WP4.3: Understanding Extreme Weather and Climate Events

1. us! WP4.3: Understanding Extreme Weather and Climate Events David B. Stephenson University of Reading • Background on extremes • Overview of WP4.3 • The first task 4.3a  some input needed • ENSEMBLES RT4/5 meeting, Paris, 10-11 February 2005

2. The definition problem: Extreme events can be defined by: Maxima/minima Magnitude Rarity Impact/losses Gare Montparnasse, 22 October 1895 “Man can believe the impossible, but man can never believe the improbable.” - Oscar Wilde

3. IPCC 2001 definition of extreme event: “An extreme weather event is an event that is rare within its statistical reference distribution at a particular place. Definitions of "rare" vary, but an extreme weather event would normally be as rare or rarer than the 10th or 90th percentile.” • No mention of magnitude • Why 10th or 90th percentile? • No mention of the tail (EVT) • Not the only definition

4. Other IPCC TAR 2001 definitions: Extreme climate event: “an average of a number of weather events over a certain period of time which is itself extreme (e.g. rainfall over a season)” Simple extremes: “individual local weather variables exceeding critical levels on a continuous scale” Complex extremes: “severe weather associated with particular climatic phenomena, often requiring a critical combination of variables”

5. A Risk Modelling Perspective Severe events (extreme loss events) caused by: • Rare weather events • Extreme weather events (amenable to EVT) • Clustered weather events (e.g. climate event) Natural hazard e.g. windstorm Damage e.g. building Loss e.g. claims ($) Risk=p(loss)=p(hazard) X vulnerability X exposure  extreme loss is not always due to extreme weather!

6. The Generalised Extreme Value distribution Maxima/minima and the extreme tail of distributions can be modelled asymptotically by the 3-parameter Generalised Extreme Value (GEV) distribution: • Models the tail of the probability distribution • Valid only when sufficiently far into the tail • No universal, absolute criteria for how far is sufficiently far

7. What can we learn from the study of tails (caudology?) …

8. … about the whole animal? PDF = Probable Dinosaur Function ??

9. Hypotheses about changing PDFs H0: No change (variation due to sampling only) Sampling uncertainty can be tested using tail model HL: Change due to “mean effect” e.g. Mearns et al. (1984), Wigley (1985), … HS: Change due to “variance effect” e.g. Katz and Brown (1992), Katz and coworkers … HLS: Change due to mean and variance effects e.g. Brown and Katz (1995), … Ha: “Structural change” in shape etc e.g. Kestin 2001, Antoniadou et al. 2001

10. Some key climate change questions … • How can we best estimate future possible changes in tail probabilities and return values for extreme events? multi-model ensemble data  tail probabilities • Do we understand the key processes that led to these changes? • Which factors are most important for determining changes in extreme events? • Large-scale atmospheric circulation • Land surface conditions (soil moisture, snow, ice) • Sea surface conditions (N. Atlantic sea surface temperatures) • Model resolution

11. ENSEMBLES WP4.3: Understanding Extreme Weather and Climate Events us!   Provision of statistical methods for identifying extreme events and the climate regimes with which they are associated. More robust assessments of the effects of climate change on the probability of extreme events and on the characteristics of natural modes of climate variability.

12. The WP4.3 team UREADMM: David Stephenson, Caio Coelho, Chris Ferro KNMI: Frank Selten and Adri Buishand and postdoc??? CERFACS: Laurent Terray and Emilia Sanchez INGV: Silvio Gualdi IFM: Mojib Latif, Ernst Bedacht NERSC: Dag Steinskog, Helge Drange, Nils Gunnar Kvamsto AUTH:Prof. Panagiotis Maheras, As. Prof. Helena Flocas, Anagnostopoulou, Konstantia Tolika, Maria Hatzaki UEA: Jean Palutikof, Tom Holt UFR: Martin Beniston, Stephane Goyette

13. Work tasks Task 4.3.a: Development and use of methodologies for the estimation of extreme event probabilities. Which are the best methods for inferring probabilistic tail information from multi-model ensembles of climate model simulations? Task 4.3.b: Exploring the relationships between extreme events, weather systems and the large-scale atmospheric circulation/climate regimes. How do different large-scale factors influence weather extremes? Task 4.3.c: The influence of anthropogenic forcings on the statistics of extreme events. How are extreme events likely to behave in the future?

14. Workpackage deliverables WP4.3a: Statistical methods for identifying regimes and estimating extreme-value tail probabilities using multi-model gridded data. Reports will be written up on this and disseminated to all partners and software in MATLAB (and possibly IDL) will be made freely available. WP4.3b: An analysis of which factors are the most important in determining extreme events in Europe obtained by applying the techniques developed in WP4.3a to the multi-model ensemble of coupled and time-slice simulations. Oceanic (e.g. SST and THC) and atmospheric (large-scale flow) factors will be investigated. Extremes in both Northern and Southern Europe will be addressed. WP4.3c: The behaviour of extremes in ENSEMBLES coupled scenarios will be analysed and interpreted using techniques and ideas developed for deliverables WP4.3a and WP4.3b.

