Climate Dynamics

, Volume 12, Issue 2, pp 77–100 | Cite as

Towards the detection and attribution of an anthropogenic effect on climate

  • Benjamin D. Santer
  • Karl E. Taylor
  • Tom M. L. Wigley
  • Joyce E. Penner
  • Philip D. Jones
  • Ulrich Cubasch
Open Access


It has been hypothesized recently that regional-scale cooling caused by anthropogenic sulfate aerosols may be partially obscuring a warming signal associated with changes in greenhouse gas concentrations. Here we use results from model experiments in which sulfate and carbon dioxide have been varied individually and in combination in order to test this hypothesis. We use centered [R(t)] and uncentered [C(t)] pattern similarity statistics to compare observed time-evolving surface temperature change patterns with the model-predicted equilibrium signal patterns. We show that in most cases, the C(t) statistic reduces to a measure of observed global-mean temperature changes, and is of limited use in attributing observed climate changes to a specific causal mechanism. We therefore focus on R(t), which is a more useful statistic for discriminating between forcing mechanisms with different pattern signatures but similar rates of global mean change. Our results indicate that over the last 50 years, the summer (JJA) and fall (SON) observed patterns of near-surface temperature change show increasing similarity to the model-simulated response to combined sulfate aerosol/CO2 forcing. At least some of this increasing spatial congruence occurs in areas where the real world has cooled. To assess the significance of the most recent trends in R(t) and C(t), we use data from multi-century control integrations performed with two different coupled atmosphere-ocean models, which provide information on the statistical behavior of ‘unforced’ trends in the pattern correlation statistics. For the combined sulfate aerosol/CO2 experiment, the 50-year R(t) trends for the JJA and SON signals are highly significant. Results are robust in that they do not depend on the choice of control run used to estimate natural variability noise properties. The R(t) trends for the C02-only signal are not significant in any season. C(t) trends for signals from both the C02-only and combined forcing experiments are highly significant in all seasons and for all trend lengths (except for trends over the last 10 years), indicating large global-mean changes relative to the two natural variability estimates used here. The caveats regarding the signals and natural variability noise which form the basis of this study are numerous. Nevertheless, we have provided first evidence that both the largest-scale (global-mean) and smaller-scale (spatial anomalies about the global mean) components of a combined C02/anthropogenic sulfate aerosol signal are identifiable in the observed near-surface air temperature data. If the coupled-model noise estimates used here are realistic, we can be highly confident that the anthropogenic signal that we have identified is distinctly different from internally generated natural variability noise. The fact that we have been able to detect the detailed spatial signature in response to combined C02 and sulfate aerosol forcing, but not in response to C02 forcing alone, suggests that some of the regional-scale background noise (against which we were trying to detect a C02-only signal) is in fact part of the signal of a sulfate aerosol effect on climate. The large effect of sulfate aerosols found in this study demonstrates the importance of their inclusion in experiments designed to simulate past and future climate change.


