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Climate model errors, feedbacks and forcings: a comparison of perturbed physics and multi-model ensembles

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Abstract

Ensembles of climate model simulations are required for input into probabilistic assessments of the risk of future climate change in which uncertainties are quantified. Here we document and compare aspects of climate model ensembles from the multi-model archive and from perturbed physics ensembles generated using the third version of the Hadley Centre climate model (HadCM3). Model-error characteristics derived from time-averaged two-dimensional fields of observed climate variables indicate that the perturbed physics approach is capable of sampling a relatively wide range of different mean climate states, consistent with simple estimates of observational uncertainty and comparable to the range of mean states sampled by the multi-model ensemble. The perturbed physics approach is also capable of sampling a relatively wide range of climate forcings and climate feedbacks under enhanced levels of greenhouse gases, again comparable with the multi-model ensemble. By examining correlations between global time-averaged measures of model error and global measures of climate change feedback strengths, we conclude that there are no simple emergent relationships between climate model errors and the magnitude of future global temperature change. Algorithms for quantifying uncertainty require the use of complex multivariate metrics for constraining projections.

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Notes

  1. Here we use the term “error” and “model error” to mean differences between models and the real world, as is common in numerical weather and climate modelling, rather than, e.g. coding errors or bugs that might be easily corrected.

References

  • Ackerley D, Highwood EJ, Frame D, Booth BBB (2009) Changes in the global sulfate burden due to perturbations in global CO2 concentrations. J Clim 20:5421–5432

    Article  Google Scholar 

  • Adler RF et al (2003) The Version 2 global precipitation climatology project (GPCP) Monthly precipitation analysis (1979-Present). J Hydrometeorol 4:1147–1167

    Article  Google Scholar 

  • Allan RJ, Ansell TJ (2006) A new globally complete monthly historical mean sea level pressure data set (HadSLP2): 1850–2004. J Clim 19:5816–5842

    Article  Google Scholar 

  • Allen MR, Kettleborough J, Stainforth DA (2002) Model error in weather and climate forecasting. In: Proceedings of the ECMWF seminar series. http://www.ecmwf.int

  • Annan JD, Hargreaves JC (2010) Reliability of the CMIP3 ensemble. Geophys Res Lett 37:L02703. doi:10.1029/2009GL041994

    Article  Google Scholar 

  • Annan JD, Hargreaves JC, Ohgaito R, Abe-Ouchi A, Emori S (2005) Efficiently constraining climate sensitivity with ensembles of Paleoclimate simulations. Sci On-line Lett Atmos 1:181–184

    Google Scholar 

  • Aumann HH et al (2003) AIRS/AMSU/HSB on the Aqua mission: design, science objectives, data products, and processing systems. IEEE Trans Geosci Remote Sens 41:253–264

    Article  Google Scholar 

  • 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 26:489–511

    Article  Google Scholar 

  • Boer G, Yu B (2003) Climate sensitivity and response. Clim Dyn 20:415–429

    Google Scholar 

  • Brierley CM, Thorpe AJ, Collins M (2009) An example of the dependence of the transient climate response on the temperature of the modelled climate state. Atmos Sci Lett 10:23–28

    Article  Google Scholar 

  • Brierley CM, Collins M, Thorpe AJ (2010) The impact of perturbations to ocean-model parameters on climate and climate change in a coupled model. Clim Dyn 34:325–343

    Article  Google Scholar 

  • Brohan P, Kennedy JJ, Harris I, Tett SFB, Jones PD (2006) Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850. J Geophys Res 111:D12106. doi:10.1029/2005JD006548

    Article  Google Scholar 

  • Cess RD et al (1990) Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J Geophys Res 95:16601–16615

    Article  Google Scholar 

  • Collins WV (2006) Radiative forcing by well-mixed greenhouse gases: Estimates from climate models in the IPCC AR4. J Geophys Res 111:D14317. doi:10.1029/2005JD006713

    Article  Google Scholar 

  • Collins M (2007) Ensembles and probabilities: a new era in the prediction of climate change. Philos Trans R Soc Lond A 365:1957–1970

    Article  Google Scholar 

  • Collins M, Booth BBB, Harris GR, Murphy JM, Sexton DMH, Webb MJ (2006) Towards quantifying uncertainty in transient climate change. Clim Dyn 27:127–147

