9.4.1.8 Remaining Uncertainties

A much larger range of forcing combinations and climate model simulations has been analysed in detection studies than was available for the TAR (Supplementary Material, Table S9.1). Detection and attribution analyses show robust evidence for an anthropogenic influence on climate. However, some forcings are still omitted by many models and uncertainties remain in the treatment of those forcings that are included by the majority of models.

Most studies omit two forcings that could have significant effects, particularly at regional scales, namely carbonaceous aerosols and land use changes. However, detection and attribution analyses based on climate simulations that include these forcings, (e.g., Stott et al., 2006b), continue to detect a significant anthropogenic influence in 20th-century temperature observations even though the near-surface patterns of response to black carbon aerosols and sulphate aerosols could be so similar at large spatial scales (although opposite in sign) that detection analyses may be unable to distinguish between them (Jones et al., 2005). Forcing from surface albedo changes due to land use change is expected to be negative globally (Sections 2.5.3, 7.3.3 and 9.3.3.3) although tropical deforestation could increase evaporation and warm the climate (Section 2.5.5), counteracting cooling from albedo change. However, the albedo-induced cooling effect is expected to be small and was not detected in observed trends in the study by Matthews et al. (2004).

For those forcings that have been included in attribution analyses, uncertainties associated with the temporal and spatial pattern of the forcing and the modelled response can affect the results. Large uncertainties associated with estimates of past solar forcing (Section 2.7.1) and omission of some chemical and dynamical response mechanisms (Gray et al., 2005) make it difficult to reliably estimate the contribution of solar forcing to warming over the 20th century. Nevertheless, as discussed above, results generally indicate that the contribution is small even if allowance is made for amplification of the response in observations, and simulations used in attribution analyses use several different estimates of solar forcing changes over the 20th century (Supplementary Material, Table S9.1). A number of different volcanic reconstructions are included in the modelling studies described in Section 9.4.1.2 (e.g., Sato et al., 1993; Andronova et al., 1999; Ammann et al., 2003; Supplementary Material, Table S9.1). Some models include volcanic effects by simply perturbing the incoming shortwave radiation at the top of the atmosphere, while others simulate explicitly the radiative effects of the aerosols in the stratosphere. In addition, some models include the indirect effects of tropospheric sulphate aerosols on clouds (e.g., Tett et al., 2002), whereas others consider only the direct radiative effect (e.g., Meehl et al., 2004). In models that include indirect effects, different treatments of the indirect effect are used, including changing the albedo of clouds according to an off-line calculation (e.g., Tett et al., 2002) and a fully interactive treatment of the effects of aerosols on clouds (e.g., Stott et al., 2006b). The overall level of consistency between attribution results derived from different models (as shown in Figure 9.9), and the ability of climate models to simulate large-scale temperature changes during the 20th century (Figures 9.5 and 9.6), indicate that such model differences are likely to have a relatively small impact on attribution results of large-scale temperature change at the surface.

There have also been methodological developments that have resulted in attribution analyses taking uncertainties more fully into account. Attribution analyses normally directly account for errors in the magnitude of the model’s pattern of response to different forcings by the inclusion of factors that scale the model responses up or down to best match observed climate changes. These scaling factors compensate for under- or overestimates of the amplitude of the model response to forcing that may result from factors such as errors in the model’s climate sensitivity, ocean heat uptake efficiency or errors in the imposed external forcing. Older analyses (e.g., Tett et al., 2002) did not take account of uncertainty due to sampling signal estimates from finite-member ensembles. This can lead to a low bias, particularly for weak forcings, in the scaling factor estimates (Appendix 9.A.1; Allen and Stott, 2003; Stott et al., 2003a). However, taking account of sampling uncertainty (as most more recent detection and attribution studies do, including those shown in Figure 9.9) makes relatively little difference to estimates of attributable warming rates, particularly those due to greenhouse gases; the largest differences occur in estimates of upper bounds for small signals, such as the response to solar forcing (Allen and Stott, 2003; Stott et al., 2003a). Studies that compare results between models and analysis techniques (e.g., Hegerl et al., 2000; Gillett et al., 2002a; Hegerl and Allen, 2002), and more recently, that use multiple models to determine fingerprints of climate change (Gillett et al., 2002c; Huntingford et al., 2006; Stott et al., 2006c; Zhang et al., 2006) find a robust detection of an anthropogenic signal in past temperature change.

A common aspect of detection analyses is that they assume the response in models to combinations of forcings to be additive. This was shown to be the case for near-surface temperatures in the PCM (Meehl et al., 2004), in the Hadley Centre Climate Model version 2 (HadCM2; Gillett et al., 2004c) and in the GFDL CM2.1 (see Table 8.1) model (Knutson et al., 2006), although none of these studies considered the indirect effects of sulphate aerosols. Sexton et al. (2003) did find some evidence for a nonlinear interaction between the effects of greenhouse gases and the indirect effect of sulphate aerosols in the atmosphere-only version of HadCM3 forced by observed SSTs; the additional effect of combining greenhouse gases and indirect aerosol effects together was much smaller than each term separately but was found to be comparable to the warming due to increasing tropospheric ozone. In addition, Meehl et al. (2003) found that additivity does not hold so well for regional responses to solar and greenhouse forcing in the PCM. Linear additivity was found to hold in the PCM model for changes in tropopause height and synthetic satellite-borne Microwave Sounding Unit (MSU) temperatures (Christy et al., 2000; Mears et al., 2003; Santer et al., 2003b).

A further source of uncertainty derives from the estimates of internal variability that are required for all detection analyses. These estimates are generally model-based because of difficulties in obtaining reliable internal variability estimates from the observational record on the spatial and temporal scales considered in detection studies. However, models would need to underestimate variability by factors of over two in their standard deviation to nullify detection of greenhouse gases in near-surface temperature data (Tett et al., 2002), which appears unlikely given the quality of agreement between models and observations at global and continental scales (Figures 9.7 and 9.8) and agreement with inferences on temperature variability from NH temperature reconstructions of the last millennium. The detection of the effects of other forcings, including aerosols, is likely to be more sensitive (e.g., an increase of 40% in the estimate of internal variability is enough to nullify detection of aerosol and natural forcings in HadCM3; Tett et al., 2002)

Few detection studies have explicitly considered the influence of observational uncertainty on near-surface temperature changes. However, Hegerl et al. (2001) show that inclusion of observational sampling uncertainty has relatively little effect on detection results and that random instrumental error has even less effect. Systematic instrumental errors, such as changes in measurement practices or urbanisation, could be more important, especially earlier in the record (Chapter 3), although these errors are calculated to be relatively small at large spatial scales. Urbanisation effects appear to have negligible effects on continental and hemispheric average temperatures (Chapter 3). Observational uncertainties are likely to be more important for surface temperature changes averaged over small regions (Section 9.4.2) and for analyses of free atmosphere temperature changes (Section 9.4.4).