IPCC_Managing Risks of Extreme Events.pdf - Climate Access

Changes in Climate Extremes and their Impacts on the Natural Physical EnvironmentChapter 3Wet Day IntensityPercentage Days with Pr>Q95Fraction of Days with Pr>10mmJJADJFANN−1.2 −0.4 0 0.4 1.2Standard Deviation−2 −1 0 1 2Percentage of Days−1.2 −0.6 0 0.6 1.2Standard DeviationFigure 3-6 | Projected annual and seasonal changes in three indices for daily precipitation (Pr) for 2081-2100 with respect to 1980-1999, based on 17 GCMs contributing to theCMIP3. Left column: wet-day intensity; middle column: percentage of days with precipitation above the 95% quantile of daily wet day precipitation for that day of the year,calculated from the 1961-1990 reference period; right column: fraction of days with precipitation higher than 10 mm. The changes are computed for the annual time scale (top row)and two seasons (DJF, middle row, and JJA, bottom row) as the fractions/percentages in the 2081-2100 period (based on simulations under emission scenario SRES A2) minus thefractions/percentages of the 1980-1999 period (from corresponding simulations for the 20th century). Changes in wet-day intensity and in the fraction of days with Pr >10 mmare expressed in units of standard deviations, derived from detrended per year annual or seasonal estimates, respectively, from the three 20-year periods 1980-1999, 2046-2065,and 2081-2100 pooled together. Changes in percentages of days with precipitation above the 95% quantile are given directly as differences in percentage points. Color shading isonly applied for areas where at least 66% (i.e., 12 out of 17) of the GCMs agree on the sign of the change; stippling is applied for regions where at least 90% (i.e., 16 out of 17)of the GCMs agree on the sign of the change. Adapted from Orlowsky and Seneviratne (2011); updating Tebaldi et al. (2006) for additional number of indices and CMIP3 models,and including seasonal time frames. For more details, see Appendix 3.A.regions where the Clausius-Clapeyron constraint is not closely followed,it still appears to be a better predictor for future changes in extremeprecipitation than the change in mean precipitation in climate modelprojections (Pall et al., 2007). An observational study seems also to supportthis thermodynamic theory. Analysis of daily precipitation from theSpecial Sensor Microwave Imager over the tropical oceans shows adirect link between rainfall extremes and temperature: heavy rainfallevents increase during warm periods (El Niño) and decrease during coldperiods (Allan and Soden, 2008). However, the observed amplificationof rainfall extremes is larger than that predicted by climate models(Allan and Soden, 2008), due possibly to widely varying changes inupward velocities associated with precipitation extremes (O’Gormanand Schneider, 2008). Evidence from measurements in the Netherlandssuggests that hourly precipitation extremes may in some cases increase14% per degree of warming, which is twice as fast as what would beexpected from the Clausius-Clapeyron relationship alone (Lenderinkand Van Meijgaard, 2008), though this is still under debate (Haerter andBerg, 2009; Lenderink and van Meijgaard, 2009). A comparison betweenobserved and multi-model simulated extreme precipitation using anoptimal detection method suggests that the human-induced increase ingreenhouse gases has contributed to the observed intensification ofheavy precipitation events over large Northern Hemisphere land areasduring the latter half of the 20th century (Min et al., 2011). Pall et al.(2011) linked human influence on global warming patterns with anincreased risk of England and Wales flooding in autumn (September-November) 2000 that is associated with a displacement in the NorthAtlantic jet stream. The present assessment based on evidence from newstudies and those used in the AR4 is that there is medium confidencethat anthropogenic influence has contributed to changes in extremeprecipitation at the global scale. However, this conclusion may bedependent on the season and spatial scale. For example, there is nowabout a 50% chance that an anthropogenic influence can be detectedin UK extreme precipitation in winter, but the likelihood of the detectionin other seasons is very small (Fowler and Wilby, 2010).Projected Changes and UncertaintiesRegarding projected changes in extreme precipitation, the AR4 concludedthat it was very likely that heavy precipitation events, that is, thefrequency of heavy precipitation or proportion of total precipitationfrom heavy precipitation, would increase over most areas of the globe144