    Article  Google Scholar 

  • Collins M, Brierley CM, MacVean M, Booth BBB, Harris GR (2007) The sensitivity of the rate of transient climate change to ocean physics perturbations. J Clim 20:2315–2320

    Article  Google Scholar 

  • Colman RA (2003) A comparison of climate feedbacks in general circulation models. Clim Dyn 20:865–873

    Google Scholar 

  • Da Silva A, Young C, Levitus S (1994) Atlas of surface marine data 1994, volume 1: algorithms and procedures. NOAA Atlas NESDIS 6. US Department of Commerce, Washington

    Google Scholar 

  • Dijkstra HA, Neelin JD (1999) Imperfections of the thermohaline circulation: multiple equilibria and flux correction. J Clim 12:1382–1392

    Article  Google Scholar 

  • Forest CE, Stone PH, Sokolov AP (2006) Estimated PDFs of climate system properties including natural and anthropogenic forcings. Geophys Res Lett 33:L01705

    Article  Google Scholar 

  • Forster PMdeF, Taylor KE (2006) Climate forcings and climate sensitivities diagnosed from coupled climate model integrations. J Clim 19:6181–6194

    Article  Google Scholar 

  • Forster PMdeF et al (2007) Changes in atmospheric constituents and in radiative forcing. 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 fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

  • Frame DJ et al (2009) The climateprediction.net BBC climate change experiment part 1: design of the coupled model ensemble. Philos Trans R Soc Lond A 367:855–870

    Article  Google Scholar 

  • Gleckler PJ, Taylor KE, Doutriaux C (2008) Performance metrics for climate models. J Geophys Res 113:D06104. doi:10.1029/2007JD008972

    Article  Google Scholar 

  • Gordon CC et al (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 16:147–168

    Article  Google Scholar 

  • Gregory JM, Webb MJ (2008) Tropospheric adjustment induces a cloud component in CO2 forcing. J Clim 21:58–71

    Article  Google Scholar 

  • Gregory JM et al (2004) A new method for diagnosing radiative forcing and climate sensitivity. Geophys Res Lett 31:L03205

    Article  Google Scholar 

  • Grist JP, Josey SA (2003) Inverse analysis adjustment of the SOC air–sea flux climatology using ocean heat transport constraints. J Clim 20:3274–3295

    Article  Google Scholar 

  • Hagedorn R, Doblas-Reyes FJ, Palmer TN (2005) The rationale behind the success of multimodel ensembles in seasonal forecasting. Part I. Basic concept. Tellus 57:219–233

    Article  Google Scholar 

  • Hansen J, Ruedy R, Sato M, Reynolds R (1996) Global surface air temperature in 1995: return to pre-Pinatubo level. Geophys Res Lett 23:1665–1668

    Article  Google Scholar 

  • Harris GR, Sexton DMH, Booth BBB, Collins M, Murphy JM, Webb MJ (2006) Frequency distributions of transient regional climate change from perturbed physics ensembles of general circulation model simulations. Clim Dyn 27:357–375

    Article  Google Scholar 

  • Harrison EF, Minnis P, Barkstrom BR, Ramanathan V, Cess R, Gibson CG (1990) Seasonal variation of cloud radiative forcing derived from the Earth Radiation Budget Experiment. J Geophys Res 95:687–703

    Google Scholar 

  • Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Clim 19:5686–5699

    Article  Google Scholar 

  • Hibbard KA, Meehl GA, Cox PM, Friedlingsten P (2007) A strategy for climate change stabilization experiments. EOS 88:20. doi:10.1029/2007EO200002

    Article  Google Scholar 

  • Huntingford C, Cox PM (2000) An analogue model to derive additional climate change scenarios from existing GCM simulations. Clim Dyn 16:575–586

    Article  Google Scholar 

  • Jackson CS, Sen MK, Huerta G, Deng Y, Bowman KP (2008) Error reduction and convergence in climate prediction. J Clim 21:6698–6709

    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 106:20293–20310

    Article  Google Scholar 

  • Joshi MM, Gregory JM, Webb MJ, Sexton DMH, Johns TC (2008) Mechanisms for the land/sea warming exhibited by simulations of climate change. Clim Dyn 30:455–465

