Introducing STARDEX: STAtistical and Regional dynamical Downscaling of EXtremes for European regions Clare Goodess* & the STARDEX team *Climatic Research Unit, UEA, Norwich, UK February 2002 to July COP10, 13 December 2004

The STARDEX team University of East Anglia, UK King's College London, UK Fundación para la Investigación del Clima, Spain University of Bern, Switzerland Centre National de la Recherche Scientifique, France Servizio Meteorologico Regional, ARPA-Emilia Romagna, Italy Atmospheric dynamics group, University of Bologna, Italy Danish Meteorological Institute, Denmark Eidgenössische Technische Hochschule, Switzerland Fachhochschule Stuttgart - Hochschule für Technik, Germany Institut für Wasserbau, Germany University of Thessaloniki, Greece

PRUDENCE focuses on dynamical downscaling STARDEX is evaluating an alternative, complementary approach to providing high- resolution information: statistical downscaling (SDS) This approach has some advantages: –Provides station/point values –Less computer intensive than dynamical downscaling STARDEX is performing a rigorous and systematic inter-comparison and evaluation of SDS methods and comparison with dynamical downscaling

STARDEX is focusing on temperature & rainfall extremes: 10 core indices covering frequency, magnitude and persistence (the STARDEX software package calculates 57 indices in total) The more robust SDS methods will be used to construct scenarios of extremes for end of 21 st century for 6 regions & Europe as a whole

The underlying principles of SDS: Observed relationships between the local surface climate and larger-scale climate (e.g., circulation patterns, atmospheric humidity) are established… And applied to GCM output for the future… On the assumption that these relationships will be unchanged in the future…. And that the larger-scale climate is better simulated by GCMs than local climate

Greenland Anticyclone Sole Cyclone (left) & (right) Examples of strong relationships between intense rainfall and circulation established by STARDEX: Guy Plaut, CNRS-INLNAndrás Bárdossy, USTUTT-IWS French Alpes MaritimeGerman Rhine (winter) Circulation pattern optimisation technique - CP02

So STARDEX has done a lot of work analysing observed data: Including a unique data set of daily station records for ~500 European stations - indices available from And denser station networks for 6 regions: Iberia, Greece, UK, German Rhine, Alps, northern Italy

trend in DJF frost days Scale is days per year. Red is decreasing Athens February 2004 Malcolm Haylock, UEA

trend in JJA heat wave duration Scale is days per year. Red is increasing Malcolm Haylock, UEA Property damage: US$ 13 bn Fatalities: 27,000 (14,800 in France) Western Europe August 2003

trend in JJA heavy rain events Scale is days per year. Blue is increasing Central and Eastern Europe August 2002 Fatalities: > 100 Economic losses: > US$18 bn Insured losses: > US$3 bn Malcolm Haylock, UEA

In part, these changes can be explained by changes in circulation & other predictors used in SDS E.g., Heavy winter rainfall and links with North Atlantic Oscillation/SLP Haylock & Goodess, IJC, 2004

A major, systematic inter-comparison of SDS methods is underway in STARDEX: 20+ methods, 7 regions, 13 indices and 4 seasons Iberian Peninsula – Spearman correlations for 6 SDS models and 4 seasons averaged across 7 extreme rainfall indices and 16 stations Neural network methods perform best in this example – but skill always varies between season & index Malcolm Haylock, UEA

Currently drawing up recommendations on the most appropriate downscaling methods and guidance for users (building on IPCC guidelines) And constructing future scenarios…. Will summarise changes and their reliability for a broader audience STARDEX has 7 months to go :

STARDEX outputs, in a variety of formats for different audiences, are available from the web site : e.g., information sheets (2-sides A4)