Assessing Human Influence on Changes in Extremes Francis Zwiers, Climate Research Division, Environm

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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
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Outline

• Introduction
• Some approaches
• Can climate models simulate extremes?
• What changes are projected?
• Have humans influence on extremes?
• Conclusions

Photo F. Zwiers
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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)

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Simple Indices
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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 QCd 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

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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
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Extreme value theory
Photo F. Zwiers
Photo F. Zwiers
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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

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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

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Can climate models simulate extremes?
Photo F. Zwiers
Photo F. Zwiers
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Zonally averaged 20-yr 24-hr precipitation
extremesRecent climate - 1981-2000
Kharin et al, 2007
Reanalyses (black, grey) CMIP3 Models (colours)
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Zonally averaged 20-yr 24-hr temperature
extremesRecent climate - 1981-2000
Kharin et al, 2007
Reanalyses (black, grey) CMIP3 Models (colours)
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What changes are projected?
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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)
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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
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Have humans influenced extremes?
Photo F. Zwiers
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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
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Detection of human influence on extremes

• 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)

Trend in 20-yr extreme SWH (1955-2004)
cm/yr
cm/yr
Wang et al, 2009
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Attributing changes in the risk of extremes

• New idea introduced during the IPCC AR4 process
• Cant 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

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Conclusions
Photo F. Zwiers
Photo F. Zwiers
Photo F. Zwiers
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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

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Photo F. Zwiers
The End