1. Indices versus Data Indices are information derived from data Indices are information derived from data Proxy for data Proxy for data More readily released than data More readily released than data Indices generally not of economic value – only research value and for applicationsIndices generally not of economic value – only research value and for applications More on this laterMore on this later Are not reproducible without the data Are not reproducible without the data A key component of scienceA key component of science

2. Many different ways to calculate indices for extremes

4. What types of extremes? Trends in extreme events characterised by the size of their societal or economic impacts Trends in extreme events characterised by the size of their societal or economic impacts Trends in “very rare” extreme events analysed by the parameters of extreme value distributions Trends in “very rare” extreme events analysed by the parameters of extreme value distributions Trends in observational series of phenomena with a daily time scale and typical return period < 1 year (as indicators of extremes) Trends in observational series of phenomena with a daily time scale and typical return period < 1 year (as indicators of extremes) NO YES

5. Motivation for choice of “extremes” The detection probability of trends depends on the return period of the extreme event and the length of the observational series The detection probability of trends depends on the return period of the extreme event and the length of the observational series For extremes in daily series with typical length ~50 yrs, the optimal return period is 10-30 days rather than 10-30 years For extremes in daily series with typical length ~50 yrs, the optimal return period is 10-30 days rather than 10-30 years

6. Approach Focus on counts of days crossing a threshold; either absolute/fixed thresholds or percentile/variable thresholds relative to local climate Focus on counts of days crossing a threshold; either absolute/fixed thresholds or percentile/variable thresholds relative to local climate Standardisation enables comparisons between results obtained in different countries, and even different parts of the world Standardisation enables comparisons between results obtained in different countries, and even different parts of the world

8. Expert Team on Climate Change Detection and Indices (ETCCDI) started in 1999 jointly sponsored by CCl, CLIVAR and JCOMM

9. the ETCCDI developed an internationally coordinated set of 27 climate indices focus on counts of days crossing a threshold; either absolute/fixed thresholds or percentile/variable thresholds relative to local climate used for both observations and models, globally as well as regionally can be coupled with – simple trend analysis techniques – standard detection and attribution methods complements the analysis of more rare extremes using EVT

10. Klein Tank, Zwiers and Zhang, 2009, WCDMP-No. 72, WMO-TD No. 1500, 56pp.

11. Example: Russian heat wave, July 2010 In-situ NOAA

12. ERA-Interim ECMWF MSU UAH Courtesy: John Christy (top), Adrian Simmons (bottom) In-situ NOAA Example: Russian heat wave, July 2010

13. ETCCDI indices add relevant information In-situ NOAA Example: Russian heat wave, July 2010

14. 31 days with T-max > 25°C against 9.5 days in a normal July Example: Russian heat wave, July 2010

15. 16 nights with T-min > 20°C against 0.5 night in a normal July Example: Russian heat wave, July 2010

17. Extremes Indices

18. upper 10-ptile 1961-1990 the year 1996 lower 10-ptile 1961-1990 Indices example

19. upper 10-ptile 1961-1990 the year 1996 lower 10-ptile 1961-1990 “cold nights” Indices example

20. upper 10-ptile 1961-1990 the year 1996 lower 10-ptile 1961-1990 “warm nights” “cold nights” Indices example

21. De Bilt, the Netherlands Indices example

23. 1) Identify heavy falls using a site specific threshold = 95th percentile at wet days in the 1961-90 period Changes in heavy falls

24. 1) Identify heavy falls using a site specific threshold = 95th percentile at wet days in the 1961-90 period 2) Determine fraction of total precipitation in each year that is due to these days Changes in heavy falls

25. 1) Identify heavy falls using a site specific threshold = 95th percentile at wet days in the 1961-90 period 2) Determine fraction of total precipitation in each year that is due to these days 3) Trend analysis in series of fractions Changes in heavy falls

26. Alexander et al.,2006; in IPCC-AR4 Changes in heavy falls

27. Extremes table IPCC-AR4, WG1 report (IPCC, 2007)