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Application of Generalized Extreme Value theory to coupled general circulation models

Michael F. WehnerLawrence Berkeley National Laboratory

mfwehner@lbl.gov

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

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Thank You!