly accessible. Datasets comprise GHG, sulfate aerosol and ozone concentrations, and land use maps, and are freely available for download from (or via web links at): http://www.cnrm.meteo.fr/ensembles/public/model_simulation.html.

  3. For those models participating in CMIP5 experiments, it should be possible to compute S in future. Several ES2 models stem from earlier CMIP3 model versions and S for those models (Randall et al. 2007, Table 8.2) also provides some interim guidance.

  4. Note that, as a consequence of the experimental design, the allowable emissions in Fig. 11 echo decadal variability that is apparent in the historical atmospheric CO2 trend. A marked dip in the CO2 trend to near zero in the 1940s is particularly noticeable. It is doubtful, however, that this observed dip can be entirely attributed to anthropogenic emissions reductions during the Second World War. Assuming instead that natural variability in transfers of carbon between the land, ocean and atmosphere partly caused this dip, then labeling the implied carbon emissions in Fig. 11 as purely anthropogenic could be regarded as misleading.

  5. The four models IPSL-CM4, IPSL-CM4-LOOP, BCM2, BCM-C again carry half weight here.

References

  • 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 pp

  • 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

    Article  Google Scholar 

  • Assmann KM, Bentsen M, Segschneider J, Heinze C (2010) An isopycnic ocean carbon cycle model. Geosci Model Dev 3:143–167

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Bellouin N et al (2007) Improved representation of aerosols for HadGEM2. Hadley Centre Technical Note No. 73, Met Office, Exeter, 42 pp

  • 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 95:3273–3285

    Article  Google Scholar 

  • 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 22:1486–1505

    Article  Google Scholar 

  • Bony S, Emanuel KA (2001) A parameterization of the cloudiness associated with cumulus convection: evaluation using TOGA COARE data. J Atmos Sci 58:3158–3183

    Article  Google Scholar 

  • Boucher O, Lohmann U (1995) The sulfate-CCN-cloud albedo effect: a sensitivity study with two general circulation models. Tellus (Ser B) 47:281–300

    Article  Google Scholar 

  • Boucher O, Pham M (2002) History of sulfate aerosol radiative forcings. Geophys Res Lett 29:1308. doi:10.1029/2001GL014048

    Article  Google Scholar 

  • Bougeault P (1985) A simple parameterization of the large-scale effects of cumulus convection. Mon Wea Rev 113:2108–2121

    Article  Google Scholar 

  • 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

  • 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 95:1883–1898

    Article  Google Scholar 

  • Champeaux JL, Masson V, Chauvin R (2005) ECOCLIMAP: a global database of land surface parameters at 1 km resolution. Meteorol Appl 12:29–32

    Article  Google Scholar 

  • 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–S81

    Google Scholar 

  • 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 pp

  • Copenhagen Accord (2009) United Nations framework convention on climate change, CoP 15, http://unfccc.int/resource/docs/2009/cop15/eng/l07.pdf

  • 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 408:184–187

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 http://www.cnrm.meteo.fr/ensembles/public/model_simulation.html

  • 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 104:17931–17936

    Article  Google Scholar 

  • Déqué M (1999) Documentation ARPEGE-CLIMAT. Tech report Centre National de Recherches Météorologiques, Météo-France, Toulouse

  • Déqué M, Dreveton C, Braun A, Cariolle D (1994) The ARPEGE/IFS atmosphere model: a contribution to the French community climate modelling. Clim Dyn 10:249–266

    Article  Google Scholar 

  • Derbyshire SH, Maidens AV, Milton SF, Stratton RA, Willett MR (2010) Adaptive detrainment in a convective parametrization. Q J Roy Meteor Soc (submitted)

  • 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 26:367–385

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 31:11–48

    Google Scholar 

  • Emanuel KA (1991) A scheme for representing cumulus convection in large-scale models. J Atmos Sci 48:2313–2335

    Article  Google Scholar 

  • 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 185–194

  • 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 30:1693–1707

    Article  Google Scholar 

  • Feichter J, Roeckner E, Lohmann U, Liepert B (2004) Nonlinear aspects of the climate response to greenhouse gas and aerosol forcing. J Clim 17:2384–2398

    Article  Google Scholar 

  • Fogli PG et al (2009) INGV-CMCC Carbon: a carbon cycle earth system model, CMCC RP0061 (http://www.cmcc.it/publications-meetings/publications/research-papers/rp0061-ingv-cmcc-carbon-icc-a-carbon-cycle-earth-system-model)

  • 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 19:3337–3353

    Article  Google Scholar 

  • 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 21:27–51

    Article  Google Scholar 

  • Gibelin AL, Déqué M (2003) Anthropogenic climate change over the Mediterranean region simulated by a global variable resolution model. Clim Dyn 20:327–339

    Google Scholar 

  • 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 16:147–168

    Article  Google Scholar 

  • 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 122–123

  • Gregory D, Rowntree PR (1990) A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon Wea Rev 118:1483–1506

    Article  Google Scholar 

  • 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 123:1153–1183

    Article  Google Scholar 

  • Hansen J et al (2005) Efficacy of climate forcings. J Geophys Res 110:D18104. doi:10.1029/2005JD005776

