One way to judge the reliability of a source, is to see what it states about a topic you are knowledgeable about. I work on homogenization of station climate data and was thus interested in the question how well the IPCC report presents the scientific state-of-the-art on the uncertainties in trend estimates due to historical changes in climate monitoring practices.
Furthermore, I have asked some colleague climate science bloggers to review the IPCC report on their areas of expertise. You find these reviews of the IPCC review report at the end of the post as they come in. I have found most of these colleagues via the beautiful list with climate science bloggers of Doug McNeall.
Large-Scale Records and their UncertaintiesThe IPCC report is nicely structured. The part that deals with the quality of the land surface temperature observations is in Chapter 2 Observations: Atmosphere and Surface, Section 2.4 Changes in Temperature, Subsection 2.4.1 Land-Surface Air Temperature, Subsubsection 220.127.116.11 Large-Scale Records and their Uncertainties.
The relevant paragraph reads (my paragraph breaks for easier reading):
Particular controversy since AR4 [the last fourth IPCC report, vv] has surrounded the LSAT [land surface air temperature, vv] record over the United States, focussed upon siting quality of stations in the US Historical Climatology Network (USHCN) and implications for long-term trends. Most sites exhibit poor current siting as assessed against official WMO [World Meteorological Organisation, vv] siting guidance, and may be expected to suffer potentially large siting-induced absolute biases (Fall et al., 2011).
However, overall biases for the network since the 1980s are likely dominated by instrument type (since replacement of Stevenson screens with maximum minimum temperature systems (MMTS) in the 1980s at the majority of sites), rather than siting biases (Menne et al., 2010; Williams et al., 2012).
A new automated homogeneity assessment approach (also used in GHCNv3, Menne and Williams, 2009) was developed that has been shown to perform as well or better than other contemporary approaches (Venema et al., 2012). This homogenization procedure likely removes much of the bias related to the network-wide changes in the 1980s (Menne et al., 2010; Fall et al., 2011; Williams et al., 2012).
Williams et al. (2012) produced an ensemble of dataset realisations using perturbed settings of this procedure and concluded through assessment against plausible test cases that there existed a propensity to under-estimate adjustments. This propensity is critically dependent upon the (unknown) nature of the inhomogeneities in the raw data records.
Their homogenization increases both minimum temperature and maximum temperature centennial-timescale United States average LSAT trends. Since 1979 these adjusted data agree with a range of reanalysis products whereas the raw records do not (Fall et al., 2010; Vose et al., 2012a).
I would argue that this is a fair summary of the state of the scientific literature. That naturally does not mean that all statements are true, just that it fits to the current scientific understanding of the quality of the temperature observations over land. People claiming that there are large trend biases in the temperature observations, will need to explain what is wrong with Venema et al. (an article of mine from 2012) and especially Williams et al. (2012). Williams et al. (2012) provides strong evidence that if there is a bias in the raw observational data, homogenization can improve the trend estimate, but it will normally not remove the bias fully.
Personally, I would be very surprised if someone would find substantial trend biases in the homogenized US American temperature observations. Due to the high station density, this dataset can be investigated and homogenized very well.