Title: Assessing Human Influence on Changes in Extremes Francis Zwiers, Climate Research Division, Environment Canada Acknowledgements
1 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
2Outline
- Introduction
- Some approaches
- Can climate models simulate extremes?
- What changes are projected?
- Have humans influence on extremes?
- Conclusions
Photo F. Zwiers
3What 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)
4Simple Indices
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5Simple 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
6Indices 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
7Extreme value theory
Photo F. Zwiers
Photo F. Zwiers
8Extreme 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
9Extreme 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
10Can climate models simulate extremes?
Photo F. Zwiers
Photo F. Zwiers
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12Zonally averaged 20-yr 24-hr precipitation
extremesRecent climate - 1981-2000
Kharin et al, 2007
Reanalyses (black, grey) CMIP3 Models (colours)
13Zonally averaged 20-yr 24-hr temperature
extremesRecent climate - 1981-2000
Kharin et al, 2007
Reanalyses (black, grey) CMIP3 Models (colours)
14What changes are projected?
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15Projected 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)
16Projected 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
17 Have humans influenced extremes?
Photo F. Zwiers
18Changes 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
19Detection 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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21Attributing 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
22 Conclusions
Photo F. Zwiers
Photo F. Zwiers
Photo F. Zwiers
23Conclusions/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
24Photo F. Zwiers
The End