Towards detecting and attributing changes in extremes

1. Towards detecting and attributing changes in extremes Gabi Hegerl, Collaborators: Jesse Kenyon, Bob Portmann, Susan Solomon, Francis Zwiers, Xuebin Zhang and others

2. Detection and attribution: Understanding why climate changed • Change significant change relative to internal climate variability? • Consistent with hypothesized cause and • Not consistent with alternative explanations • Anthropogenic greenhouse gas increases very likely caused most of the observed warming since mid-20th century (IPCC 2007)

3. Why? • Evaluates models: Do they provide the right explanation? Is the pattern and amplitude of model simulated change right? • Can provide probabilistic forecasts of future change (Stott and Kettleborough) • Question if observed change is large relative to past climate variability is relevant for impacts

4. IPCC WGI conclusions on climate extremes Expert assessment only

5. T extremes should be affected by shift SPM-4 Temperatures over individual continentslikely show a significant anthropogenic contribution over the past 50 years Observations All forcing natural forcing IPCC Ch9

6. T extremes are more interesting than that! Future changes: Models Hegerl et al 2004

7. Attribution results differ between Tmax and Tmin No Detection (in most cases) Detection ALL ANTHRO GHG Christidis et al 2005 Too much NAT Cooling in Cold Days & Nights

8. Tmax extremes seem to show less change – why? • Role of circulation in changing temperature extremes • Precipitation extremes

9. Observed trend in counts of unusually warm (90th %ile) daily boreal warm season (May-Oct) temperatures: Trend 1956-2005 max min

10. El Nino causes cooling pattern in SE US • But decreasing only warm extremes, not much increasing cold ones in E US Kenyon and Hegerl, J Clim in press

11. El Nino response in boreal warm season • ~ Less warm extremes, more cold except along Pacific coasts • Cold season response to North Pacific Index similar to CTI

12. But doesn’t explain the downtrend in Tx90 summer • Wrong season, little correlation: CTI E US TX90

13. Processes different in other regions China: No correlation with mean precipitation, aerosols? SE US: paper in preparation about connection to precipitation (Portmann et al.)

14. We still have data problems

15. Circulation matters - NAO Warm days cold nights NAO Influence on cold season T extremes

16. This is more interesting than just a shift (data: daily Central England Temperature, 19th and 20th century) Days in NAO- winters Days in NAO+ winters

17. NAO influence on heavy pcp Extreme rainfall (rainfall on 5 wettests day/yr) is significantly different in high NAO and low NAO winters if plotted in colour (Kenyon, in prep) • See also Scaife et al. NAO/NAM: from Kenyon

18. Precipitation trends: noisy, scales matter

19. Conclusions • Temperature extremes are more interesting than one might expect • Temperature and precipitation distributions affected by changes in aerosols, clouds, modes of climate variability, change of this with climate change is not simulated well • Precipitation extremes even more difficult, although they should be easier to detect than spatial pattern of mean precipitation changes • Estimate of human contribution to extremes provides challenges for years to come • But we would like a better supported attributuion column in the next IPCC assessment