Figure 4.5. Top: European summer temperatures for 1500–2010. Statistical frequency distribution of best-guess reconstructed and instrument-based European ([35°N, 70°N], [25°W, 40°E]) summer land temperature anomalies (degrees Celsius, relative to the 1970–1999 period) for the 1500–2010 period (vertical lines). The five warmest and coldest summers are highlighted. Gray bars represent the distribution for the 1500–2002 period, with a Gaussian fit in black. Data for the 2003–2010 period are from Hansen et al. (1999). Bottom left: The running decadal frequency of extreme summers, defined as those with temperature above the 95th percentile of the 1500–2002 distribution. A 10year smoothing is applied. Dotted line shows the 95th percentile of the distribution of maximum decadal values that would be expected by random chance. Lower right: The difference between the percentage of European areas with summer maxima abovethe given temperature (in SDs) for the 1500–2000 (dashed line) and 1500–2010 (dotted line) periods. Source: Barriopedro et al. (2011).
Stott et al. (2004) pointed out that the question of “whether the 2003 heatwave was caused, in a simple deterministic sense, by a modification of the external influences on climate—for example, increasing concentrations of greenhouse gases in the atmosphere” is ill-posed, “because almost any such weather event might have occurred by chance in an unmodified climate. However, it is possible to estimate by how much human activities may have increased the risk of the occurrence of such a heatwave.” The authors did this by conducting two types of GCM simulations over central and southern Europe; the attribution method is described in more detail below in Sec. 4.6 for the case of flood risk.
The most notable outcome of the Stott et al. study was that extremely hot summers (defined as those having a SAT in the 1990-99 period > 1.6°C above the 1961-90 summer mean), which were 1-in-1000 year events in simulations with natural (solar and volcanic) forcing, only became 1-in250 year events in the simulations including anthropogenic (increasing greenhouse gas, aerosol, and ozone) forcing. Equivalently, extreme summer temperatures were always larger in the latter simulations for a given occurrence frequency (return period). On the basis of further analysis, the authors concluded: “… there is a greater than 90% chance that over half the risk of European 62
Determining the Impact of Climate Change on Insurance Risk and the Global Community