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For T4, adjusted confidence intervals are much smaller in observations than in the four ALL realizations (Fig. 2A), primarily due to PCM's overestimate of the stratospheric temperature responses to El Chichón and Pinatubo (Fig. 1A). Note that ensemble averaging reduces internally generated noise and increases the autocorrelation of trend residuals. This is why adjusted confidence intervals in the "ALL MEAN" case are not smaller than in individual ALL realizations (Fig. 2A).
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The precise timing of El Niño and La Niña events (and hence of their effects on tropospheric temperatures) is not the same in the PCM simulations and in the real world. Similar timing of modeled and observed El Niño variability occurs in integrations with an atmospheric general circulation model (GCM) driven by observed changes in SST (21). Such similarities do not occur (except by chance) in a coupled atmosphereocean GCM like PCM.
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The fact that the PCM T2 results fall within the UAH and RSS range is unlikely to be due to compensating errors in PCM, such as excessive stratospheric cooling offsetting unrealistically large upper tropospheric warming. PCM's stratospheric cooling is smaller than in either UAH or RSS, which rules out error compensation (at least at the global mean level) as a plausible explanation for model-data trend correspondence.
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The PCM climate change experiments available to us did not consider indirect aerosol forcing. However, we did have access to an ECHAM experiment that incorporated the indirect effects of anthropogenic sulfate aerosols on cloud albedo (26). Like the PCM All experiment, the ECHAM integration (GSDIO) applied changes in well-mixed greenhouse gases, sulfate aerosol direct effects, and tropospheric ozone. GSDIO differed from ALL both in its inclusion of indirect sulfate aerosol effects and in its neglect of changes in solar irradiance, volcanic aerosols, and stratospheric ozone. These differences in the ALL and GSDIO forcings (and the fact that no ECHAM experiment included all GSDIO forcings except indirect sulfate aerosols) make it difficult to isolate the influence of indirect sulfate aerosols on the detection of T4 and T2 fingerprints. Nevertheless, we note that repeating our detection analysis with GSDIO fingerprints yields results qualitatively similar to those obtained with PCM (fig. S2 and Fig. 4, respectively). This suggests that our primary conclusions may be relatively insensitive to the inclusion of indirect sullate aerosol effects. The main difference between the PCM and ECHAM results is the delayed and less robust detection of the GSDIO mean-removed T4 fingerprint, which is probably due to GSDIO's neglect of stratospheric ozone depletion. Another difference is that the GSDIO mean-removed T2 fingerprint is detected earlier and more consistently than the corresponding PCM ALL fingerprint.
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Mitchell, J.F.B.1
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0038741573
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note
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Work at Lawrence Livermore National Laboratory (LLNL) was performed under the auspices of the U.S. Department of Energy (DOE), Environmental Sciences Division, contract W-7405-ENG-48. T.M.L.W. was supported by the National Oceanic and Atmospheric Administration Office of Global Programs ("Climate Change Data and Detection") grant no. NA87GP0105, and by DOE grant no. DE-FG0298ER62601. A portion of this study was supported by the DOE Office of Biological and Environmental Research, as part of its Climate Change Prediction Program. The MSU T2 and T4 data and static MSU weighting functions were provided by J. Christy (Univ. of Alabama in Huntsville). ECHAM model data were supplied by E. Roeckner. We thank T. Barnett and an anonymous reviewer for useful comments and suggestions.
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