    Article  Google Scholar 

  • Heimann M, Reichstein M (2008) Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451:289–292

    Article  Google Scholar 

  • Hewitt CD, Griggs DJ (2004) Ensembles-based predictions of climate change and their impacts (ENSEMBLES). EOS Trans AGU 85:566

    Article  Google Scholar 

  • Hibbard KA, Meehl GA, Cox PM, Friedlingstein P (2007) A strategy for climate change stabilization experiments. EOS Trans AGU 88:217–221

    Article  Google Scholar 

  • 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 27:787–813

    Article  Google Scholar 

  • 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 365:2089–2101

    Article  Google Scholar 

  • 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 pp

  • 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 19:1327–1353

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 19:3952–3972

    Article  Google Scholar 

  • Kiehl JT, Schneider TL, Portmann RW, Solomon S (1999) Climate forcing due to tropospheric and stratospheric ozone. J Geophys Res Atmos 104:31239–31254

    Article  Google Scholar 

  • Klein Goldewijk K (2001) Estimating global land use change over the past 300 years: the HYDE database. Glob Biogeochem Cycles 15:417–433

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 2:831–836

    Article  Google Scholar 

  • Legutke S, Voss R (1999) The Hamburg atmosphere—ocean coupled climate circulation model ECHO-G. DKRZ Technical report no. 18. Deutsches Klimarechenzentrum, Hamburg

    Google Scholar 

  • 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 90:181–182

    Article  Google Scholar 

  • 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 pp

  • Maier-Reimer E (1993) Geochemical cycles in an ocean general circulation model: Preindustrial tracer distributions. Glob Biogeochem Cycles 7:645–677

    Article  Google Scholar 

  • 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, http://www.mpimet.mpg.de], 50 pp

  • 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’Aquila

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 5:91–127

    Article  Google Scholar 

  • 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 34:1–26

    Article  Google Scholar 

  • 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 19:1274–1301

    Article  Google Scholar 

  • Matthews HD, Caldeira K (2008) Stabilizing climate requires near-zero emissions. Geophys Res Lett 35:L04705. doi:10.1029/2007GL032388

    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 Dyn 31:283–313

    Article  Google Scholar 

  • 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, Cambridge

  • 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 75:151–194

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Mitchell JFB, Wilson CA, Cunnington WM (1987) On CO2 climate sensitivity and model dependence of results. Q J R Meteor Soc 113:293–332

    Article  Google Scholar 

  • 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), Bilthoven

    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 463:747–756. doi:10.1038/nature08823

    Article  Google Scholar 

  • 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 pp

  • 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, Reading

  • Otterå OH (2008) Simulating the effects of the 1991 Mount Pinatubo volcanic eruption using the ARPEGE atmosphere general circulation model. Adv Atmos Sci 25:213–226

    Article  Google Scholar 

  • 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 2:197–212

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 21:2721–2751

    Article  Google Scholar 

  • Pope VD, Gallani ML, Rowntree PR, Stratton RA (2000) The impact of new physical parametrizations in the Hadley Centre climate model: HadAM3. Clim Dyn 16:123–146

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 29:565–574

    Article  Google Scholar 

  • Ramankutty N, Foley JA (1999) Estimating historical changes in land cover: North American croplands from 1850 to 1992. Global Ecol Biogeogr 8:381–396

    Article  Google Scholar 

  • 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, Cambridge

  • 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):887–935

    Article  Google Scholar 

  • 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, Hamburg

  • 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 19:3771–3791

    Article  Google Scholar 

  • 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 

  • 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):1103–1120

    Article  Google Scholar 

  • 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 334:147–154

    Article  Google Scholar 

  • Salas-Mélia D (2002) A global coupled sea ice-ocean model. Ocean Model 4:137–172

    Article  Google Scholar 

  • 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: http://www.cnrm.meteo.fr/scenario2004/paper_cm3.pdf)

  • Sato M, Hansen JE, McCormick MP, Pollack JB (1993) Stratospheric aerosol optical-depths, 1850–1990. J Geophys Res Atmos 98:22987–22994

    Article  Google Scholar 

  • 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 9:161–185

    Article  Google Scholar 

  • 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 22:5175–5204

    Article  Google Scholar 

  • Solanki SK, Krivova NA (2003) Can solar variability explain global warming since 1970? J Geophys Res 108(A5):1200. doi:10.1029/2002JA009753

    Article  Google Scholar 

  • 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 pp

  • 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:D09304

    Article  Google Scholar 

  • 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 19:2763–2782

    Article  Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2009) A Summary of the CMIP5 experiment design. Available from http://cmip-pcmdi.llnl.gov/cmip5/

  • Terray L, Thual O (1995) OASIS : le couplage océan-atmosphère. La Météorol 10:50–61

    Google Scholar 

  • 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 11:487–505

    Article  Google Scholar 

  • Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large scale models. Mon Wea Rev 117:1779–1800

    Article  Google Scholar 

  • 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 8:175–201

    Article  Google Scholar 

  • 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 3:123–141

    Article  Google Scholar 

  • Valcke S (2006) OASIS3 User Guide (prism_2-5), PRISM Report No 2, 6th edn. CERFACS, Tolouse, 64 pp