    Article  Google Scholar 

  • Jun M, Knutti R, Nychka DW (2008) Spatial analysis to quantify numerical model bias and dependence: how many climate models are there? J Am Stat Assoc Appl Case Stud 103:934–947

    Article  Google Scholar 

  • Knutti R, Meehl GA, Allen MR, Stainforth DA (2006) Constraining climate sensitivity from the seasonal cycle in surface temperature. J Clim 19:4224–4233

    Article  Google Scholar 

  • Knutti R, Furrer R, Tebaldi C, Cernak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim (in press)

  • Lambert SJ, Boer HJ (2001) CMIP1 evaluation and intercomparison of coupled climate models. Clim Dyn 17:83–106

    Article  Google Scholar 

  • Lambert FH, Chiang JCH (2007) Control of land–ocean temperature contrast by ocean heat uptake. Geophys Res Lett 34:L13704

    Article  Google Scholar 

  • Legates DR, Willmott CJ (1990) Mean seasonal and spatial variability in global surface air temperature. Theor Appl Climatol 41:11–21

    Google Scholar 

  • McKay MD, Conover WJ, Beckman RJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239–245

    Article  Google Scholar 

  • Meehl GA, Stocker T et al (2007a) Global climate projections. I. Climate Change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of working Group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

  • Meehl GA et al (2007b) The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394

    Article  Google Scholar 

  • Min SK, Simonis D, Hense A (2007) Probabilistic climate change predictions applying Bayesian model averaging. Philos Trans R Soc Lond A 365:2103–2116

    Article  Google Scholar 

  • Molteni F, Buizza R, Palmer TN, Petroliagis T (2006) The ECMWF ensemble prediction system: methodology and validation. Quart J Roy Meteorol Soc 122:73–119

    Article  Google Scholar 

  • Moore B, Gates WL, Mata LJ, Underdal A (2001) Advancing our understanding. In: Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (eds) Climate change 2001: the scientific basis. Contribution of working Group I to the third assessment report of the intergovernmental panel on climate change, Cambridge University Press

  • 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 8:496–514

    Article  Google Scholar 

  • 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 430:768–772

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Murphy JM, Sexton DMH, Jenkins G, Boorman P, Booth BBB, Brown K, Clark R, Collins M, Harris GR, Kendon E (2009) Climate change projections. ISBN 978-1-906360-02-3

  • Myhre G, Highwood EJ, Shine KP, Stordal F (1998) New estimates of radiative forcing due to well mixed greenhouse gases. Geophys Res Lett 25(14):2715–2718. doi:10.1029/98GL01908

    Google Scholar 

  • Niehörster F, Spangehl T, Fast I, Cubasch U (2006) Quantification of model uncertainties: parameter sensitivities of the coupled model ECHO-G with middle atmosphere. Geophys Res Abs 8, EGU06-A-08526

  • 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:10.1029/2005GL024452

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Raper SCB, Gregory JM, Stouffer RJ (2002) The role of climate sensitivity and ocean heat uptake on AOGCM transient temperature response. J Clim 15:124–130

    Article  Google Scholar 

  • Rayner NA et al (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108, D14, 4407. doi:10.1029/2002JD002670

  • Reichler T, Kim J (2008) How well do climate models simulate today’s climate? Bull Am Meteorol Soc 89:303–311

    Article  Google Scholar 

  • Rossow WB, Walker AW, Beuschel DE, Roiter MD (1996) International Satellite Cloud Climatology Project (ISCCP) documentation of new cloud datasets World Meteorological Organisation WMO/TD 737, pp 115

  • Rougier JC (2007) Probabilistic inference for future climate using an ensemble of climate model evaluations. Clim Change 81:247–264

    Article  Google Scholar 

  • Rougier JC, Sexton DMH, Murphy JM, Stainforth DA (2009) Analysing the climate sensitivity of the HadSM3 climate model using ensembles from different but related experiments. J Clim 22:3540–3557

    Article  Google Scholar 

  • Sanderson BM, Piani C (2007) Towards constraining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics GCM simulations. Clim Dyn 30:175–190