Sulfate Aerosol Spatial Congruence Anthropogenic Sulfate Observe Climate Change Spatial Anomaly 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Allen MR, Smith LA (1994) Investigating the origins and significance of low-frequency modes of climate variability. Geophys Res Lett 21:883–886Google Scholar
  2. Allen MR, Mutlow CT, Blumberg GMC, Christy JR, McNider RT, Llewellyn-Jones DT (1994) Global change detection. Nature 370:24–35Google Scholar
  3. Anderson JL, van den Dool HM (1994) Skill and return of skill in dynamic extended-range forecasts. Mon Weather Rev 122:507–516Google Scholar
  4. Barnett TP (1986) Detection of changes in global tropospheric temperature field induced by greenhouse gases. J Geophys Res 91:6659–6667Google Scholar
  5. Barnett TP, Schlesinger ME (1987) Detecting changes in global climate induced by greenhouse gases. J Geophys Res 92:14772–14780Google Scholar
  6. Barnett TP, Santer BD, Jones PD, Bradley RS, Briffa KR (1995) Estimates of low-frequency natural variability in near-surface air temperature. The Holocene (accepted)Google Scholar
  7. Benkovitz C (1982) Compilation of an inventory of anthropogenic emissions in the United States and Canada. Atmos Environ 16:1551–1563Google Scholar
  8. Bloomfield P, Nychka D (1992) Climate spectra and detecting climate change. Clim Change 21:275–288Google Scholar
  9. Charlson RJ, Wigley TML (1994) Sulfate aerosol and climate change. Sci Am 270(2):48–57Google Scholar
  10. Charlson RJ, Langer J, Rodhe H, Leovy CB, Warren SG (1991) Perturbation of the Northern Hemisphere radiative balance by backscattering from anthropogenic sulfate aerosols. Tellus 43:152–163Google Scholar
  11. Charlson RJ, Schwartz SE, Hales JM, Cess RD, Coakley JA, Hansen JE, Hofmann DJ (1992) Climate forcing by anthropogenic aerosols. Science 255:423–430Google Scholar
  12. Crowley TJ, North GR (1991) Paleoclimatology. Oxford University Press, New York, USAGoogle Scholar
  13. Cubasch U, Hasselmann K, Höck H, Maier-Reimer E, Mikolajewicz U, Santer BD, Sausen R (1992) Time-dependent greenhouse warming computations with a coupled ocean-atmosphere model. Clim Dyn 8:55–69CrossRefGoogle Scholar
  14. Cubasch U, Santer BD, Hellbach A, Hegerl G, Höck H, Maier-Reimer E, Mikolajewicz U, Stössel A, Voss R (1994) Monte Carlo climate change forecasts with a global coupled ocean-atmosphere model. Clim Dyn 10:1–19Google Scholar
  15. Delworth T, Manabe S, Stouffer RJ (1993) Interdecadal variability of the thermohaline circulation in a coupled ocean-atmosphere model. J Clim 6:1993–2011Google Scholar
  16. Engardt M, Rodhe H (1993) A comparison between patterns of temperature trends and sulfate aerosol pollution. Geophys Res Lett 20:117–120Google Scholar
  17. Enting IG, Heimann H, Wigley TML (1994) Future emissions and concentrations of carbon dioxide: key ocean/atmosphere/land analyses. CSIRO Division of Atmospheric Res Techn Pap 31Google Scholar
  18. Fichefet T, Tricot C (1992) Influence of starting date of model integration on projections of greenhouse-gas-induced climatic change. Geophys Res Lett 19:1771–1774Google Scholar
  19. Folland CK, Karl TR, Nicholls N, Nyenzi BS, Parker DE, Vinnikov K Ya (1992) Observed Climate variability and change. In: Houghton JT, Callander BA, Varney SK (eds) Climate change 1992. The supplementary report to the IPCC scientific assessment. Cambridge University Press, Cambridge, pp 135–170Google Scholar
  20. Gates WL, Mitchell JFB, Boer GJ, Cubasch U, Meleshko VP (1992) Climate modelling, climate prediction and model validation. In: Houghton JT, Callander BA, Varney SK (eds) Climate change 1992. The supplementary report to the IPCC scientific assessment. Cambridge University Press, Cambridge, pp 97–134Google Scholar
