Climate Dynamics

, Volume 35, Issue 5, pp 785–806 | Cite as

A probabilistic calibration of climate sensitivity and terrestrial carbon change in GENIE-1

  • Philip B. HoldenEmail author
  • N. R. Edwards
  • K. I. C. Oliver
  • T. M. Lenton
  • R. D. Wilkinson


In order to investigate Last Glacial Maximum and future climate, we “precalibrate” the intermediate complexity model GENIE-1 by applying a rejection sampling approach to deterministic emulations of the model. We develop ~1,000 parameter sets which reproduce the main features of modern climate, but not precise observations. This allows a wide range of large-scale feedback response strengths which generally encompass the range of GCM behaviour. We build a deterministic emulator of climate sensitivity and quantify the contributions of atmospheric (±0.93°C, 1σ) vegetation (±0.32°C), ocean (±0.24°C) and sea–ice (±0.14°C) parameterisations to the total uncertainty. We then perform an LGM-constrained Bayesian calibration, incorporating data-driven priors and formally accounting for structural error. We estimate climate sensitivity as likely (66% confidence) to lie in the range 2.6–4.4°C, with a peak probability at 3.6°C. We estimate LGM cooling likely to lie in the range 5.3–7.5°C, with a peak probability at 6.2°C. In addition to estimates of global temperature change, we apply our ensembles to derive LGM and 2xCO2 probability distributions for land carbon storage, Atlantic overturning and sea–ice coverage. Notably, under 2xCO2 we calculate a probability of 37% that equilibrium terrestrial carbon storage is reduced from modern values, so the land sink has become a net source of atmospheric CO2.


Climate sensitivity Last glacial maximum Precalibration Structural error Emulation GENIE-1 



This work was funded by the U.K. Natural Environment Research Council (QUEST-DESIRE, Quaternary QUEST and RAPID UK THC MIP), the U.K. Engineering and Physical Sciences Research Council (Managing Uncertainty in Complex Models project, MUCM) and the Leverhulme Trust. We are grateful for the thorough reviews of both referees which have greatly helped to strengthen the paper and to Jonathan Rougier for several very useful discussions.


