IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group I: The Physical Science Basis Overall Atmospheric Temperature Change

Anthropogenic influence on free atmosphere temperatures has been detected in analyses of satellite data since 1979, although this finding has been found to be sensitive to which analysis of satellite data is used. Satellite-borne MSUs, beginning in 1978, estimate the temperature of thick layers of the atmosphere. The main layers represent the lower troposphere (T2LT), the mid-troposphere (T2) and the lower stratosphere (T4) (Section Santer et al. (2003c) compare T2 and T4 temperature changes simulated by the PCM model including anthropogenic and natural forcings with the University of Alabama in Huntsville (UAH; Christy et al., 2000) and Remote Sensing Systems (RSS; Mears and Wentz, 2005) satellite data sets (Section They find that the model fingerprint of the T4 response to combined anthropogenic and natural forcing is consistently detected in both satellite data sets, whereas the T2 response is detected only in the RSS data set. However, when the global mean changes are removed, the T2 fingerprint is detected in both data sets, suggesting a common spatial pattern of response overlain by a systematic global mean difference.

Anthropogenic influence on free atmosphere temperatures has been robustly detected in a number of different studies analysing various versions of the Hadley Centre Radiosonde Temperature (HadRT2) data set (Parker et al., 1997) by means of a variety of different diagnostics and fingerprints estimated with the HadCM2 and HadCM3 models (Tett et al., 2002; Thorne et al., 2002, 2003; Jones et al., 2003). Whereas an analysis of spatial patterns of zonal mean free atmosphere temperature changes was unable to detect the response to natural forcings (Tett et al., 2002), an analysis of spatio-temporal patterns detected the influence of volcanic aerosols and (less convincingly) solar irradiance changes, in addition to detecting the effects of greenhouse gases and sulphate aerosols (Jones et al., 2003). In addition, Crooks (2004) detects a solar signal in atmospheric temperature changes as seen in the HadRT2.1s radiosonde data set when a diagnostic chosen to extract the solar signal from other signals is used. The models used in these studies have poor vertical resolution in the stratosphere and they significantly underestimate stratospheric variability, thus possibly overestimating the significance of these detected signals (Tett et al., 2002). However, a sensitivity study (Thorne et al., 2002) showed that detection of human influence on free atmosphere temperature changes does not depend on the inclusion of stratospheric temperatures. An analysis of spatial patterns of temperature change, represented by large-scale area averages at the surface, in broad atmospheric layers and in lapse rates between layers, showed robust detection of an anthropogenic influence on climate when a range of uncertainties were explored relating to the choice of fingerprints and the radiosonde and model data sets (Thorne et al., 2003). However, Thorne et al. were not able to attribute recent observed tropospheric temperature changes to any particular combination of external forcing influences because the models analysed (HadCM2 and HadCM3) overestimate free atmosphere warming as estimated by the radiosonde data sets, an effect also seen by Douglass et al. (2004) during the satellite era. However, there is evidence that radiosonde data during the satellite era are contaminated by spurious cooling trends (Sherwood et al., 2005; Randel and Wu, 2006; Section 3.4.1), and since structural uncertainty arising from the choice of techniques used to analyse radiosonde data has not yet been quantified (Thorne et al., 2005), it is difficult to assess, based on these analyses alone, whether model-data discrepancies are due to model or observational deficiencies. However further information is provided by an analysis of modelled and observed tropospheric lapse rates, discussed in Section

A different approach is to assess detectability of observed temperature changes through the depth of the atmosphere with AGCM simulations forced with observed SSTs, although the vertical profile of the atmospheric temperature change signal estimated in this way can be quite different from the same signal estimated by coupled models with the same external forcings (Hansen et al., 2002; Sun and Hansen, 2003; Santer et al., 2005). Sexton et al. (2001) find that inclusion of anthropogenic effects improves the simulation of zonally averaged upper air temperature changes from the HadRTt1.2 data set such that an anthropogenic signal is detected at the 5% significance level in patterns of seasonal mean temperature change calculated as overlapping eight-year means over the 1976 to 1994 period and expressed as anomalies relative to the 1961 to 1975 base period. In addition, analysing patterns of annual mean temperature change for individual years shows that an anthropogenic signal is also detected on interannual time scales for a number of years towards the end of the analysis period.