15. Kilometre-stones M4.3.1: Spatial Extreme Value (SEV) model developed and coded up in MATLAB. (month 18) M4.3.2: Techniques for extracting large-scale regimes developed. (month 18) M4.3.3: Preliminary analysis of extremes and regimes in coupled runs completed. (month 24) M4.3.4: Key large-scale factors for extremes identified. (month 36) M4.3.5: Extremes in scenario runs summarised. (month 48)

16. What key scientific questions would you like to address? UREADMM What are the key processes that create extreme events and what are the best ways to diagnose these processes? KNMI Influence of the large scale circulation on extreme temperatures and precipitation CERFACSUnderstanding the links between large-scale circulation (LSC) patterns and extremes Predict changes in EE distribution due to anthropogenic forcing and the links with changes of the mean hydrological cycle INGV The relationship between the extreme events in the Euro-Mediterranean region and the large scale circulation IFMWill there be more extreme events in future climate (2xCO2) ? NERSCHow do different large-scale factors influence weather extremes? How are extreme events likely to behave in the future and what is the uncertainty in the prediction (e.g. due to model horizontal resolution, etc.)? AUTHHow are extreme events likely to behave in the future in Mediterranean? Sensitivity of changes in extremes to horizontal resolution inMediterranean UEA What are the linkages between circulation types and the occurrence of extremes at present and in the future under conditions of climate change? How will changes in weather types affect the occurrence of extremes? What other factors are responsible for the changing occurrence of extremes?

17. What types of extreme event are you most interested in? UREADMM Heat waves, extratropical storm-related extremes KNMIOccurences of heat waves and multi-day rainfall extremes CERFACS Precipitation, temperature and storms INGV Temperature (heat waves and cold air outbreaks) and Precip (floods and droughts) in the Euro-Mediterranean region. IFMExtreme windspeed, precipitation, temperature NERSCExtreme events associated with: extra-tropical cyclones, polar lows, Arcticfronts and (high latitude) topography AUTHPrecipitation and Temperature extremes in the Mediterranean UEA Floods (heavy rainfall events); heat waves (high temp events UFR ???

18. Which factors controlling these extremes are you interested in investigating? UREADMM Large-scale flow patterns KNMI Large scale flow regimes and soil moisture conditions CERFACS Tropical and extra-tropical LSC patterns, surface conditions INGV Large scale regimes in the North Atlantic (e.g., NAO), SSTs in the tropical Oceans IFM Changes/shift in general circulation, upper atmosphere NERSCThe large-scale atmospheric circulation Low frequency variability, ocean and sea-ice state Model uncertainty (sensitivity to resolution) AUTH Regional circulation (circulation types) surface and 500hPa conditions UEA Changing frequencies of weather types; changes in the relationship between extremes and weather types UFR ???

19. What software do you normally use to analyse data? UREADMM MATLAB, R KNMI Fortran routines and GrADS CERFACS IDL, ncl or fortran routines for the analysis ferret or ncl for the graphics. netcdf for the data. INGV GrADS and Matlab IFM Matlab, Fortran, Grads NERSCMatlab AUTHFortran and Visual Basic routines UEA Matlab, R UFR ??? R software: www.r-project.org (free, unix/microsoft/mac, statistical language)

20. What statistical tools might be useful for exploring extreme events in the simulations? UREADMM Spatio-temporal exploratory methods and probability models   KNMI Analysis of extremes with covariates fi indices of large scale flow regimes. These methods can be developed using the 62 member ensemble of 140 year long transient coupled simulations that we have done. CERFACS characterize completely extremes properties improve the methods to compare observed extremes to simulated ones. address the questions of stationarity and homogenization of obs daily data INGV Clustering defined using quantiles and thresholds exceedances IFM percentile analysis, use of extrem indices (e.g. number of frost days, number wet days,....), ARMA model to find out, if a trend is a product of quasistationary climate NERSC Multivariate extreme analysis AUTH Statistica, R, SPSS, Stardex Diagnostic tools UEA GEV; r largest; covariates/joint probabilities UFR ???

21. Original person months in WP4.3

22. DoW person months in WP4.3

23. First 18 months D4.3.1: Statistical methods for identifying regimes and estimating extreme-value tail probabilities using multi-model gridded data. Reports will be written up on this and disseminated to all partners and software in MATLAB (and possibly IDL) will be made freely available (Month 18 – February 2007) Milestones M4.3.1: Development of methodologies to explore climate variability and extreme events, tested initially on existing simulations, for use with the ENSEMBLES multi-model system (Month 18 – February 2007)

24. What statistical tools need developing? • What are the scientific questions/hypotheses? questions  design of experiments & analysis • Which type of extremes are to be investigated? • Which type of processes are important? Some ideas: • Toolkit of exploratory tools for extreme events • Spatial extreme value model multi-model ensemble data  tail probabilities • Extremes statistical model workshops

25. Extremes workshops in Switzerland • PRUDENCE project: small workshops in 2003 and 2004 in Chateau D’Oex • ENSEMBLES – workshop 5-8 March 2005 Chateau D’Oex to discuss the development of statistical methods • 2006,2007,2008,… - would like to continue these workshops Martin Beniston’s website: http://www.unifr.ch/geoscience/geographie/ENSEMBLES/rt8

26. Some statistical issues • Exploratory methods (curse of dimensionality) • Dependency • Spatial (between variables at different places) • Temporal • Short-term (e.g. between daily values) • Long-term (e.g. between winters) • Significance testing • Pointwise or simultaneous? • Local or global? • Power (i.e. failure to detect a change – type 2 error) • Large data sets (many grid point variables) • Spatial pooling/borrowing

27. What I would like to see in WP4.3 • Good interaction between all partners rather than unilateral work • Good communication between all partners (e.g. via messages sent to all on WP4.3 email list) • Some exciting scientific questions rather than just minimal achievement of deliverables • Joint WP4.3 papers and talks • Shared knowledge, software, and data

28. What colours do you see? 92% 99.6% 6% 0.25% deuteranopic 2% 0.10% protanopic http://www.vischeck.com

29. Can you see a difference? I can’t! Nor can 6% of the male population

30. Can you see a difference? I can’t! Nor can 6% of the male population

31. That’s all Folks