  1. Annan JD, Hargreaves JC (2006) Using multiple observationally-based constraints to estimate climate sensitivity. Geophys Res Lett 33:L06704. doi: 10.1029/2005GL025259 CrossRefGoogle Scholar
  2. Annan JD, Hargreaves JC, Ohgaito R, Abe-Ouchi A, Emori S (2005) Efficiently constraining climate sensitivity with ensembles of paleoclimate simulations. SOLA 1:181–184. doi: 10.2151/sola.2005-047 CrossRefGoogle Scholar
  3. Arrhenius S (1896) On the influence of carbonic acid in the air upon the temperature of the ground. Philos Mag 41:237–276Google Scholar
  4. Ballantyne AP, Lavine M, Crowley TJ, Liu J, Baker PB (2005) Meta-analysis of tropical surface temperatures during the last Glacial maximum. Geophys Res Lett 32:L05712. doi: 10.1029/2004GL021217 CrossRefGoogle Scholar
  5. Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in population genetics. Genetics 162:2025–2035Google Scholar
  6. Berger A (1978) Long term variations of caloric insolation resulting from the Earth’s orbital elements. Quat Res 9:139–167. doi: 10.1016/0033-5894(78)90064-9 CrossRefGoogle Scholar
  7. Claquin T et al (2003) Radiative forcing of climate by ice-age atmospheric dust. Clim Dyn 20:193–202. doi: 10.1007/s00382-002-0269-1 Google Scholar
  8. Colman R, McAvaney B (2009) Climate feedbacks under a broad range of forcing. Geophys Res Lett 36:L01702. doi: 10.1029/2008GL036268 CrossRefGoogle Scholar
  9. Crucifix M (2006) Does the last glacial maximum constrain climate sensitivity? Geophys Res Lett 33:L18701. doi: 10.1029/2006GL027137 CrossRefGoogle Scholar
  10. Edwards NR, Marsh R (2005) Uncertainties due to transport-parameter sensitivity in an efficient 3-D ocean-climate model. Clim Dyn 24:415–433. doi: 10.1007/s00382-004-0508-8 CrossRefGoogle Scholar
  11. Ferreira D, Marshall J, Heimbach P (2005) Estimating eddy stresses by fitting dynamics to observations using a residual-mean ocean circulation model and its adjoint. J Phys Oceanogr 35:1891–1910. doi: 10.1175/JPO2785.1 CrossRefGoogle Scholar
  12. Friedlingstein P et al (2006) Climate-carbon cycle feedback analysis: results from the C4MIP model intercomparison. J Clim 19:3337–3353. doi: 10.1175/JCLI3800.1 CrossRefGoogle Scholar
  13. Hargreaves JC, Abe-Ouchi A, Annan JD (2007) Linking glacial and future climates through and ensemble of GCM simulations. Clim Past 3:77–87CrossRefGoogle Scholar
  14. IPCC (2007) Climate change 2007: the physical science basis. Cambridge University Press, CambridgeGoogle Scholar
  15. Knutti R, Stocker TF, Joos F, Plattner G-K (2002) Constraints on radiative forcing and future climate change from observations and climate model ensembles. Nature 416:719–723CrossRefGoogle Scholar
  16. Krinner G, Genthon C (1998) GCM simulations of the last glacial maximum surface climate of Greenland and Antarctica. Clim Dyn 14:741–758. doi: 10.1007/s003820050252 CrossRefGoogle Scholar
  17. Lea DW (2004) The100,000-year cycle in tropical SST, greenhouse forcing, and climate sensitivity. J Clim 17:2170–2179. doi: 10.1175/1520-0442(2004)017<2170:TYCITS>2.0.CO;2 CrossRefGoogle Scholar
  18. Lenton TM, Huntingford C (2003) Global terrestrial carbon storage and uncertainties in its temperature sensitivity examined with a simple model. Glob Change Biol 9:1333–1352. doi: 10.1046/j.1365-2486.2003.00674.x CrossRefGoogle Scholar
  19. Lenton TM, Williamson MS, Edwards NR, Marsh R, Price AR, Ridgwell AJ, Shepherd JG, Cox SJ, The GENIE team (2006) Millennial timescale carbon cycle and climate change in an efficient Earth system model. Clim Dyn 26:687–711. doi: 10.1007/s00382-006-0109-9 CrossRefGoogle Scholar
  20. Lunt DJ, Williamson MS, Valdes PJ, Lenton TM, Marsh R (2006) Comparing transient, accelerated, and equilibrium simulations of the last 30,000 years with the GENIE-1 model. Clim Past 2:221–235CrossRefGoogle Scholar
  21. Marsh R, Yool A, Lenton TM, Gulamali MY, Edwards NR, Shepherd JG, Krznaric M, Newhouse S, Cox SJ (2004) Bistability of the thermohaline circulation identified through comprehensive 2-parameter sweeps of an efficient climate model. Clim Dyn 23:761–777. doi: 10.1007/s00382-004-0474-1 CrossRefGoogle Scholar
  22. Masson-Delmotte V, Kageyama M, Braconnot P, Charbit S, Krinner G, Ritz C, Guilyardi E, Jouzel J, Abe-Ouchi A, Crucifix M, Gladstone RM, Hewitt CD, Jitoh A, LeGrande AN, Marti O, Merkel U, Motoi T, Ohgaito R, Otto-Bliesner B, Peltier WR, Ross I, Valdes PJ, Vettoretti G, Weber SL, Wolk F, Yu Y (2006) Past and future polar amplification of climate change: climate model intercomparisons and ice-core constraints. Clim Dyn 26:513–529. doi: 10.1007/s00382-005-0081-9 CrossRefGoogle Scholar