  21. Gates WL, Cubasch U, Meehl GA, Mitchell JFB, Stouffer RJ (1993) An intercomparison of selected features of the control climates simulated by coupled ocean-atmosphere general circulation models. World Climate Research Programme, WCRP-82, Geneva, SwitzerlandGoogle Scholar
  22. Hansen J, Lacis A, Rind D, Russel G, Stone P, Fung I, Ruedy R, Lerner J (1984) Climate sensitivity: Analysis of feedback mechanisms. In: Hansen J, Takahasi T (eds) Climate processes and climate sensitivity. Maurice Ewing Series 5, American Geophysical Union, Washington D.C., pp 130–163Google Scholar
  23. Hansen J, Fung I, Lacis A, Rind D, Lebedeff S, Ruedy R, Russell G, Stone P (1988) Global climate changes as forecast by Goddard Institute for Space Studies three-dimensional model. J Geophys Res 93:9341–9364Google Scholar
  24. Hansen J, Lacis A, Ruedy R, Sato M, Wilson H (1993) How sensitive is the world's climate? Nat Geog Res Explor 9:142–158Google Scholar
  25. Hasselmann K (1976) Stochastic climate models. Part I: theory. Tellus 28:473–485Google Scholar
  26. Hasselmann K (1979) On the signal-to-noise problem in atmospheric response studies. In: Shaw DB (ed) Meteorology of tropical oceans. Roy Meteorol Soc London, pp 251–259Google Scholar
  27. Hasselmann K (1993) Optimal fingerprints for the detection of time dependent climate change. J Clim 6:1957–1971Google Scholar
  28. Hegerl GC, Storch H, Hasselmann K, Santer BD, Cubasch U, Jones PD (1994) Detecting anthropogenic climate change with an optimal fingerprint method. Max-Planck Institut für Meteorologic Report 142, Hamburg, GermanyGoogle Scholar
  29. Hunter DE, Schwartz SE, Wagoner R, Benkovitz CM (1993) Seasonal, latitudinal, and secular variations in temperature trend: evidence for influence of anthropogenic sulfate. Geophys Res Lett 20:2455–2458Google Scholar
  30. Jenkins GM, Watts DG (1968) Spectral analysis and its applications. Holden-Day, San Francisco, USAGoogle Scholar
  31. Jones PD (1994) Recent warming in global temperature series. Geophys Res Lett 21:1149–1152Google Scholar
  32. Jones PD, Briffa KR (1992) Global surface air temperature variations during the twentieth century: Part 1, spatial, temporal and seasonal details. The Holocene 2:165–179Google Scholar
  33. Jones PD, Wigley TML, Farmer G (1991) Marine and land temperature data sets: a comparison and a look at recent trends. In: Schlesinger ME (ed) Greenhouse-gas-induced climatic change: a critical appraisal of simulations and observations. Elsevier, Amsterdam, pp 153–172Google Scholar
  34. Karl TR, Heim RR Jr, Quayle RG (1991) The greenhouse effect in central North America: if not now, when? Science 251:1058–1061Google Scholar
  35. Karl TR, Knight RW, Kukla G, Gavin J (1995) Evidence for radiative effects of anthropogenic sulfate aerosols in the observed climate record. In: Charlson R, Heintzenberg J (eds) Aerosol forcing of climate. John Wiley, Chichester, UK, pp 363–382Google Scholar
  36. Kelly PM, Jones PD, Sear CB, Cherry BSG, Tavakol RK (1982) Variations in surface air temperatures, Part 2: Arctic regions, 1881–1980. Mon Weather Rev 110:71–83Google Scholar
  37. Kiehl JT, Briegleb BP (1993) The relative role of sulfate aerosols and greenhouse gases in climate forcing. Science 260:311–314Google Scholar
  38. MacCracken MC, Moses H (1982) The first detection of carbon dioxide effects: Workshop Summary, 8–10 June 1981, Harpers Ferry, West Virginia. Bull Am Meteorol Soc 63:1164–1178Google Scholar
