3.2.2.3 Sea Surface Temperature and Marine Air Temperature
Most analyses of SST estimate the subsurface bulk temperature (i.e., the temperature in the uppermost few metres of the ocean), not the ocean skin temperature measured by satellites. For maximum resolution and data coverage, polar-orbiting infrared satellite data since 1981 can be used so long as the satellite ocean skin temperatures are adjusted to estimate bulk SST values through a calibration procedure (see e.g., Reynolds et al., 2002; Rayner et al., 2003, 2006; Appendix 3.B.3). But satellite SST data alone have not been used as a major resource for estimating climate change because of their strong time-varying biases which are hard to completely remove, for example, as shown in Reynolds et al. (2002) for the Pathfinder polar orbiting satellite SST data set (Kilpatrick et al., 2001). Figures 3.9 and 3.10 (Section 3.2.2.7) do, however, make use of spatial relationships based on adjusted satellite SST estimates after November 1981 to provide nearer-to-global coverage for the 1979 to 2005 period, and O’Carroll et al. (2006) have developed an analysis based on Along-Track Scanning Radiometers (ATSRs) with potential for the future. However, satellite data are unable to fill in estimates of surface temperature over or near sea ice areas.
Recent bulk SSTs estimated using ship and buoy data also have time-varying biases (e.g., Christy et al., 2001; Kent and Kaplan, 2006) that are larger than originally estimated by Folland et al. (1993), but not large enough to prejudice conclusions about recent warming (see Appendix 3.B.3). As reported in the TAR, a combined physical-empirical method (Folland and Parker, 1995) is mainly used to estimate adjustments to ship SST data obtained up to 1941 to compensate for heat losses from uninsulated (mainly canvas) or partly insulated (mainly wooden) buckets. Details are given in Appendix 3.B.3.
The SST analyses of Rayner et al. (2003) and Smith and Reynolds (2004) are interpolated to fill missing data areas. The main problem for estimating climate variations in the presence of large data gaps is underestimation of change, as most interpolation procedures tend to bias the analysis towards the modern climatologies used in these data sets (Hurrell and Trenberth, 1999). To address non-stationary aspects, Rayner et al. (2003) extracted the leading global covariance pattern, which represents long-term changes, before interpolating using reduced-space optimal interpolation (see Appendix 3.B.1); and Smith and Reynolds removed a smoothed, moving 15-year-average field before interpolating by a related technique.
Figure 3.4a shows annual and decadally smoothed anomalies of global SST from the new, uninterpolated Hadley Centre SST data set version 2 (HadSST2) analysis (Rayner et al., 2006). Figure 3.4a also shows NMAT (referred to as HadMAT: Hadley Centre Marine Air Temperature data set), which is used to avoid daytime heating of ship decks (Bottomley et al., 1990). The global averages are ocean-area weighted sums (0.44 × NH + 0.56 × SH). The HadMAT analysis includes limited optimal interpolation (Rayner et al., 2003) and was chosen because of the demonstration by Folland et al. (2003) of its skill in the sparsely observed South Pacific from the late 19th century onwards, but major gaps (e.g., the Southern Ocean) are not interpolated. Although HadMAT data have been corrected for warm biases during World War II they may still be too warm in the NH and too cool in the SH at that time (Figure 3.4c,d). However, global HadSST2 and HadMAT generally agree well, especially after the 1880s. The SST analysis in the TAR is included in Figure 3.4a. The changes in SST since the TAR are generally fairly small, though the new SST analysis is warmer around 1880 and cooler in the 1950s. The peak warmth in the early 1940s is likely to have arisen partly from closely spaced multiple El Niño events (Brönnimann et al., 2004; see also Section 3.6.2) and also due to the warm phase of the Atlantic Multi-decadal Oscillation (AMO; see Section 3.6.6). The HadMAT data generally confirm the hemispheric SST trends in the 20th century (Figure 3.4c,d and Table 3.2). Overall, the SST data should be regarded as more reliable because averaging of fewer samples is needed for SST than for HadMAT to remove synoptic weather noise. However, the changes in SST relative to NMAT since 1991 in the tropical Pacific may be partly real (Christy et al., 2001). As the atmospheric circulation changes, the relationship between SST and surface air temperature anomalies can change along with surface fluxes. Interannual variations in the heat fluxes to the atmosphere can exceed 100 W m-2 locally in individual months, but the main prolonged variations occur with the El Niño-Southern Oscillation (ENSO), where changes in the central tropical Pacific exceed ±50 W m-2 for many months during major ENSO events (Trenberth et al., 2002a).
Figure 3.4b shows three time series of changes in global SST. Neither the HadSST2 series (as in Figure 3.4a) nor the NCDC series include polar-orbiting satellite data because of possible time-varying biases that remain difficult to correct fully (Rayner et al., 2003). The Japanese series (Ishii et al., 2005; referred to as Centennial in-situ Observation-Based Estimates of SSTs (COBE-SST) from the Japan Meteorological Agency (JMA)) is also in situ except for the specification of sea ice. The warmest year globally in each SST record was 1998 (0.44°C, 0.38°C and 0.37°C above the 1961 to 1990 average for HadSST2, NCDC and COBE-SST, respectively). The five warmest years in all analyses have occurred after 1995.
Understanding of the variability and trends in different oceans is still developing, but it is already apparent that they are quite different. The Pacific is dominated by ENSO and modulated by the Pacific Decadal Oscillation (PDO), which may provide ways of moving heat from the tropical ocean to higher latitudes and out of the ocean into the atmosphere (Trenberth et al., 2002a), thereby greatly altering how trends are manifested. In the Atlantic, observations reveal the role of the AMO (Folland et al., 1999; Delworth and Mann, 2000; Enfield et al., 2001; Goldenberg et al., 2001; Section 3.6.6 and Figure 3.33). The AMO is likely to be associated with the Thermohaline Circulation (THC), which transports heat northwards, thereby moderating the tropics and warming the high latitudes. In the Indian Ocean, interannual variability is small compared with the trend. Figure 3.5 presents latitude-time sections from 1900 for SSTs (from HadSST2) for the zonal mean across each ocean, filtered to remove fluctuations of less than about six years, including the ENSO signal. In the Pacific, the long-term warming is clearly evident, but punctuated by cooler episodes centred in the tropics, and no doubt linked to the PDO. The prolonged 1939–1942 El Niño shows up as a warm interval. In the Atlantic, the warming from the 1920s to about 1940 in the NH was focussed on higher latitudes, with the SH remaining cool. This inter-hemispheric contrast is believed to be one signature of the THC (Zhang and Delworth, 2005). The subsequent relative cooling in the NH extratropics and the more recent intense warming in NH mid-latitudes was predominantly a multi-decadal variation of SST; only in the last decade is an overall warming signal clearly emerging. Therefore, the recent strong warming appears to be related in part to the AMO in addition to a global warming signal (Section 3.6.6). The cooling in the northwestern North Atlantic just south of Greenland, reported in the SAR, has now been replaced by strong warming (see also Section 3.2.2.7, Figures 3.9 and 3.10; also Figures 5.1 and 5.2 for ocean heat content). The Indian Ocean also reveals a poorly observed warm interval in the early 1940s, and further shows the fairly steady warming in recent years. The multi-decadal variability in the Atlantic has a much longer time scale than that in the Pacific, but it is noteworthy that all oceans exhibit a warm period around the early 1940s.