  23. Matthews HD, Caldeira K (2007) Transient climate-carbon simulations of planetary geoengineering. Proc Natl Acad Sci USA 104:9949–9954. doi: 10.1073/pnas.0700419104 CrossRefGoogle Scholar
  24. 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 365:1993–2028. doi: 10.1098/rsta.2007.2077 CrossRefGoogle Scholar
  25. Olsen JS, Watts JA, Allison LJ (1985) World major ecosystem complexes ranked by carbon in live vegetation. NDP-017, Carbon Dioxide Information Analysis Centre. Oak Ridge National Laboratory, Oak RidgeGoogle Scholar
  26. Peltier WR (1994) Ice age paleotopography. Science 265:195–201. doi: 10.1126/science.265.5169.195 CrossRefGoogle Scholar
  27. Peng CH, Guiot J, van Campo E (1998) Estimating changes in terrestrial vegetation and carbon storage: using palaeoecological data and models. Quat Sci Rev 17:719–735. doi: 10.1016/S0277-3791(97)00045-0 CrossRefGoogle Scholar
  28. R Development Core Team (2004) R: a language and environment for statistical computing, R foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-00-3,
  29. Rougier J (2007) Probabilistic inference for future climate using an ensemble of climate model evaluations. Clim Change 81:247–264. doi: 10.1007/s10584-006-9156-9 CrossRefGoogle Scholar
  30. Rougier J, Cameron D, Edwards NR, Price AR (in preparation) Precalibrating an intermediate complexity climate model (EMIC)Google Scholar
  31. Saltelli A, Chan K, Scott M (2000) Sensitivity analysis. Wiley, New YorkGoogle Scholar
  32. Santner T, Williams B, Notz W (2003) The design and analysis of computer experiments. Springer, New YorkGoogle Scholar
  33. Schneider von Deimling T, Held H, Ganopolski A, Rahmstorf S (2006a) Climate sensitivity estimated from ensemble calculations of glacial climate. Clim Dyn 27:149–163. doi: 1007/s00382-006-0126-8 CrossRefGoogle Scholar
  34. Schneider von Deimling T, Ganopolsky A, Held H, Rahmstorf S (2006b) How cold was the last glacial maximum? Geophys Res Lett 33:L14709. doi: 10.1029/2006GL026484 CrossRefGoogle Scholar
  35. Soden BJ, Held IM (2006) An assessment of climate feedbacks in coupled ocean–atmosphere models. J Clim 19:3354–3360CrossRefGoogle Scholar
  36. Stainforth DA et al (2005) Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433:403–406. doi: 10.1038/nature03301 CrossRefGoogle Scholar
  37. Thompson SL, Warren SG (1982) Parametization of outgoing infrared radiation derived from detailed radiative calculations. J Atmos Sci 39:2667–2680. doi: 10.1175/1520-0469(1982)039<2667:POOIRD>2.0.CO;2 CrossRefGoogle Scholar
  38. Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, New YorkGoogle Scholar
  39. Webb MJ et al (2006) On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Clim Dyn 27:17–38. doi: 10.1007/s00382-006-0111-2 CrossRefGoogle Scholar
  40. Weber SL, Drijfhout SS, Abe-Ouchi A, Crucifix M, Eby M, Ganopolski A, Murakami S, Otto-Bliesner B, Peltier WR (2007) The modern and glacial overturning circulation in the Atlantic ocean in PMIP coupled model simulations. Clim Past 3:51–64CrossRefGoogle Scholar
  41. Williamson MS, Lenton TM, Shepherd JG, Edwards NR (2006) An efficient numerical terrestrial scheme (ENST) for earth system modelling. Ecol Modell 198:362–374. doi: 10.1016/j.ecolmodel.2006.05.027 CrossRefGoogle Scholar
  42. Wullshleger SD, Post WM, King AW (1995) On the potential for a CO2 fertilization effect in forests: estimates of the biotic growth factor based on 58 controlled exposure studies? In: Woodwell GM, Mackenzie FT (eds) Biotic feedbacks in the global system: will the warming feed the warming. Oxford University Press, Oxford, pp 85–107Google Scholar
  43. Zaucker F, Broecker WS (1992) The influence of atmospheric moisture transport on the freshwater balance of the Atlantic drainage basin: general circulation model simulations and observations. J Geophys Res 97:2765–2773Google Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Philip B. Holden
    • 1
    Email author
  • N. R. Edwards
    • 1
  • K. I. C. Oliver
    • 1
  • T. M. Lenton
    • 2
    • 3
  • R. D. Wilkinson
    • 4
  1. 1.Department of Earth and Environmental SciencesThe Open UniversityMilton KeynesUK
  2. 2.School of Environmental SciencesUniversity of East AngliaNorwichUK
  3. 3.Tyndall Centre for Climate Change ResearchNorwichUK
  4. 4.Department of Probability and StatisticsUniversity of SheffieldSheffieldUK

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