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

  • P. A. StottEmail author
  • M. R. Allen
  • G. S. Jones


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.


Ordinary Little Square Ordinary Little Square Regression Sulfate Aerosol Total Little Square Volcanic Signal 
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.



Peter Stott and Gareth Jones were funded by the UK Department of the Environment, Transport and the Regions (DETR) under contract PECD 7/12/37 and Myles Allen was supported by an NERC Advanced Research Fellowship with additional support fom the DETR, the European Commission QUARCC project ENV4-CT97-0501 and the NOAA/DoE Ad Hoc Detection Group.


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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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