9.4.3.2 Global Assessments

Evidence for observed changes in short-duration extremes generally depends on the region considered and the analysis method (IPCC, 2001). Global analyses have been restricted by the limited availability of quality-controlled and homogenised daily station data. Indices of temperature extremes have been calculated from station data, including some indices from regions where daily station data are not released (Frich et al., 2002; Klein Tank and Können, 2003; Alexander et al., 2006). Kiktev et al. (2003) analyse a subset of such indices by using fingerprints from atmospheric model simulations driven by prescribed SSTs. They find significant decreases in the number of frost days and increases in the number of very warm nights over much of the NH. Comparisons of observed and modelled trend estimates show that inclusion of anthropogenic effects in the model integrations improves the simulation of these changing temperature extremes, indicating that human influences are probably an important contributor to changes in the number of frost days and warm nights. Tebaldi et al. (2006) find that changes simulated by eight MMD models agreed well with observed trends in heat waves, warm nights and frost days over the last four decades.

Christidis et al. (2005) analyse a new gridded data set of daily temperature data (Caesar et al., 2006) using the indices shown by Hegerl et al. (2004) to have a potential for attribution, namely the average temperature of the most extreme 1, 5, 10 and 30 days of the year. Christidis et al. (2005) detect robust anthropogenic changes in indices of extremely warm nights using signals estimated with the HadCM3 model, although with some indications that the model overestimates the observed warming of warm nights. They also detect human influence on cold days and nights, but in this case the model underestimates the observed changes, significantly so in the case of the coldest day of the year. Anthropogenic influence was not detected in observed changes in extremely warm days.