  39. Madden RA, Ramanathan V (1980) Detecting climate change due to increasing carbon dioxide. Science 209:763–768Google Scholar
  40. Manabe S, Stouffer RJ (1980) Sensitivity of a global climate model to an increase in the C02 concentration in the atmosphere. J Geophys Res 85:5529–5554Google Scholar
  41. Meehl GA, Washington WM, Karl TR (1993) Low-frequency variability and CO2 transient climate change. Clint Dyn 8:117–133Google Scholar
  42. Mehta VM, Delworth T (1995) Decadal variability of the tropical Atlantic Ocean surface temperature in shipboard measurements and in a global ocean-atmosphere model. J Clim 8:172–190Google Scholar
  43. Metchell JFB, Davis RA, Ingram WJ, Senior CA (1995a) On surface temperatures greenhouse gases and aerosols: models and observations. J Clim (in press)Google Scholar
  44. Mitchell JFB, Johns TC, Gregory JM, Tett SFB (1995b) Transient climate response to increasing sulphate aerosols and greenhouse gases. Nature 376:501–504CrossRefGoogle Scholar
  45. Parker DE, Jones PD, Folland CK, Bevan A (1994) Interdecadal changes of surface temperature since the late nineteenth century. J Geophys Res 99:14373–14399CrossRefGoogle Scholar
  46. Penner JE, Dickinson R, O'Neill C (1992) Effects of aerosol from biomass burning on the global radiation budget. Science 256:1432–1434Google Scholar
  47. Penner JE, Atherton CA, Graedel TE (1994a) Global emissions and models of photochemically active compounds. In: Prinn R (ed) Global atmospheric-biospheric chemistry. Plenum, New York, pp 223–248Google Scholar
  48. Penner JE, Charlson RJ, Hales JM, Laulainen NS, Leifer R, Novakov T, Ogren J, Radke LF, Schwartz SE, Travis L (1994b) Quantifying and minimizing uncertainty of climate forcing by anthropogenic aerosols. Bull Am Meteorol Soc 75:375–400Google Scholar
  49. Preisendorfer RW, Barnett TP (1983) Numerical model-reality intercomparison tests using small-sample statistics. J Atmos S640:1884–1896Google Scholar
  50. Roeckner E, Siebert T, Feichter J (1995) Climatic response to anthropogenic sulfate forcing simulated with a general circulation model. In: Charlson R, Heintzenberg J (eds) Aerosol forcing of climate. John Wiley, Chichester, UK, pp 349–362Google Scholar
  51. Santer BD, Wigley TML, Jones PD (1993) Correlation methods in fingerprint detection studies. Clim Dyn 8:265–276Google Scholar
  52. Santer BD, Brüggemann W, Cubasch U, Hasselmann K, Höck H. Maier-Reimer E, Mikolajewicz U (1994) Signal-to-noise analysis of time-dependent greenhouse warming experiments. Part 1: pattern analysis. Clim Dyn 9:267–285Google Scholar
  53. Santer BD, Taylor KE, Wigley TML, Penner JE, Jones PD, Cubasch U (1995a) Towards the detection and attribution of an anthropogenic effect on climate. PCMDI Report 21, Lawrence Livermore National Laboratory, Livermore, CaliforniaGoogle Scholar
  54. Santer BD, Mikolajewicz U, Brüggemann W, Cubasch U, Hasselmann K, Höck H, Maier-Reimer E, Wigley TML (1995b) Ocean variability and its influence on the detectability of greenhouse warming signals. J Geophys Res 100:10693–10725Google Scholar
  55. Schlesinger ME, Mitchell JFB (1987) Climate model simulations of the equilibrium climatic response to increased carbon dioxide. Rev Geophys 25:760–798Google Scholar
  56. Spiro PA, Jacob DJ, Logan JA (1992) Global inventory of sulfur emissions in the United States and Canada. J Geophys Res 97:6023–6036Google Scholar
  57. Stocker TF, Mysak LA (1992) Climatic fluctuations on the century time scale: a review of high-resolution proxy data and possible mechanisms. Clim Change 20:227–250Google Scholar
  58. Stouffer RJ, Manabe S, Bryan K (1989) Interhemispheric asymmetry in climate response to a gradual increase of atmospheric C02. Nature 342:660–662Google Scholar
