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Statistical Analyses of Extremes from a Regional Climate Model
Chris Ferro
Climate Analysis Group
Department of Meteorology
University of Reading
Royal Meteorological Society, London, 21 January 2004
Overview
• Regional climate-change experiment
• Application of extreme-value theory
• Daily maximum temperature extremes– Seasonality– Clustering– Apparent temperature
• Concluding remarks
Regional Modelling Experiment
• PRUDENCE• 10 high-res. RCMs
nested in global GCM• 30-year control
simulation, 1961-1990• 30-year A2 scenario
simulation, 2071-2100
Extreme-value Theory
Aim quantify extremal behaviour
Problems limited data, extrapolation
Solution exploit statistical regularities
Example
ondistributi GEV),,max(
ondistributi Normal
1
1
n
n
XX
XX
Seasonality: London grid box
Seasonality: statistical model
Estimate threshold: quantile regression
Excess distribution: generalised Pareto
Estimate parameters: maximum likelihood
Davison and Smith (1990) J. Royal Statistical Soc. B, 52, 393–442
Seasonality: London estimates
Scale (ese)1.27 (0.1)1.44 (0.2)
Shape (ese)-0.11 (0.04)-0.01 (0.07)
/1/1
ExcessPr
x
x
Seasonality: London estimates
Scale (ese)1.27 (0.1)1.44 (0.2)
Shape (ese)-0.11 (0.04)-0.01 (0.07)
/1/1
ExcessPr
x
x
Times of annual maxima: Europe
day of year
Control Scenario – Control
days
Clustering: London grid box
Clustering: London results
Mean cluster size (days)
90% confidence interval (days)
Control 3.2 (2.4, 3.9)
Scenario 4.0 (3.3, 4.7)
Ferro and Segers (2003) J. Royal Statistical Soc. B, 65, 545–556
Mean Cluster Size: EuropeControl Scenario / Control
days
Apparent Temperature: London
1984) (Steadman,e2.2T92.03.1Tapp
Apparent Temperature: model
Univariate distributions: GEV model for tails
Dependence structure: nonparametric estimate
cAXcAX PrPr
de Haan and Sinha (1999) The Annals of Statistics, 27, 732–759
Steadman (1984) J. Climate Applied Met., 23, 1674–1687
year) ain C35(TPrp Estimate app (scenario) 0.70(control), 0p̂:Empirical
Apparent Temperature: results
04.0,0017.0p̂ 89.0,50.061.0p̂
Review
• Many applications of extreme-value theory– Individual values (e.g. seasonality)– Clusters (e.g. warm spells)– Combinations (e.g. temp. and humidity)
• Preliminary Tmax analysis (London)– Shifted annual cycle– Longer warm spells– Greater heat stress
Further Information
PRUDENCE
Climate Analysis
Group
E-mail address
prudence.dmi.dk
www.met.rdg.ac.uk/cag /extremes
