Climate Dynamics

, Volume 27, Issue 2–3, pp 127–147 | Cite as

Towards quantifying uncertainty in transient climate change

  • Matthew Collins
  • Ben B. B. Booth
  • Glen R. Harris
  • James M. Murphy
  • David M. H. Sexton
  • Mark J. Webb
Article

Abstract

Ensembles of coupled atmosphere–ocean global circulation model simulations are required to make probabilistic predictions of future climate change. “Perturbed physics” ensembles provide a new approach in which modelling uncertainties are sampled systematically by perturbing uncertain parameters. The aim is to provide a basis for probabilistic predictions in which the impact of prior assumptions and observational constraints can be clearly distinguished. Here we report on the first perturbed physics coupled atmosphere–ocean model ensemble in which poorly constrained atmosphere, land and sea-ice component parameters are varied in the third version of the Hadley Centre model (the variation of ocean parameters will be the subject of future study). Flux adjustments are employed, both to reduce regional sea surface temperature (SST) and salinity biases and also to admit the use of combinations of model parameter values which give non-zero values for the global radiation balance. This improves the extent to which the ensemble provides a credible basis for the quantification of uncertainties in climate change, especially at a regional level. However, this particular implementation of flux-adjustments leads to a weakening of the Atlantic overturning circulation, resulting in the development of biases in SST and sea ice in the North Atlantic and Arctic Oceans. Nevertheless, model versions are produced which are of similar quality to the unperturbed and un-flux-adjusted version. The ensemble is used to simulate pre-industrial conditions and a simple scenario of a 1% per year compounded increase in CO2. The range of transient climate response (the 20 year averaged global warming at the time of CO2 doubling) is 1.5–2.6°C, similar to that found in multi-model studies. Measures of global and large scale climate change from the coupled models show simple relationships with associated measures computed from atmosphere-mixed-layer-ocean climate change experiments, suggesting that recent advances in computing the probability density function of climate change under equilibrium conditions using the perturbed physics approach may be extended to the transient case.

Notes

Acknowledgements

This work could not have been possible without the input from numerous and dedicated Hadley Centre staff, in particular Peter Good who downloaded the AR4 model data and Ian Culverwell who supplied the ocean heat transport data. Chris Brierley also helped in the analysis of ocean heat uptake efficiency. The work was supported by the UK Department of the Environment, Food and Rural Affairs under Contract PECD/7/12/37 and by the European Community ENSEMBLES (GOCE-CT-2003–505539) and DYNAMITE (GOCE-003903) projects under the Sixth Framework Programme. We acknowledge the international modeling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modelling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis activity, and the IPCC WG1 TSU for technical support. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science, U.S. Department of Energy.

