Climate Change 2001:
The Scientific Basis
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2.2 How Much is the World Warming?

2.2.1 Background

The SAR concluded that, on a global average, land-surface air and sea surface temperature rose by between 0.3°C and 0.6°C between the late 19th century and 1994. In this section, the recent warming is re-examined, using updated data. We include recent analyses of the diurnal asymmetry of the warming and its geographical structure. Conventional temperature observations are supplemented by indirect evidence and by satellite-based data. For the first time, we make objective estimates of uncertainties in the surface temperature data, though these are preliminary. We also assess recent work in compiling hemispheric and global temperature records from palaeoclimatic data, especially for the most recent millennium.

2.2.2 Temperature in the Instrumental Record for Land and Oceans

Note that all data sets are adjusted to have zero anomaly when averaged over the period 1961 to 1990.

2.2.2.1 Land-surface air temperature

The SAR reviewed the three databases of land-surface air temperature due to Jones (1994), Hansen and Lebedeff (1988) and Vinnikov et al. (1990). The first and second databases have been updated by Jones et al. (2001) and Hansen et al. (1999), respectively, and a further analysis has become available (Peterson and Vose, 1997; Peterson et al., 1998a, 1999). The last paper also separates rural temperature stations in the Global Historical Climatology Network (GHCN) (Peterson and Vose, 1997) from the full set of stations which, in common with the other three analyses, have been screened for urbanisation effects. While there is little difference in the long-term (1880 to 1998) rural (0.70°C/century) and full set of station temperature trends (actually less at 0.65°C/century), more recent data (1951 to 1989), as cited in Peterson et al. (1999), do suggest a slight divergence in the rural (0.80°C/century) and full set of station trends (0.92°C/century). However, neither pair of differences is statistically significant. In addition, while not reported in Peterson et al., the 1951 to 1989 trend for urban stations alone was 0.10°C/decade. We conclude that estimates of long-term (1880 to 1998) global land-surface air temperature variations and trends are relatively little affected by whether the station distribution typically used by the four global analyses is used, or whether a special effort is made to concentrate on rural stations using elaborate criteria to identify them. Part of the reason for this lack of sensitivity is that the average trends in available worldwide urban stations for 1951 to 1989 are not greatly more than those for all land stations (0.09°C/decade). The differences in trend between rural and all stations are also virtually unaffected by elimination of areas of largest temperature change, like Siberia, because such areas are well represented in both sets of stations.

These results confirm the conclusions of Jones et al. (1990) and Easterling et al. (1997) that urban effects on 20th century globally and hemispherically averaged land air temperature time-series do not exceed about 0.05°C over the period 1900 to 1990 (assumed here to represent one standard error in the assessed non-urban trends). However, greater urbanisation influences in future cannot be discounted. Note that changes in borehole temperatures (Section 2.3.2), the recession of the glaciers (Section 2.2.5.4), and changes in marine temperature (Section 2.2.2.2), which are not subject to urbanisation, agree well with the instrumental estimates of surface warming over the last century. Reviews of the homogeneity and construction of current surface air temperature databases appear in Peterson et al. (1998b) and Jones et al. (1999a). The latter shows that global temperature anomalies can be converted into absolute temperature values with only a small extra uncertainty.

Figure 2.1a shows the Jones et al. (2001) CRU (Climatic Research Unit) annual averages, together with an approximately decadally smoothed curve, to highlight decadal and longer changes. This is compared with smoothed curves from the other three analyses in Figure 2.1b. We do not show standard errors for the CRU land data using the Jones et al. (1997b) method as tests suggest that these may not be reliable for land data on its own. Instead we use an optimum averaging method (Folland et al., 2001) where the calculated uncertainties are centred on the simple CRU average. We have added an estimate of the additional, independent, uncertainty (twice the standard error) due to urbanisation increasing from zero in 1900 to 0.12°C in 2000. (The Jones et al. (1990) estimates can be interpreted as one standard error equal to 10% of the global warming to that time of about 0.05°C, see also Box 2.1 on urbanisation.) Note that the warming substantially exceeds the calculated uncertainties. (We have not included the possible refinement of assuming urbanisation uncertainties to apply to the cold side of the trend line only, which would reduce the total uncertainty range in Figure 2.1.)

Figure 2.1: (a) Annual anomalies of global average land-surface air temperature (°C), 1861 to 2000, relative to 1961 to 1990 values. Bars and solid curve are from CRU (Jones et al., 2001). Values are the simple average of the anomalies for the two hemispheres. The smoothed curve was created using a 21-point binomial filter giving near decadal averages. (b) As (a) but smoothed curves only from NCDC (updated from Peterson and Vose, 1997) – thin solid curve; GISS (adapted from Hansen et al., 1999) – thick dashed curve; SHI (updated from Vinnikov et al., 1990) – thin dashed curve to 1999 only; Peterson and Vose (1997) – thin solid curve. Thick solid curve – as in (a). Two standard error uncertainties are centred on the CRU curve and are estimated using an optimum averaging method (Folland et al., 2001) and include uncertainties due to urbanisation but not due to uncertainties in thermometer exposures. The NCDC curve is the weighted average of the two hemispheres according to the area sampled, which accounts for most of the differences from the CRU curve.

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