Application of Generalized Extreme Value theory to coupled general circulation models
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Transcript of Application of Generalized Extreme Value theory to coupled general circulation models
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N
Application of Generalized Extreme Value theory to coupled general circulation models
Michael F. WehnerLawrence Berkeley National Laboratory
SAMSI Climate Change WorkshopFebruary 17-19, 2010
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Outline
GEV results in assessment reports
Uncertainty in temperature extremes
Model fidelity and precipitation extremes
A few points for the discussion session
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GEV results in assessment reports
“Rare events will become commonplace”
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Simulations for 2090-2099 indicating how currently rare extremes (a 1-in-20-year event) are projected to become more commonplace. a) Temperature - a day so hot that it is currently experienced once every 20 years would occur every other year or more by the end of the century. (Units:years)
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Sources of uncertainty in estimating return values
20 year return value of annual maximum daily mean surface air temperature
GEV parameters (Short sample size)
Unforced internal variability
Multi-model differences
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GEV parameter uncertainty
Following the bootstrapping method of Hosking and Wallis1. Fit GEV parameters to sample
2. Generate 50 random samples distributed by the GEV distribution
3. Calculate return values and their standard deviation
CCSM3.0a)20 yearsb)40 yearsc)100 yearsd)Average over land (CMIP3 models)
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Internal variability
1. Divide long control run into 40 year segments
2. Calculate return value for each segment and
CCSM3.0600 years
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Multi-model variation
Fifteen CMIP3 forty year control runs Intermodel standard deviation
Color scale is5 times the previous twoslides
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Multi-model variation
Fifteen CMIP3 forty year control runs Sam as previous except remove mean state bias
Color scale is5 times the previous twoslides
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Model resolution and extreme precipitation
Typical CMIP3 models are too coarse to simulate rare intense storms.
Horizontal resolution study with fvCAM2.2 200km (B mesh) 100km (C mesh) 50km (D mesh)
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Model resolution and extreme precipitation
20 year return value of annual maximum daily total precipitation (mm/day)
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from the CCSP3.3 report
Simulations for 2090-2099 indicating how currently rare extremes (a 1-in-20-year event) are projected to become more commonplace. (b) daily total precipitation events that occur on average every 20 years in the present climate would, for example, occur once in every 4-6 years for N.E. North America. (Units:years)
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Conclusions
IPCC AR5 will contain far more about extremes than AR4
Largest source of uncertainty is inter-model difference Uncertainty in the fit of GEV is about the same as
unforced internal variability and is small!
Extreme precipitation requires high resolution. At least over land. Makes it hard to make projections with the CMIP3
models.
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Discussion
GEV distribution fits climate data very well
Cells that fail the Anderson Darling test at 5% level Surface air temperature annual
maximum Arctic failure is due to clustering
at freezing point. Not very interesting, return value is 0oC.
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Discussion
Detection & attribution of changes in extreme weather events Zwiers et al GEV methodology: let location parameter
be time dependent. Scale and shape parameters be static. Test whether time dependence is significant.• Temperature: Is this trivial if mean temperature changes have
been detected and attributed? How does the difference between a return value and the mean change?
• Precipitation: Widely believed to be more detectible due to Clausius-Clayperon relationship. But changes may not be of the same sign. May not be as severe as mean precipitation changes
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Projected 1990-2090 RV minus Tmean
SRES A1B (4 models) Change is confined to land and fairly small (<2.5K) Should we expect to detect this change in distribution shape?
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1990-2090 wintertime precipitation changes
SRES A1B
20 year return value mean x