It is important to stress that the attribution process is inherently open-ended, since we have no way of predicting what alternative explanations for observed climate change may be proposed, and be accepted as plausible, in the future. This problem is not unique to the climate change issue, but applies to any problem of establishing cause and effect given a limited sample of observations.Climate Change 2001 The Scientific Basis
we assess new studies using a number of techniques, ranging from descriptive analyses of simple indices to sophisticated optimal detection techniques that incorporate the time and space-dependence of signals over the 20th century.Climate Change 2001 The Scientific Basis
We begin in Section 12.4.1 with a brief discussion of detection studies that use simple indices and time-series analyses. In Section 12.4.2 we discuss recent pattern correlation studies (see Table 12.1) that assess the similarity between observed and modelled climate changes. Pattern correlation studies were discussed extensively in the SAR, although subsequently they received some criticism. We therefore also consider the criticism and studies that have evaluated the performance of pattern correlation techniques. Optimal detection studies of various kinds are assessed in Section 12.4.3. We consider first studies that use a single fixed spatial signal pattern (Section 12.4.3.1) and then studies that simul-taneously incorporate more than one fixed signal pattern (Section 12.4.3.2). Finally, optimal detection studies that take into account temporal as well as spatial variations (so-called space-time techniques) are assessed in Section 12.4.3.3.Climate Change 2001 The Scientific Basis
The detection technique that has been used in most “optimal detection” studies performed to date has several equivalent representations (Hegerl and North, 1997; Zwiers, 1999). It has recently been recognised that it can be cast as a multiple regression problem with respect to generalised least squares (Allen and Tett, 1999; see also Hasselmann, 1993, 1997)
Climate Change 2001 The Scientific Basis
Correlation structures in surface temperature
Wigley et al., (1998a). Karoly and Braganza (2001) al
Statistical models of time-series
Tol and de Vos (1998), Tol and Vellinga (1998) Zheng and Basher (1999) Walter et al. (1998),
a multiple regression model (Schönwiese et al., 1997).
Kaufmann and Stern (1997) examine the lagged-covariance structure of hemispheric mean temperature and find it consistent with unequal anthropogenic aerosol forcing in the two hemispheres.
Smith et al. (2001), using similar bivariate time-series models, find that the evidence for causality becomes weak when the effects of ENSO are taken into account. Bivariate time-series models of hemispheric mean temperature that account for box–diffusion estimates of the response to anthropogenic and solar forcing are found to fit the observations significantly better than competing statistical models. All of these studies draw conclusions that are consistent with those of earlier trend detection studies (as described in the SAR).
Results from studies using pattern correlations were reported extensively in the SAR (for example, Santer et al., 1995, 1996c; Mitchell et al., 1995b). They found that the patterns of simulated surface temperature change due to the main anthropogenic factors in recent decades are significantly closer to those observed than expected by chance. Pattern correlations have been used because they are simple and are insensitive to errors in the amplitude of the spatial pattern of response and, if centred, to the global mean response. They are also less sensitive than regression-based optimal detection techniques to sampling error in the model-simulated response. The aim of pattern-correlation studies is to use the differences in the large-scale patterns of response, or “fingerprints”, to distinguish between different causes of climate change.
Strengths and weaknesses of correlation methods
Pattern correlation statistics come in two types – centred and uncentred (see Appendix
12.3).Wigley et al. (1998b
Nevertheless, all the studies indicate that anthropogenic factors account for a significant part of recent observed changes, whereas internal and naturally forced variations alone, at least as simulated by current models, cannot explain the observed changes. In addition, there are physical arguments for attributing the changes in the vertical profile of temperature to anthropogenic influence (Section 12.3.2).Climate Change 2001 The Scientific Basis
As noted in Section 12.3.2, increases in greenhouse gases produce a distinctive change in the vertical profile of temperature. Santer et al. (1996c) assessed the significance of the observed changes in recent decades using equilibrium GCM simulations with changes in greenhouse gases, sulphate aerosols and strato-spheric ozone. This study has been extended to include results from the transient AOGCM simulations, additional sensitivity studies and estimates of internal variability from three different models (Santer et al., 1996a). Results from this study are consistent with the earlier results – the 25-year trend from 1963 to 1988 in the centred correlation statistic between the observed and simulated patterns for the full atmosphere was significantly different from the population of 25-year trends in the control simulations. The results were robust even if the estimates of noise levels were almost doubled, or the aerosol response (assumed linear and additive) was halved. The aerosol forcing leads to a smaller warming in the Northern Hemisphere than in the Southern Hemisphere.
Tett et al. (1996) refined Santer et al.’s (1996a) study by using ensembles of transient simulations which included increases in CO2, and sulphate aerosols, and reductions in stratospheric ozone, as well as using an extended record of observations (see Figure 12.8). They found that the best and most significant agreement with observations was found when all three factors were included1. Allen and Tett (1999) find that the effect of greenhouse gases can be detected with these signal patterns using optimal detection (see Appendix 12.1).
Folland et al. (1998) and Sexton et al. (2001) take a complementary approach using an atmospheric model forced with sea surface temperatures (SST) and ice extents prescribed from observations. The correlation between the observed and simulated temperature changes in the vertical relative to the base period from 1961 to 1975 was computed. The experiments with anthropogenic forcing (including some with tropospheric ozone changes), give significantly higher correlations than when only SST changes are included.
In addition, there are physical arguments for attributing the changes in the vertical profile of temperature to anthropogenic influence (Section 12.3.2).Climate Change 2001 The Scientific Basis
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