References

  1. Andronova NG, Schlesinger ME (2001) Objective estimation of the probability density function for climate sensitivity. J Geophys Res 106:22605–22611CrossRefGoogle Scholar
  2. 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 (in press). DOI 10.1007/s00382-005-0097-1Google Scholar
  3. Collins M (2000) Understanding Uncertainties in the response of ENSO to Greenhouse Warming. Geophys Res Lett 27:3509–3513CrossRefGoogle Scholar
  4. Collins M, Tett SFB, Cooper C (2001) The internal climate variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 17:61–81CrossRefGoogle Scholar
  5. Collins M., the CMIP2 Modelling Groups (2005) El Niño- or La Niña-like climate change? Clim Dyn 24:89–104Google Scholar
  6. Covey C, AchutaRao KM, Cubasch U, Jones P, Lambert SJ, Mann ME, Phillips TJ, Taylor KE (2003) An overview of results from the Coupled Model Intercomparison Project. Glob Planet Change 37:103–133CrossRefGoogle Scholar
  7. 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
  8. Cox PM, Betts RA, Collins M, Harris PP, Huntingford C, Jones CD (2004) Amazonian forest dieback under climate-carbon cycle projections for the 21st century. Theoret Appl Climatol 78:137–156CrossRefGoogle Scholar
  9. Cubasch U, Meehl GA, Boer GJ, Stouffer RJ, Dix M, Noda A, Senior CA, Raper S, Yap KS (2001) Projections of future climate change. In: Climate Change 2001: The Scientific Basis. In: Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden P, Dai X, Maskell K, Johnson CI (eds.) Contribution of Working Group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, 525–582Google Scholar
  10. Doblas-Reyes FJ, Hagedorn R, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting. Part II: Calibration and combination. Tellus (in press)Google Scholar
  11. Forest CE, Stone PH, Sokolov AP, Allen MR, Webster MD (2002) Quantifying uncertainties in climate system properties with the use of recent climate observations. Science 295:113–117CrossRefPubMedGoogle Scholar
  12. Ganachaud A, Wunsch C (2000) Improved estimates of global ocean circulation, heat transport and mixing from hydrographic data. Nature 408:453–457CrossRefGoogle Scholar
  13. 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
  14. Gregory JM, Ingram WJ, Palmer MA, Jones GS, Stott PA, Thorpe RB, Lowe JA, Johns TC, Williams KD (2004) A new method for diagnosing radiative forcing and climate sensitivity. Geophs Res Lett 31:L03205CrossRefGoogle Scholar
  15. Grist JP, Josey SA (2003) Inverse analysis adjustment of the SOC air-sea flux climatology using ocean heat transport constraints. J Clim 20:3274–3295CrossRefGoogle Scholar
  16. Hagedorn R, Doblas-Reyes FJ, Palmer TN (2004) The rationale behind the success of multimodel ensembles in seasonal forecasting. Part I: Basic concept. Tellus (in press)Google Scholar
  17. Knutti R, Stocker TF, Joos F, Plattner GK (2002) Constraints on radiative forcing and future climate change from observations and climate model ensembles. Nature, 416:719–723CrossRefPubMedGoogle Scholar
  18. Lambert SJ, Boer GJ (2001) CMIP1 evaluation and intercomparison of coupled climate models. Clim Dyn 17:83–106CrossRefGoogle Scholar
  19. Levitus S, Boyer T (1994) World Ocean Atlas 1994. NOAA Atlas NESDIS, U.S. Department of Commerce, WashingtonGoogle Scholar
  20. MacDonald AM, Wunsch C (1996) An estimate of global ocean circulation and heat fluxes. Nature 382:436–439CrossRefGoogle Scholar
  21. Manabe S, Stouffer RJ (1988) Two stable equilibria of a coupled ocean-atmosphere model. J Clim 1:841–866CrossRefGoogle Scholar
  22. Meehl GA, Washington WM, Arblaster JM, Hu A (2004) Factors affecting climate sensitivity in global coupled models. J Clim 17:1584–1596CrossRefGoogle Scholar
  23. Molteni F, Buzzia R, Palmer TN, Petroliagis T (1996) The ECMWF ensemble prediction system: methodology and validation. Quart J Roy Met Soc 122:73–119CrossRefGoogle Scholar
  24. Murphy JM (1995) Transient response of the Hadley Centre coupled ocean-atmosphere model to increasing carbon dioxide. Part III: Analysis of global mean response using simple models. J Clim 8:496–514CrossRefGoogle Scholar
  25. 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 430:768–772CrossRefPubMedGoogle Scholar
  26. Palmer TN (2001) A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parametrization in weather and climate prediction models. Quart J Roy Met Soc 127:279–304CrossRefGoogle Scholar
  27. Palmer TN (2002) The economic value of ensemble forecasts as a tool for risk assessment: From days to decades. Quart J Roy Met Soc 128:747–774CrossRefGoogle Scholar
  28. Piani C, Frame DJ, Stainforth DA, Allen MR (2005) Constraints on climate change from a multi-thousand member ensemble of simulations. Geophys Res Lett 32:L23825. DOI 10.1029/2005GL024452Google Scholar
  29. Pope VD, Gallani ML, Rowntree PR, Stratton RA (2000) The impact pf new physical parametrizations in the Hadley Centre climate model—HadAM3. Clim Dyn 16:123–146CrossRefGoogle Scholar
  30. Raper SCB, Gregory JM, Osborn TJ (2001) Use of an upwelling-diffusion energy balance climate model to simulate and diagnose A/OGCM results. Clim Dyn 17:601–613CrossRefGoogle Scholar
  31. Raper SCB, Gregory JM, Stouffer RJ (2002) The role of climate sensitivity and ocean heat uptake on AOGCM transient temperature response. J Clim, 15:124–130CrossRefGoogle Scholar
  32. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of SST, sea ice and night marine air temperature since the late nineteenth century. J Geophys Res 108:10.1029/2002JD002670Google Scholar
  33. Robertson AW, Lall U, Zebiak SE, Goddard L (2004) Improved combination of multiple atmospheric GCM ensembles for seasonal prediction. Month Weather Rev 132:2732–2744CrossRefGoogle Scholar
  34. Senior CA, Mitchell JFB (2000) The time-dependence of climate sensitivity. Geophys Res Letts 27:2685–2688CrossRefGoogle Scholar
  35. Senior C, Wielicki B, McAvaney B, Boer G (2004) Report on the joint WCRP CFMIP/IPCC expert meeting on climate sensitivity and feedbacks. Annex 5 of IPCC Working Group 1 report of the Workshop on Climate SensivityGoogle Scholar
  36. Stainforth DA, Aina T, Christensen C, Collins M, Faull N, 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 433:403–406CrossRefGoogle Scholar
  37. Tebaldi C, Smith RL, Nychka D, Mearns LO (2005) Quantifying uncertainty in projections of regional climate change: a Bayesian approach to the analysis of multi-model ensembles. J Clim 18:1524–1540CrossRefGoogle Scholar
  38. Thorpe RB, Gregory JM, Johns TC, Wood RA, Mitchell JFB (2001) Mechanisms determining the Atlantic thermohaline circulation response to greenhouse gas forcing in a non-flux-adjusted coupled climate model. J Clim 14:3102–3116CrossRefGoogle Scholar
  39. Toth Z, Kalnay E (1997) Ensemble forecasting at NCEP and the breeding method. Month Weather Rev 125:3297–3319CrossRefGoogle Scholar
  40. Vellinga M, Wu PL (2004) Low-latitude freshwater influence on centennial variability of the Atlantic thermohaline circulation. J Clim 17:4498–4511CrossRefGoogle Scholar
  41. Webb MJ, Senior CA, Williams KD, Sexton MDH, Ringer MA, McAvaney BJ, Colman R, Soden BJ, Andronova NG, Emori S, Tsushima Y, Ogura T, Musat I, Bony S, Taylor K (2006) On uncertainty in feedback mechanisms controlling climate sensitivity in two GCM ensembles. Clim Dyn (in press). DOI 10.1007/s00382-006-0111-2Google Scholar
  42. Wigley TML, Raper SCB (2001). Interpretation of high projections for global-mean warming. Science, 293:451–454CrossRefPubMedGoogle Scholar
  43. Wood RA, Keen AB, Mitchell JFB, Gregory JM (1999) Changing spatial structure of the thermohaline circulation in response to atmospheric CO2 forcing in a climate model. Nature 399:572–575CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Matthew Collins
    • 1
  • Ben B. B. Booth
    • 1
  • Glen R. Harris
    • 1
  • James M. Murphy
    • 1
  • David M. H. Sexton
    • 1
  • Mark J. Webb
    • 1
  1. 1.Hadley Centre for Climate Prediction and ResearchMet OfficeExeterUK

Personalised recommendations