Document Sample
Zwiers_ppt_Mar23_09 Powered By Docstoc
					     Assessing Human Influence on
         Changes in Extremes
                                                                                     Photo: F. Zwiers

        Francis Zwiers, Climate Research Division, Environment Canada
Acknowledgements – Slava Kharin, Seung-Ki Min , Xiaolan Wang, Xuebin Zhang, Bill Photo: F. Zwiers
• Introduction
• Some approaches
• Can climate models simulate
• What changes are projected?
• Have humans influence on extremes?
• Conclusions

                                       Photo: F. Zwiers
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)
Simple Indices

                 Photo: F. Zwiers
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
•   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
Indices of temperature “extremes”
           DJF Cold nights                                JJA Warm days
Trend in frequency Tmin below 10 percentile   Trend in frequency Tmax above 90th percentile

                                                                Alexander, Zhang, et al 2006
Extreme value theory

                       Photo: F. Zwiers
                        Photo: F. Zwiers
Extreme value theory
 • Statistical modelling of behaviour of either
    – Block maxima (eg, the annual extreme), or
    – Peaks over threshold (POT, exceedances above a high

 • 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 è Gaussian distribution
    – Similar limit theorems for extremes
       • Block maxima è Generalized Extreme Value distribution
       • Peaks above a high threshold è Generalized Pareto
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
Can climate models simulate extremes?

                                    Photo: F. Zwiers
Zonally averaged 20-yr 24-hr precipitation extremes
                Recent climate - 1981-2000
                                             Kharin et al, 2007

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

   Reanalyses   (black, grey)
   CMIP3 Models (colours)
   What changes are projected?

Photo: F. Zwiers
 Projected waiting time for late 20th century
20-yr 24-hr precipitation extremes circa 2090
             Expected waiting time for 1990 event, 2081-2100



                                                              Kharin et al, 2007

                     Increase in frequency (for N. America)
                        B1: ~66% (33% - 166%)
                        A1B: ~120% (66% - 233%)
                        A2: ~150% (80% - 300%)
 Projected change in 20-yr temperature extremes
   20-yr extreme                                          °C
annual maximum


~2090 vs ~1990


   20-yr extreme
 annual minimum
    temperature                      Kharin et al, 2007
Have humans influenced extremes?

                               Photo: F. Zwiers
Changes in background state related to extremes
•   Regional mean surface temperature
     – Global, continents, many
     – Area affected by European 2003
        heatwave (Stott et al, 2004)
     – Tropical cyclogensis regions
        (Santer et al, 2006; Gillett et al,
•   Global and regional precipitation

    distribution (Zhang et al, 2007; Min
    et al 2008)
•   Atmospheric water vapour content

                                                        ROBERT SULLIVAN/AFP/Getty Images
    (Santer et al, 2007)
•   Surface pressure distribution (Gillett
    et al, 2003, 2005; Wang et al, 2009)
Detection of human influence on extremes
•   Temperature
                                               Trend in 20-yr extreme SWH
     – Potential detectability (Hegerl                 (1955-2004)
       et al, 2004)                     cm/yr
     – In observed surface                   2      HadSLP2 hindcast
       temperature indices (Christidis
       et al, 2005; Brown et al, pers.
       comm., others)                        0
•   Precipitation
     – Potential detectability (Hegerl,
       et al, 2004; Min et al, 2009)        -2
•   Drought                             cm/yr
     – In area affected based on a         0.8    Simulated (9 models)
       global PDSI dataset (Burke et
       al, 2006)
•   Extreme wave height                      0
     – In trends of 20-yr events
       estimate used a downscaling
       approach (Wang et al, 2008)        -0.8
                                                               Wang et al, 2009
 AR4 basis for      Current status

  Formal study

  Formal study

Expert judgement          ??

                   Global precip and
Expert judgement     water vapour

  Formal study

Expert judgement   Supporting SST
                   detection results
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

                                                            Schar et al, 2004

 • 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 …

              Photo:F. Zwiers
              Photo: F. Zwiers
              Photo: F. Zwiers
•   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
Photo: F. Zwiers

                   The End

Shared By: