Indices versus Data

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

2. Many different ways to calculate indices for extremes

4. NO NO What types of extremes? • 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 observational series of phenomena with a daily time scale and typical return period < 1 year(as indicators of extremes) 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 • 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 • 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. Example: Russian heat wave, July 2010 MSU UAH In-situ NOAA ERA-Interim ECMWF • Courtesy: John Christy (top), Adrian Simmons (bottom)

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

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

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

17. Extremes Indices

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

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

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

21. Indices example De Bilt, the Netherlands

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

24. Changes in heavy falls 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

25. Changes in heavy falls 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

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

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