3.2.2 Temperature in the Instrumental Record for Land and Oceans

3.2.2.1 Land-Surface Air Temperature

Figure 3.1 shows annual global land-surface air temperatures, relative to the period 1961 to 1990, from the improved analysis (CRU/Hadley Centre gridded land-surface air temperature version 3; CRUTEM3) of Brohan et al. (2006). The long-term variations are in general agreement with those from the operational version of the Global Historical Climatology Network (GHCN) data set (National Climatic Data Center (NCDC); Smith and Reynolds, 2005; Smith et al. 2005), and with the National Aeronautics and Space Administration’s (NASA) Goddard Institute for Space Studies (GISS; Hansen et al., 2001) and Lugina et al. (2005) analyses (Figure 3.1). Most of the differences arise from the diversity of spatial averaging techniques. The global average for CRUTEM3 is a land-area weighted sum (0.68 × NH + 0.32 × SH). For NCDC it is an area-weighted average of the grid-box anomalies where available worldwide. For GISS it is the average of the anomalies for the zones 90°N to 23.6°N, 23.6°N to 23.6°S and 23.6°S to 90°S with weightings 0.3, 0.4 and 0.3, respectively, proportional to their total areas. For Lugina et al. (2005) it is (NH + 0.866 × SH) / 1.866 because they excluded latitudes south of 60°S. As a result, the recent global trends are largest in CRUTEM3 and NCDC, which give more weight to the NH where recent trends have been greatest (Table 3.2).

Figure 3.1. Annual anomalies of global land-surface air temperature (°C), 1850 to 2005, relative to the 1961 to 1990 mean for CRUTEM3 updated from Brohan et al. (2006). The smooth curves show decadal variations (see Appendix 3.A). The black curve from CRUTEM3 is compared with those from NCDC (Smith and Reynolds, 2005; blue), GISS (Hansen et al., 2001; red) and Lugina et al. (2005; green).

Table 3.2. Linear trends in hemispheric and global land-surface air temperatures, SST (shown in table as HadSST2) and Nighttime Marine Air Temperature (NMAT; shown in table as HadMAT1). Annual averages, with estimates of uncertainties for CRU and HadSST2, were used to estimate trends. Trends with 5 to 95% confidence intervals and levels of significance (bold: <1%; italic, 1–5%) were estimated by Restricted Maximum Likelihood (REML; see Appendix 3.A), which allows for serial correlation (first order autoregression AR1) in the residuals of the data about the linear trend. The Durbin Watson D-statistic (not shown) for the residuals, after allowing for first-order serial correlation, never indicates significant positive serial correlation.

Further, small differences arise from the treatment of gaps in the data. The GISS gridding method favours isolated island and coastal sites, thereby reducing recent trends, and Lugina et al. (2005) also obtain reduced recent trends owing to their optimal interpolation method that tends to adjust anomalies towards zero where there are few observations nearby (see, e.g., Hurrell and Trenberth, 1999). The NCDC analysis, which begins in 1880, is higher than CRUTEM3 by between 0.1°C and 0.2°C in the first half of the 20th century and since the late 1990s. This is probably because its anomalies have been interpolated to be spatially complete: an earlier but very similar version (CRUTEM2v; Jones and Moberg, 2003) agreed very closely with NCDC when the global averages were calculated in the same way (Vose et al., 2005b). Differences may also arise because the numbers of stations used by CRUTEM3, NCDC and GISS differ (4,349, 7,230 and >7,200 respectively), although many of the basic station data are in common. Differences in station numbers relate principally to CRUTEM3 requiring series to have sufficient data between 1961 and 1990 to allow the calculation of anomalies (Brohan et al., 2006). Further differences may have arisen from differing homogeneity adjustments (see also Appendix 3.B.2).

Trends and low-frequency variability of large-scale surface air temperature from the ERA-40 reanalysis and from CRUTEM2v (Jones and Moberg, 2003) are in general agreement from the late 1970s onwards (Simmons et al., 2004). When ERA-40 is sub-sampled to match the Jones and Moberg coverage, correlations between monthly hemispheric- and continental-scale averages exceed 0.96, although trends in ERA-40 are then 0.03°C and 0.07°C per decade (NH and SH, respectively) lower than Jones and Moberg (2003). The ERA-40 reanalysis is more homogeneous than previous reanalyses (see Section 3.2.1 and Appendix 3.B.5.4) but is not completely independent of the Jones and Moberg data (Simmons et al., 2004). The warming trends continue to be greatest over the continents of the NH (see maps in Section 3.2.2.7, Figures 3.9 and 3.10), in line with the TAR. Issues of homogeneity of terrestrial air temperatures are discussed in Appendix 3.B.2.

Table 3.2 provides trend estimates from a number of hemispheric and global temperature databases. Warming since 1979 in CRUTEM3 has been 0.27°C per decade for the globe, but 0.33°C and 0.13°C per decade for the NH and SH, respectively. Brohan et al. (2006) and Rayner et al. (2006) (see Section 3.2.2.3) provide uncertainties for annual estimates, incorporating the effects of measurement and sampling error, and uncertainties regarding biases due to urbanisation and earlier methods of measuring SST. These factors are taken into account, although ignoring their serial correlation. In Table 3.2, the effects of persistence on error bars are accommodated using a red noise approximation, which effectively captures the main influences. For more extensive discussion see Appendix 3.A

From 1950 to 2004, the annual trends in minimum and maximum land-surface air temperature averaged over regions with data were 0.20°C per decade and 0.14°C per decade, respectively, with a trend in diurnal temperature range (DTR) of –0.07°C per decade (Vose et al., 2005a; Figure 3.2). This is consistent with the TAR where data extended from 1950 to 1993; spatial coverage is now 71% of the terrestrial surface instead of 54% in the TAR, although tropical areas are still under-represented. Prior to 1950, insufficient data are available to develop global-scale maps of maximum and minimum temperature trends. For 1979 to 2004, the corresponding linear trends for the land areas where data are available were 0.29°C per decade for both maximum and minimum temperature with no trend for DTR. Diurnal temperature range is particularly sensitive to observing techniques, and monitoring it requires adherence to GCOS monitoring principles (GCOS, 2004). A map of the trend of annual DTR over the period 1979 to 2004 (Section 3.2.2.7, Figure 3.11) is discussed later in the chapter.

Figure 3.2. Annual anomalies of maximum and minimum temperatures and DTR (°C) relative to the 1961 to 1990 mean, averaged for the 71% of global land areas where data are available for 1950 to 2004. The smooth curves show decadal variations (see Appendix 3.A). Adapted from Vose et al. (2005a).