The analyses of SST described here all estimate the sub-surface bulk temperature,
(i.e. the temperature in the first few metres of the ocean) not the skin temperature.
Thus the Reynolds and Smith (1994) and Smith et al. (1996) data, which incorporate
polar orbiting satellite temperatures, utilise skin temperatures that have been
adjusted to estimate bulk SST values through a calibration procedure.
Many historical in situ marine data still remain to be digitised and incorporated into the database, to improve coverage and reduce the uncertainties in our estimates of marine climatic variations. A combined physical-empirical method (Folland and Parker, 1995) is used, as in the SAR, to estimate adjustments to ships’ SST data obtained up to 1941 to compensate for heat losses from uninsulated (mainly canvas) or partly-insulated (mainly wooden) buckets (see Box 2.2). The corrections are independent of the land-surface air temperature data. Confirmation that these spatially and temporally complex adjustments are quite realistic globally is emerging from simulations of the Jones (1994) land-surface air temperature anomalies using the Hadley Centre atmospheric climate model HadAM3 forced with observed SST and sea-ice extents since 1871, updated from Rayner et al. (1996). Figure 2.4 (Folland et al., 2001) shows simulations of global land-surface air temperature anomalies in model runs forced with SST, with and without bias adjustments to the SST data before 1942. All runs with uncorrected SST (only the average is shown) give too cold a simulation of land-surface air temperature for much of the period before 1941 relative to the 1946 to 1965 base period, with a dramatic increase in 1942. All six individual runs with bias-adjusted SST (only the average is shown) give simulated land air temperatures close to those observed so that internal model variability is small on decadal time-scales compared to the signal being sought. These global results are mostly confirmed by ten similar large regional land-surface air temperature analyses (not shown). Hanawa et al. (2000) have provided independent confirmation of the SST bias corrections around Japan. Therefore, our confidence in the SST data sets has increased. Marine data issues are discussed further in Box 2.2, in Trenberth et al. (1992) and Folland et al. (1993).
Figure 2.5a shows annual values of global SST,
using a recently improved UKMO analysis that does not fill regions of missing
data (Jones et al., 2001), together with decadally smoothed values of SST from
the same analysis. NMAT is also shown. These generally agree well after 1900,
but NMAT data are warmer before that time with a slow cooling trend from 1860
not seen in the SSTs, though the minimum around 1910 is seen in both series.
The SST analysis from the SAR is also shown. The changes in SST since the SAR
are generally fairly small, though the peak warmth in the early 1940s is more
evident in the more recent analysis, supported by the NMAT analysis. A contribution
to decadally averaged global warmth at that time is likely to have arisen from
closely spaced multiple El Niño events centred near 1939 to 1941 and
perhaps 1942 to 1944 (Bigg and Inonue, 1992; and Figure
2.29). The NMAT data largely avoid daytime heating of ships’ decks
(Bottomley et al., 1990; Folland and Parker, 1995). Although NMAT data have
been corrected for warm biases in World War II they may still be too warm in
the Northern Hemisphere at that time (Figure 2.5c),
though there is good agreement in the Southern Hemisphere (Figure
2.5d). The NMAT analysis is based on that in Parker et al. (1995) but differs
from that used in the SAR in that it incorporates optimal inter-polated data
using orthogonal spatial patterns (eigenvectors). This is similar to the technique
described by Kaplan et al. (1997, 1998) but with additional allowance for non-stationarity
of the data (Parker et al, 1995). Great care is needed in making these reconstructions
in a changing climate, as pointed out by Hurrell and Trenberth (1999). This
NMAT analysis has been chosen because of the often very sparse data. NMAT confirms
the SST trends in the 20th century until 1991 (see also Table
2.1). After 1991, NMAT warmed at a slower rate than SST in parts of the
Southern Hemisphere, notably the South Indian and the tropical South Pacific
Oceans. Overall, however, the SST data should be regarded as more reliable,
though the relative changes in NMAT since 1991 may be partly real (Christy et
al., 2001). The similar trends in SST and island air temperature found by Folland
et al. (1997) for four regions of the tropical and extra-tropical South Pacific
over much of the last century support the generally greater reliability of the
SST data.