  59. Stouffer RJ, Manabe S, Vinnikov K Ya (1994) Model assessment of the role of natural variability in recent global warming. Nature 367:634–636Google Scholar
  60. Taylor KE, Ghan SJ (1992) An analysis of cloud liquid water feedback and global climate sensitivity in a general circulation model. J Clim 5:907–919Google Scholar
  61. Taylor KE, Penner JE (1994) Response of the climate system to atmospheric aerosols and greenhouse gases. Nature 369:734–737Google Scholar
  62. Walton JJ, MacCracken MC, Ghan SJ (1988) A global-scale Lagrangian trace species model of transport, transformation and removal processes. J Geophys Res 93:8339–8354Google Scholar
  63. Wang W-C, Dudek MP, Liang X-Z, Kiehl JT (1991) Inadequacy of effective C02 as a proxy in simulating the greenhouse effect of other radiatively active gases. Nature 350:573–577Google Scholar
  64. Washington WM, Meehl GA (1989) Seasonal cycle experiments on the climate sensitivity due to a doubling of C02 with an atmospheric general circulation model coupled to a simple mixed layer ocean model. J Geophys Res 89:9475–9503Google Scholar
  65. Wigley TML (1989) Possible climate change due to SO2-derived cloud condensation nuclei. Nature 339:365–367Google Scholar
  66. Wigley TML (1991) Could reducing fossil-fuel emissions cause global warming? Nature 349:503–506CrossRefGoogle Scholar
  67. Wigley TML, Jones PD (1981) Detecting CO2-induced climatic change. Nature 292:205–208Google Scholar
  68. Wigley TML, Raper SCB (1990) Natural variability of the climate system and detection of the greenhouse effect. Nature 344:324–327Google Scholar
  69. Wigley TML, Raper SCB (1991a) Internally-generated natural variability of global-mean temperatures. In: Schlesinger ME (ed) Greenhouse-gas-induced climatic change: a critical appraisal of simulations and observations. Elsevier, Amsterdam, pp 471–482Google Scholar
  70. Wigley TML, Raper SCB (1991b) Detection of the enhanced greenhouse effect on climate. In: Jäger J, Ferguson HL (eds) Climate change: science, impacts and policy. Cambridge University Press, Cambridge, UK, pp 231–242Google Scholar
  71. Wigley TML, Raper SCB (1992) Implications for climate and sea level of revised IPCC emissions scenarios. Nature 357:293–300Google Scholar
  72. Wigley TML, Santer BD (1990) Statistical comparison of spatial fields in model validation, perturbation, and predictability experiments. J Geophys Res 95:851–865Google Scholar
  73. Wigley TML, Jones PD, Kelly PM, Raper SCB (1989) Statistical significance of global warming. Proc 13th Ann Clim Diag Workshop, pp Al–A8 Wigley TML, Jones PD, Kelly PM, Hulme M (1992) Recent global temperature changes: ozone and aerosol influences. Proc 16th Ann Clint Diag Workshop, pp 194–202Google Scholar
  74. Woodward WA, Gray HL (1993) Global warming and the problem of testing for trend in time series data. J Clim 46:953–962Google Scholar

Copyright information

© Springer-Verlag 1995

Authors and Affiliations

  • Benjamin D. Santer
    • 1
  • Karl E. Taylor
    • 1
    • 2
  • Tom M. L. Wigley
    • 3
  • Joyce E. Penner
    • 2
  • Philip D. Jones
    • 4
  • Ulrich Cubasch
    • 5
  1. 1.Program for Climate Model Diagnosis and IntercomparisonLawrence Livermore National LaboratoryLivermoreUSA
  2. 2.Global Climate Research DivisionLawrence Livermore National LaboratoryLivermoreUSA
  3. 3.National Center for Atmospheric ResearchBoulderUSA
  4. 4.Climatic Research UnitUniversity of East AngliaNorwichUK
  5. 5.Deutsches KlimarechenzentrumBundesstrasseHamburgGermany

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