    Article  Google Scholar 

  • Sanderson BM et al (2008) Constraints on model response to greenhouse gas forcing and the role of subgrid-scale processes. J Clim 21:2384–2400

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Schneider von Deimling T, Held H, Ganopolski A, Rahmstorf S (2006) Climate sensitivity estimated from ensemble simulations of glacial climates. Clim Dyn 27:149–163

    Article  Google Scholar 

  • Senior CA, Mitchell JFB (2000) The time dependence of climate sensitivity. Geophys Res Lett 27:2685–2688

    Article  Google Scholar 

  • Smith TM, Reynolds RW (2004) Improved extended reconstruction of SST (1854–1997). J Clim 17:2466–2477

    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 

  • Soden BJ, Broccoli AJ, Hemler RS (2004) On the use of cloud forcing to estimate cloud feedback. J Clim 17(19):3661–3665

    Article  Google Scholar 

  • Sokolov AP et al (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 

  • Stainforth DA et al (2005) Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433:403–406

    Article  Google Scholar 

  • Stocker TF (2004) Climate change: models change their tune. Nature 430:737–738

    Google Scholar 

  • Stott PA, Forest CE (2007) Ensemble climate predictions using climate models and observational constraints. Philos Trans R Soc Lond A 365:2029–2052

    Article  Google Scholar 

  • Sutton RT, Dong B-W, Gregory JM (2007) Land/sea warming ratio in response to climate change: IPCC AR4 model results and comparison with observations. Geophys Res Lett 34:L02701

    Article  Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106:7183–7192

    Article  Google Scholar 

  • Taylor KE, Crucifix M, Doutriaux C, Broccoli AJ, Mitchell JFB, Webb MJ (2007) Estimating shortwave radiative forcing and response in climate models. J Clim 20:2530–2543

    Article  Google Scholar 

  • Tziperman E, Toggweiler JR, Feliks Y, Bryan K (1994) Instability of the thermohaline circulation with respect to mixed boundary conditions: is it really a problem for realistic models? J Phys Oceanogr 24:217–232

    Article  Google Scholar 

  • Uppala SM et al (2005) The ERA-40 re-analysis. Quart J Roy Meteorol Soc 131:2961–3012

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Webster MD et al (2002) Uncertainty in emissions projections for climate models. Atmos Environ 36:3659–3670

    Article  Google Scholar 

  • Wielicki BA, Barkstrom BR, Harrison EF, Lee RB III, Louis Smith G, Cooper JE (1996) Clouds and the Earth’s Radiant Energy System (CERES): an earth observing system experiment. Bull Am Meteorol Soc 77:853–868

    Article  Google Scholar 

  • Wylie DP, Menzel WP, Woolf HM, Strabala KI (1994) Four years of global cirrus cloud statistics using HIRS. J Clim 7:1972–1986

    Article  Google Scholar 

  • Xie P, Arkin PA (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Am Meteorol Soc 78:2539–2558

    Article  Google Scholar 

  • Yokohata T et al (2008) Comparison of equilibrium and transient responses to CO2 increase in eight state-of-the-art climate models. Tellus 60:946–961

    Article  Google Scholar 

  • Yokohata T, Webb MJ, Collins M, Williams KD, Yoshimori M, Hargreaves JC, Annan JD (2010) Structural similarities and differences in climate responses to CO2 increase between two perturbed physics ensembles. J Clim 23(6):1392–1410

    Google Scholar 

  • Zhang MH, Cess RD, Hack JJ, Kiehl JT (1994) Diagnostic study of climate feedback processes in atmospheric GCMs. J Geophys Res 99:5525–5537

    Article  Google Scholar 

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Acknowledgments

This work was supported by the Joint DECC and Defra Integrated Climate Programme—DECC/Defra (GA01101) and by the European Community ENSEMBLES (GOCE-CT-2003-505539). Hugo Lambert made useful comments on an earlier version of the manuscript and we thank three anonymous reviewers for their comments.

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Collins, M., Booth, B.B.B., Bhaskaran, B. et al. Climate model errors, feedbacks and forcings: a comparison of perturbed physics and multi-model ensembles. Clim Dyn 36, 1737–1766 (2011). https://doi.org/10.1007/s00382-010-0808-0

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