2.2.2.2 Sea surface temperature and ocean air temperature
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.
Figure 2.4: Tests of bias adjustments to sea surface temperature
(SST) using a climate model (Folland et al., 2001). Black line - annual
mean observed land surface air temperature (SAT) anomaly (°C) from a
1946 to 1965 average (Jones, 1994), a period before major anthropogenic
warming. Red line – annual averages of four simulations of SAT anomalies
using uncorrected SST data, 1872 to 1941, and an average of six simulations
for 1941 to 1998. Blue line – average of six simulations of SAT, forced
with SST data corrected up to 1941. |
Figure 2.5: (a) Annual anomalies of global SST (bars and solid curve)
and global night marine air temperature (NMAT, dotted curve), 1861 to 2000,
relative to 1961 to 1990 (°C) from UK Met Office analyses (NMAT updated
from Parker et al., 1995). Smoothed curves were created using a 21-point
binomial filter to give near-decadal averages. Also shown are the equivalent
SST anomalies from the SAR – dashed curve. (b) Smoothed annual global SST
(°C), 1861 to 2000, relative to 1961 to 1990, from USA National Climate
Data Centre, Quayle et al. (1999) (thin dashed line, includes satellite
data); USA National Centres for Environmental Prediction, Reynolds and Smith
(1994) and Smith et al. (1996) (thin solid line, includes satellite data,
to 1999 only), and UK Met Office (Jones et al., 2001) (thick line). (c)
UKMO SST and NMAT anomaly time-series from a 1961 to 1990 average for the
Northern Hemisphere. (d) As (c) but for the Southern Hemisphere. Both for
1861 to 2000. |
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 interpolated 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.
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 existence
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.
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).
|
Box 2.2: Adjustments and Corrections to Marine Observations.
The SST data used here comprise over 80 million observations from the
UK Main Marine Data Bank, the United States Comprehensive Ocean Atmosphere
Data Set (COADS) and recent information telecommunicated from ships and
buoys from the World Weather Watch. These observations have been carefully
checked for homogeneity and carefully corrected for the use of uninsulated
wooden and canvas buckets for collecting seawater prior to 1942. However,
corrections prior to about 1900 are less well known because of uncertainties
in the mix of wooden and canvas buckets. Nevertheless, Figure
2.4 provides good evidence that even in the 1870s, SST was little
biased relative to land-surface air temperatures globally. Since 1941,
observations mainly come from ship engine intake measurements, better
insulated buckets and, latterly, from buoys. SST anomalies (from a 1961
to 1990 average) are first averaged into 1° latitude by 1° longitude
boxes for five-day periods; the anomaly for a given observation is calculated
from a 1° box climatology that changes each day throughout the year.
The five-day 1° box anomalies are then aggregated into 5° boxes
for the whole month with outlying values rejected, and monthly average
anomalies calculated. Further adjustments are made to monthly SST anomalies
for the varying numbers of observations in each 5° box because when
observations are few, random errors tend to increase the variance of the
monthly mean. NMAT data are treated similarly and have quite similar characteristics.
However, a variance adjustment to NMAT data is not yet made. NMAT data
are also corrected for the progressive increase in the height of thermometer
screens on ships above the ocean surface, though no corrections have been
made since 1930. Because there are only about half as many NMAT as SST
data and NMAT have smaller temporal persistence, monthly NMAT anomalies
may be less representative than SST anomalies even on quite large space
scales. On longer time-scales, and over the majority of large ocean regions
in the 20th century, there is good agreement between NMAT and SST. 19th
century NMAT anomaly time-series should be viewed cautiously because of
the sparse character of the constituent observations, and regionally varying
biases, only some of which have been corrected.
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