Climate Dynamics

, Volume 21, Issue 5–6, pp 493–500 | Cite as

Estimating signal amplitudes in optimal fingerprinting. Part II: application to general circulation models

Article

Abstract.

We show that there is a significant low bias in standard estimates of the amplitudes of climate change signals estimated by small ensembles of coupled ocean atmosphere general circulation models. This bias can be eliminated either by making larger ensembles of at least eight members or by employing total least squares regression (TLS) to take account of sampling uncertainty in model-simulated signals. Results using TLS agree with previous work using ordinary least squares regression (OLS) in showing that recent interdecadal warming trends in near-surface temperature are largely anthropogenic in origin. Consistent with previous results, we detect evidence of solar influence on surface temperature changes in the first half of the twentieth century. However the amplitudes of model-predicted signals in the observed record were previously underestimated by ordinary least squares regression. This implies that over the last 30 years the observations are consistent with a greater degree of greenhouse warming and sulfate cooling than previously thought and the early century warming is consistent with a greatly enhanced model response to solar changes with very little contribution from anthropogenic causes. The model-simulated response to solar forcing is, however, relatively weak and subject to large uncertainties. Contributions of both anthropogenic and natural forcings to the early century warming are therefore very poorly constrained.

References

  1. Allen MR, Stott PA (2003) Estimating signal amplitudes in optimal finger-printing. part I: theory. Clim Dyn DOI 10.1007/s00382-003-0313-9Google Scholar
  2. Allen MR, Tett SFB (1999) Checking for model consistency in optimal fingerprinting. Clim Dyn 15: 419–434CrossRefGoogle Scholar
  3. Allen MR, Stott PA, Mitchell JFB, Schnur R, Delworth TL (2000) Uncertainty in forecasts of anthropogenic climate change. Nature 407: 617–620PubMedGoogle Scholar
  4. Hasselmann K (1993) Optimal fingerprints for the detection of time-dependent climate change. J Clim 6: 1957–1971CrossRefGoogle Scholar
  5. Hasselmann K (1997) Multi-pattern fingerprint method for detection and attribution of climate change. Clim Dyn 13: 601–611CrossRefGoogle Scholar
  6. Hoyt DV, Schatten KH (1993) A discussion of plausible solar irradiance variations, 1700–1992. J Geophys Res 98: 18,895–18,906Google Scholar
  7. Johns TC, Carnell RE, Crossley JF, Gregory JM, Mitchell JFB, Senior CA, Tett SFB, Wood RA (1997) The second Hadley Centre coupled ocean–atmosphere GCM: model description, spinup and validation. Clim Dyn 13: 103–134CrossRefGoogle Scholar
  8. Langner J, Rodhe H (1991) A global three-dimensional model of the tropo-spheric sulfur cycle. J Atmos Chem 13: 225–263Google Scholar
  9. Mardia KV, Kent JT, Bibby JM (1979) Multivariate analysis. Academic Press, New York, USAGoogle Scholar
  10. Mitchell JFB, Johns TC (1997) On modification of global warming by sulfatc aerosols. J Clim 10: 245–267CrossRefGoogle Scholar
  11. Mitchell JFB, Johns TC, Gregory JM, Tett SFB (1995) Climate response to increasing levels of greenhouse gases and sulphate aerosols. Nature 376: 501–504Google Scholar
  12. 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
  13. North GR, Kim KY (1995) Detection of forced climate Signals. Part II: simulation results. J C1im 8: 409–417CrossRefGoogle Scholar
  14. North GR, Kim KY, Shen SSP, Hardin JW (1995) Detection of force Climate signals. Part I: filter theory. J Clim 8: 401–408CrossRefGoogle Scholar
  15. Parker DE, Jones PD, Folland CK, Bevan A (1994) Interdecadal chang surface temperature since the late nineteenth century. J Geophys Res 99: 14,373–14,399Google Scholar
  16. Ripley BD, Thompson M (1987) Regression techniques for the detection of an analytical bias. Analyst 112: 377–383Google Scholar
  17. Sato M, Hansen JE, McCormick MP, Pollacl JB (1993) Stratospheric aersol optical depths (1850–1990). J Geophys Res 98: 22,987—22,994Google Scholar
  18. Senior CA, Mitchell JFB (2000) The time dependence of climate sensitivity. Geophys Res Lett 27: 2686–2688Google Scholar
  19. Stott PA, Tett SFB (1998) Scale-dependent detection of climate change. J Clim 11: 3282–3294CrossRefGoogle Scholar
  20. Stott PA, Tett SFB, Jones GS, Allen MR, Ingram WJ, Mitchell JFB (2001) Attribution of twentieth century temperature change to natural and anthropogenic causes. Clim Dyn 17: 1–21Google Scholar
  21. Tett SFB, Stott PA, Allen MR, Ingram WJ, Mitchell JFB (1999) Causes of twentieth century temperature change near the earth's surface. Nature 399: 569–572Google Scholar
  22. Trenberth KE, Hoar TJ (1996) The 1990–1995 EI Nino-Southern Oscillation event: longest on record. Geophys Res Lett 23: 57–60Google Scholar
  23. Willson RC (1997) Total solar irradiance trend during solar cycles 21 and 22. Science 277: 1963–1965Google Scholar

Copyright information

© Springer-Verlag 2003

Authors and Affiliations

  1. 1.Hadley Centre for Climate Prediction and Research, Meteorological Office, Bracknell RG12 2SY, Berks, UK
  2. 2.Rutherford Appleton Laboratory

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