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Photo: F. Zwiers

Assessing Human Influence on Changes in Extremes Francis Zwiers, Climate Research Division, Environment Canada Acknowledgements – Slava Kharin, Seung-Ki Min , Xiaolan Wang, Xuebin Zhang, Bill Hogg. Photo: F. Zwiers. Photo: F. Zwiers. Outline. Introduction Some approaches

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Photo: F. Zwiers

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  1. Assessing Human Influence on Changes in ExtremesFrancis Zwiers, Climate Research Division, Environment CanadaAcknowledgements – Slava Kharin, Seung-Ki Min , Xiaolan Wang, Xuebin Zhang, Bill Hogg Photo: F. Zwiers Photo: F. Zwiers

  2. Outline • Introduction • Some approaches • Can climate models simulate extremes? • What changes are projected? • Have humans influence on extremes? • Conclusions Photo: F. Zwiers

  3. What is an extreme? • Language used in climate science is not very precise • High impact (but not really extreme) • Exceedence over a relatively low threshold • e.g., 90th percentile of daily precipitation amounts • Rare events (long return period) • Unprecedented events (in the available record) • Space and time scales vary widely • Violent, small scale, short duration events (tornadoes) • Persistent, large scale, long duration events (drought)

  4. Simple Indices Photo: F. Zwiers

  5. Simple indices • Examples include • Day-count indices • eg, number of days each year above 90th percentile • Magnitude of things like warmest night of the year • Easily calculated, comparable between locations if the underlying data are well QC’d and homogenized • ETCCDI and APN have put a lot of effort into this • Peterson and Manton, BAMS, 2008 • http://cccma.seos.uvic.ca/ETCCDI/ • Can be analysed with simple trend analysis techniques and standard detection and attribution methods • Have been used to • Assess change in observed and simulated climates • Understand causes of observed changes using formal detection and attribution methods

  6. Indices of temperature “extremes” DJF Cold nights Trend in frequency Tmin below 10th percentile JJA Warm days Trend in frequency Tmax above 90th percentile Alexander, Zhang, et al 2006

  7. Extreme value theory Photo: F. Zwiers Photo: F. Zwiers

  8. Extreme value theory • Statistical modelling of behaviour of either • Block maxima (eg, the annual extreme), or • Peaks over threshold (POT, exceedances above a high threshold) • Relies on limit theorems that predict behaviour when blocks become large or threshold becomes very high • A familiar limit theorem is the Central Limit Theorem • Predicts that sample average  Gaussian distribution • Similar limit theorems for extremes • Block maxima  Generalized Extreme Value distribution • Peaks above a high threshold  Generalized Pareto Distribution

  9. Extreme value theory … • Used to estimate things like long-period return values • Eg, the magnitude of the 100-year event • Can be used to • Learn about climate model performance • Identify trends in rare events (e.g., 10- or 20-yr event) • Account for the effects of “covariates” • New research is venturing into detection and attribution • Fully generalized approach is not yet available

  10. Can climate models simulate extremes? Photo: F. Zwiers Photo: F. Zwiers

  11. Zonally averaged 20-yr 24-hr precipitation extremesRecent climate - 1981-2000 Kharin et al, 2007 Reanalyses (black, grey) CMIP3 Models (colours)

  12. Zonally averaged 20-yr 24-hr temperature extremesRecent climate - 1981-2000 Kharin et al, 2007 Reanalyses (black, grey) CMIP3 Models (colours)

  13. What changes are projected? Photo: F. Zwiers

  14. Projected waiting time for late 20th century 20-yr 24-hr precipitation extremes circa 2090 Expected waiting time for 1990 event, 2081-2100 20-years 10-years 5-years Kharin et al, 2007 Increase in frequency (for N. America) B1: ~66% (33% - 166%) A1B: ~120% (66% - 233%) A2: ~150% (80% - 300%)

  15. °C 10 8 6 4 2 1 Projected change in 20-yr temperature extremes 20-yr extreme annual maximum temperature A1B ~2090 vs ~1990 20-yr extreme annual minimum temperature Kharin et al, 2007

  16. Have humans influenced extremes? Photo: F. Zwiers

  17. Changes in background state related to extremes • Regional mean surface temperature • Global, continents, many regions • Area affected by European 2003 heatwave (Stott et al, 2004) • Tropical cyclogensis regions (Santer et al, 2006; Gillett et al, 2008) • Global and regional precipitation distribution (Zhang et al, 2007; Min et al 2008) • Atmospheric water vapour content (Santer et al, 2007) • Surface pressure distribution (Gillett et al, 2003, 2005; Wang et al, 2009) scrapetv.com ROBERT SULLIVAN/AFP/Getty Images

  18. HadSLP2 hindcast 2 0 -2 Simulated (9 models) 0.8 0 -0.8 Detection of human influence on extremes Trend in 20-yr extreme SWH (1955-2004) • Temperature • Potential detectability (Hegerl et al, 2004) • In observed surface temperature indices (Christidis et al, 2005; Brown et al, pers. comm., others) • Precipitation • Potential detectability (Hegerl, et al, 2004; Min et al, 2009) • Drought • In area affected based on a global PDSI dataset (Burke et al, 2006) • Extreme wave height • In trends of 20-yr events estimate used a downscaling approach (Wang et al, 2008) cm/yr cm/yr Wang et al, 2009

  19. Schar et al, 2004 Attributing changes in the risk of extremes … • New idea introduced during the IPCC AR4 process • Can’t attribute specific events… • ..... but might be able to attribute changes in the risk of extreme events • Approach to date has been • Detect and attribute observed change in mean state • Use a climate model to estimate change in risk of an extreme event • Stott et al (2004) estimated that human influence had more than doubled the risk of an event like the European 2003 heat wave • Would like to constrain this estimate observationally …

  20. Conclusions Photo: F. Zwiers Photo: F. Zwiers Photo: F. Zwiers

  21. Conclusions/Discussion • The evidence on human influence on extremes is beginning to emerge, albeit it slowly • Pushing into the tails reveals weaknesses in observations, models and analysis techniques • We have done / are doing the easy stuff on extremes • Indices (3D space-time optimal detection) • Trends in return values (2D optimal detection) • Bayesian decision analysis approaches • Concept of attributable risk is extremely useful • Available estimates of attributable risk are currently very limited, and not observationally constrained • Data will continue to be a limitation • Scaling issues will continue to be a concern

  22. Photo: F. Zwiers The End

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