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Analyzing Extreme Climate Events for Natural Hazard Identification

Study identifying natural hazards in climate databases, focusing on extreme events and trends using observational data and indices. Analysis includes temperature, precipitation, and societal impacts for improved risk assessment.

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Analyzing Extreme Climate Events for Natural Hazard Identification

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  1. Identifying natural hazardsin climate databases Albert Klein Tank KNMI, the Netherlands 19 September 2002 acknowledgements:Lisa Alexander (Met Office, UK) Janet Wijngaard, Aryan van Engelen & Günther Können (KNMI) 36 ECA-participants (Europe & Middle East)

  2. European Study: http://www.knmi.nl /samenw/eca

  3. River Rhine flooding 1995; precipitation data GPCC (GPCC, 1995)

  4. Recent Elbe flooding 2002; precipitation data (GPCC, 1995)

  5. NO NO Studying trends: 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 using indices of extremes YES

  6. Motivation for choice of “extremes” • The statistical 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 of e.g. temperature and precipitation having typical length ~50 yrs, the optimal return period is 10-30 days rather than 10-30 years

  7. example: 80% detection prob. (5%-level) T (see also: Frei & Schär, J.Climate, 2001) N

  8. Approach • Using derived climate indices as proposed by the joint CCL/CLIVAR Working Group on Climate Change Detection (Peterson et al., WMO-TD No. 1071, 2001) • 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 parts of the world(e.g. Frich et al., Clim. Res. 2002; also in IPCC-TAR)

  9. Temperature indices I • Example of “frost days” as an extreme index based on an absolute temperature threshold

  10. Frich et al. (Clim.Res., 2002) in IPCC-TAR

  11. Temperature indices II • Example of winter “warm spells” and “cold spells” as an extreme index based on counts of events over a (seasonally varying) percentile threshold

  12. IPCC-TAR (Ch.2, Folland and Karl)

  13. Precipitation indices I • Example of “R95%tot” extreme index for the precipitation fraction due to very wet days

  14. Easterling et al. (BAMS, 2000) in IPCC-TAR see also Groisman et al. (Clim.Change, 1999) Linear trends in rainy season over ~50 years

  15. Frich et al. (Clim.Res., 2002) in IPCC-TAR

  16. the end...

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