  • van Vuuren D, Riahi K (2008) Do recent emission trends imply higher emissions forever? Clim Change 91:237–248

    Article  Google Scholar 

  • 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 

  • 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

    Article  Google Scholar 

  • 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 64:110–134

    Article  Google Scholar 

  • 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)

  • Wolff JO, Maier-Reimer E, Legutke S (1997) The Hamburg ocean primitive equation model. DKRZ Technical report no. 13. Deutsches Klimarechenzentrum, Hamburg

    Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Hadley Centre, Met Office, FitzRoy Road, Exeter, EX1 3PB, UK

    T. C. Johns & C. D. Hewitt

  2. Centre National de Recherches Météorologiques-Groupe d’Etude de l’Atmosphère Météorologique (CNRM-GAME Meteo-France CNRS), 42 Avenue G. Coriolis, 31057, Toulouse, France

    J.-F. Royer & D. Salas y Melia

  3. Institute for Meteorology, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6-10, 12165, Berlin, Germany

    I. Höschel & J. Körper

  4. Hessian Agency for the Environment and Geology, Rheingaustraße 186, 65203, Wiesbaden, Germany

    H. Huebener

  5. Max Planck Institute for Meteorology, Bundesstrasse 53, 20146, Hamburg, Germany

    E. Roeckner, E. Manzini & M. A. Giorgetta

  6. Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy

    E. Manzini

  7. Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC), Bologna, Italy

    E. Manzini & P. G. Fogli

  8. Danish Climate Centre, Danish Meteorological Institute, Lyngbyvej 100, 2100, Copenhagen, Denmark

    W. May & S. Yang

  9. UMR 8539 CNRS, ENS, UPMC, Ecole Polytechnique, Laboratoire de Météorologie Dynamique (LMD/IPSL), 75252, Paris Cedex 05, France

    J.-L. Dufresne

  10. Nansen Environmental and Remote Sensing Center, Thormøhlensgt. 47, 5006, Bergen, Norway

    O. H. Otterå

  11. Uni. Bjerknes Centre, Allegt. 55, 5007, Bergen, Norway

    O. H. Otterå

  12. Bjerknes Centre for Climate Research, Allegt. 55, 5007, Bergen, Norway

    O. H. Otterå & J. F. Tjiputra

  13. Utrecht University, Utrecht, The Netherlands

    D. P. van Vuuren

  14. Planbureau voor de Leefomgeving (PBL), Bilthoven, The Netherlands

    D. P. van Vuuren & E. Stehfest

  15. FR 636 CNRS, UVSQ, UPMC, Institut Pierre Simon Laplace (IPSL), 75252, Paris Cedex 05, France

    S. Denvil

  16. Department of Geophysics, University of Bergen, Allegt. 70, 5007, Bergen, Norway

    J. F. Tjiputra

Authors
  1. T. C. Johns
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J.-F. Royer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I. Höschel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. H. Huebener
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E. Roeckner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. E. Manzini
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. W. May
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. J.-L. Dufresne
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. O. H. Otterå
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. D. P. van Vuuren
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. D. Salas y Melia
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. M. A. Giorgetta
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. S. Denvil
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. S. Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. P. G. Fogli
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. J. Körper
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. J. F. Tjiputra
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. E. Stehfest
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. C. D. Hewitt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. C. Johns.

Additional information

An erratum to this article can be found at http://dx.doi.org/10.1007/s00382-011-1102-5

Appendix: Output data availability

Appendix: Output data availability

Data outputs from ES2 simulations, for the historical period (20C3M) and E1 and A1B scenarios for the period 2000–2100 have been archived in CF-compliant NetCDF format in the CERA database in Hamburg (http://cera-www.dkrz.de), from where they can be downloaded for non-commercial scientific research, subject to user registration. Detailed metadata are also stored there describing the simulations and variables archived.

The data include monthly mean values, daily values (daily mean, max and min, or instantaneous, depending on the variable), and also several instantaneous variables at 6- and 12-hourly resolution at the surface and on vertical pressure levels. Data files include various meteorological and oceanographic (surface or near-surface only) variables using Climate and Forecast (CF) compliant names and metadata conventions, including variables related to the interactive CC and aerosol schemes for some models and simulations that include them. For several models, ensemble simulations are included in the database. Additional variables other than those archived at CERA may be available on request direct from the associated ENSEMBLES partners or from other data centres (contact details are given in the metadata).

The full multi-model dataset at CERA includes all the runs listed in Table 1, including those following the A1B-IMAGE scenario, plus some alternatively forced 20C3M runs. The total dataset exceeds 6000 simulated model years in total and is about 11 TB in size. This makes it about one third as large as the WCRP CMIP3 multi-model dataset: http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Johns, T.C., Royer, JF., Höschel, I. et al. Climate change under aggressive mitigation: the ENSEMBLES multi-model experiment. Clim Dyn 37, 1975–2003 (2011). https://doi.org/10.1007/s00382-011-1005-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-011-1005-5

Keywords

Search

Navigation