A major advance since the SAR has been the increase in the range of techniques used to assess the quantitative agreement between observed and modelled climate change, and the evaluation of the degree to which the results are independent of the assumptions made in applying those techniques (Table 12.1). Also, some studies have based their conclusions on estimates of the amplitude of anthropogenic signals in the observations and consideration of their consistency with model projections. Estimates of the changes in forcing up to 1990 used in these studies, where available, are given in Table 12.2. In this section 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.

Table 12.1: Summary of the main detection and attribution studies considered.
Study	Signals	Signal source	Noise source	Method	S, V	Sources of uncertainty	Time-scale	No. of patterns	Detect
Santer et al., 1996	G, GS, O etc.	Equilibrium / future LLNL, GFDL R15, HadCM2	GFDL R15, HadCM2, ECHAM1	F, Corr	V	Internal variability	25 year Annual and seasonal	1	GSO
Hegerl, 1996, 1997	G, GS	Future ECHAM3, HadCM2	GFDL R15, ECHAM1, HadCM2; observation	F, Pattern	S	Internal variability	30, 50 years Annual and JJA	1, 2	G, GS, S
Tett et al., 1996	G, GS, GSO	Historical HadCM2	HadCM2	F, Corr	V	Internal variability	35 years	1	GSO
Hegerl et al., 2000	G, GS, Vol, Sol	Future, ECHAM3, HadCM2	ECHAM3, HadCM2	F, Pattern	S	Internal variability; model uncertainty	30, 50 years Annual and JJA	1, 2	GS, G, S (not all cases)
Allen and Tett, 1999	G, GS, GSO	Historical HadCM2	HadCM2	F, pattern	V	Internal variability	35 years Annual	1, 2	GSO and also G
Tett et al., 1999 Stott et al., 2001	G,GS, Sol, Vol	Historical HadCM2	HadCM2	Timespace	S	Internal variability, 2 solar signals	50 years decadal and seasonal	2 or more	G, GS, Sol (Vol)
North and Stevens, 1998 Leroy, 1998	G, GS, Sol, Vol	Historical EBM	GFDL ECHAM1, EBM	Freq-Space	S	Internal variability	Annual and hemispheric summer	4	G, S, Vol
North and Wu, 2001			Same+Had CM2	Timespace			Annual		G, Vol
Barnett et al., 1999	G, GS, GSIO Sol+vol	Future ECHAM3, ECHAM4, HadCM2, GFDL R15	ECHAM3, ECHAM4, HadCM2, GFDL R15	F, Pattern	S	Observed sampling error, model uncertainty, internal variability	50 years JJA trends	2	GS, G, S (S not all cases)
Hill et al., 2001	G, GSO,Sol	Historical HadCM2	HadCM2	F, pattern	V	Internal variability	35 years annual	3	G
Tett et al., 2000	G,GSTI, GSTIO, Nat	Historical HadCM3	HadCM3	Timespace	S	Internal variability	50, 100 years decadal	2 or more	G, SIT, GSTIO and Nat
				F, pattern	V	Internal variability	35 years, annual	2	GSTI
The columns contain the following information: Study: the main reference to the study. Signals: outlines the principal signals considered: G-greenhouse gases, S-sulphate aerosol direct effect, T-tropospheric ozone, I-sulphate aerosol indirect effect, O-stratospheric ozone, Sol-solar, Vol-volcanoes, Nat-solar and volcanoes. Signal source: “historical” indicates the signal is taken from a historical hindcast simulation, “future” indicates that the pattern is taken from a prediction. Noise source: origin of the noise estimates. Method: “F” means fixed spatial pattern, “corr” indicates a correlation study, “pattern” an optimal detection study. S, V: “V” indicates a vertical temperature pattern, “S” a horizontal temperature pattern. Sources of uncertainty: any additional uncertainties allowed for are indicated. Modelled internal variability is allowed for in all studies. Time-scale: the lengths of time interval considered. (JJA= June-July-August) No. of patterns: the number of patterns considered simultaneously. Detect: signals detected.

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.

Table 12.2: Estimated forcing from pre-industrial period to 1990 in simulations used in detection studies (Wm-2). GS indicates only direct sulphate forcing included, GSI indicates both direct and indirect effects included. Other details of the detection studies are given in Table 12.1. Details of the models are given in Chapter 8, Table 8.1.
Model	Aerosol	Baseline forcing	1990 aerosol forcing	1990 greenhouse gas forcing	Source of estimate
HadCM2	GS	1760	-0.6	1.9	Mitchell and Johns, 1997
HadCM3	GSI	1860	-1.0	2.0	Tett et al., 2000
ECHAM3/LSG	GS	1880	-0.7	1.7	Roeckner
ECHAM4/OPYC	GSI	1760	-0.9	2.2	Roeckner et al., 1999
GFDL_R30	GS	1760	-0.6	2.1	Stouffer
CGCM1,2	GS	1760	~ -1.0	~2.2	Boer et al., 2000a,b

We provide various aids to the reader to clarify the distinction between the various detection and attribution techniques that have been used. Box 12.1 in Section 12.4.3 provides a simple intuitive description of optimal detection. Appendix 12.1 provides a more technical description and relates optimal detection to general linear regression. The differences between fixed pattern, space-time and space-frequency optimal detection methods are detailed in Appendix 12.2 and the relationship between pattern correlation and optimal detection methods is discussed in Appendix 12.3. Dimension reduction, a necessary part of optimal detection studies, is discussed in Appendix 12.4.

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