Skip to main content

Advertisement

Log in

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

Climate Dynamics Aims and scope Submit manuscript

Abstract

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.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  • Annan JD, Hargreaves JC (2006) Using multiple observationally-based constraints to estimate climate sensitivity. Geophys Res Lett 33:L06704. doi:10.1029/2005GL025259

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Arrhenius S (1896) On the influence of carbonic acid in the air upon the temperature of the ground. Philos Mag 41:237–276

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in population genetics. Genetics 162:2025–2035

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 

  • Colman R, McAvaney B (2009) Climate feedbacks under a broad range of forcing. Geophys Res Lett 36:L01702. doi:10.1029/2008GL036268

    Article  Google Scholar 

  • Crucifix M (2006) Does the last glacial maximum constrain climate sensitivity? Geophys Res Lett 33:L18701. doi:10.1029/2006GL027137

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Hargreaves JC, Abe-Ouchi A, Annan JD (2007) Linking glacial and future climates through and ensemble of GCM simulations. Clim Past 3:77–87

    Article  Google Scholar 

  • IPCC (2007) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge

    Google Scholar 

  • 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 416:719–723

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 2:221–235

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Murphy JM, Booth BBB, Collins M, Harris GR, Sexton DMH, Webb MJ (2007) A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles. Philos Trans R Soc A 365:1993–2028. doi:10.1098/rsta.2007.2077

    Article  Google Scholar 

  • 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 Ridge

    Google Scholar 

  • Peltier WR (1994) Ice age paleotopography. Science 265:195–201. doi:10.1126/science.265.5169.195

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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, http://www.R-project.org

  • 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

    Article  Google Scholar 

  • Rougier J, Cameron D, Edwards NR, Price AR (in preparation) Precalibrating an intermediate complexity climate model (EMIC)

  • Saltelli A, Chan K, Scott M (2000) Sensitivity analysis. Wiley, New York

    Google Scholar 

  • Santner T, Williams B, Notz W (2003) The design and analysis of computer experiments. Springer, New York

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Soden BJ, Held IM (2006) An assessment of climate feedbacks in coupled ocean–atmosphere models. J Clim 19:3354–3360

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, New York

  • 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

    Article  Google Scholar 

  • 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 3:51–64

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 85–107

    Google Scholar 

  • 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 97:2765–2773

    Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Philip B. Holden.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Holden, P.B., Edwards, N.R., Oliver, K.I.C. et al. A probabilistic calibration of climate sensitivity and terrestrial carbon change in GENIE-1. Clim Dyn 35, 785–806 (2010). https://doi.org/10.1007/s00382-009-0630-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-009-0630-8

Keywords

Navigation