Figure 2.5b shows three time-series of changes
in global SST. The UKMO series (as in Figure 2.5a)
does not include polar orbiting satellite data because of possible time-varying
biases in them that remain difficult to correct fully (Reynolds, 1993) though
the NCEP (National Centers for Environmental Prediction) data (adapted from
Smith et al., 1996 and Reynolds and Smith, 1994), starting in 1950, do include
satellite data after 1981.
| Table 2.2: As Table 2.1 but for annual optimally averaged combined CRU land-surface air temperature anomalies and UKMO sea surface temperature anomalies (CRU LSAT + UKMO SST). All of the estimates of trends and errors in the table account for uncertainties in the annual anomalies due to data gaps, urbanisation over land, and bias corrections to SST. | |||||
 |
|||||
|
1861 to 2000
|
1901 to 2000
|
1910 to 1945
|
1946 to 1975
|
1976 to 2000
|
|
|
|||||
| Northern Hemisphere CRU LSAT + UKMOSST (Folland et al., 2001) |
0.05
(0.02) 1% |
0.06
(0.02) 1% |
0.17
(0.03) 1% |
-005
(0.05) |
0.24
(0.07) 1% |
|
|||||
| Southern Hemisphere CRU LSAT + UKMOSST (Folland et al., 2001) |
0.04
(0.01) 1% |
0.05
(0.02) 1% |
0.09
(0.05) 1% |
0.03
(0.07) |
0.11
(0.05) 1% |
|
|||||
| Global CRU LSAT + UKMOSST (Folland et al., 2001) |
0.04
(0.01) 1% |
0.06
(0.02) 1% |
0.14
(0.04) 1% |
-0.01
(0.04) |
0.17
(0.05) 1% |
The NCDC series (updated from Quayle et al., 1999) starts in 1880 and includes satellite data to provide nearly complete global coverage. Up to 1981, the Quayle et al series is based on the UKMO series, adjusted by linear regression to match the NCEP series after 1981. It has a truly global coverage based on the optimally interpolated Reynolds and Smith data. The Kaplan et al. (1998) global analysis is not shown because it makes no allowance for non-stationarity (here the existance of global warming) in its optimum interpolation procedures, as noted by Hurrell and Trenberth (1999). The warmest year globally in each SST record was 1998 (UKMO, 0.43°C, NCDC, 0.39°C, and NCEP, 0.34°C, above the 1961 to 1990 average). The latter two analyses are in principle affected by artificially reduced trends in the satellite data (Hurrell and Trenberth, 1999), though the data we show include recent attempts to reduce this. The global SST show mostly similar trends to those of the land-surface air temperature until 1976, but the trend since 1976 is markedly less (Table 2.1). NMAT trends are not calculated from 1861, as they are too unreliable. The difference in trend between global SST and global land air temperature since 1976 does not appear to be significant, but the trend in NMAT (despite any residual data problems) does appear to be less than that in the land air temperature since 1976. Figures 2.5c and d show that NMAT and SST trends remain very similar in the Northern Hemisphere to the end of the record, but diverge rather suddenly in the Southern Hemisphere from about 1991, as mentioned above. The five warmest years in each of the UKMO, NCDC and NCEP SST analyses have occurred after 1986, four of them in the 1990s in the UKMO analysis.
|
Figure 2.8: Smoothed annual anomalies of global combined land-surface air and sea surface temperatures (°C), 1861 to 2000, relative to 1961 to 1990, and twice their standard errors. The smoothed curves and shaded areas were created using a 21-point binomial filter giving near-decadal averages, with appropriate errors. Optimally averaged anomalies and uncertainties (Folland et al., 2001) – solid curve and dark shading; standard area weighted anomalies and uncertainties (adapted from Jones et al., 1997b, 2001) – dashed curve and light shading. Note that uncertainties decrease after 1941 due to the cessation of uncertainties due to bias corrections in sea surface temperature. On the other hand, uncertainties due to urbanisation of the land component, assessed as zero in 1900, continue to increase after 1941 to a maximum in 2000. |
Particularly strong warming has occurred in the extra-tropical North Atlantic since the mid-1980s (approximately 35° to 65°N, 0° to 35°W not shown). This warming appears to be related in part to the warming phase of a multi-decadal fluctuation (Folland et al., 1986, 1999a; Delworth and Mann, 2000; see Section 2.6), perhaps not confined to the North Atlantic (Minobe, 1997; Chao et al., 2000), though global warming is likely to be contributing too. In addition, the cooling in the north-western North Atlantic south of Greenland, reported in the SAR, has ceased. These features were noted by Hansen et al. (1999).
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