STARDEX STAtistical and Regional dynamical Downscaling of EXtremes for European regions A project within the EC 5th Framework Programme EVK2-CT February 2002 to 31 July 2005 Co-ordinator: Dr Clare Goodess Climatic Research Unit, UK

The STARDEX consortium

STARDEX general objectives V To rigorously & systematically inter-compare & evaluate statistical, dynamical & statistical-dynamical downscaling methods for the reconstruction of observed extremes & the construction of scenarios of extremes for selected European regions V To identify the more robust downscaling techniques & to apply them to provide reliable & plausible future scenarios of temperature & precipitation-based extremes for selected European regions

STARDEX regional case studies V The Mediterranean (Iberia & Greece) V Scandinavia V British Isles V Germany V The Alps V Europe ( stations)

STARDEX WP1: Data set development, co-ordination & dissemination V To ensure that a consistent approach is used for all analyses V To ensure that the needs of the climate impacts community are taken into account & that output from the most recent simulations is available for use V To ensure wide dissemination to stakeholders, the scientific community & public

STARDEX WP1 deliverables V Public & members’ web sites V Standard data sets of: –station temperature/precipitation data –NCEP reanalysis data –Objective circulation patterns & indices –GCM & RCM data –damages arising from extreme events V Statistical diagnostic software tool V Final project report (July 2005)

STARDEX Diagnostic extremes indices software V Based on NCDC ClimateIndices/ECA code V Fortran subroutine: –19 temperature indices –e.g. 10/90th percentiles, growing degree days, frost days –35 precipitation indices –e.g. 20/40/50/60/80/90/95th percentiles, wet/dry spells –least squares linear regression to fit linear trends & Kendall-Tau significance test V Program that uses subroutine to process standard format station data V User information document

STARDEX WP2: Observational analysis of changes in extremes, their causes & impacts V To analyse observed station/regional data series for the 2nd half of the 20th century from specific regions of Europe in order to identify trends in the magnitude/frequency of occurrence of extremes &, for specific events, their losses in life and financial costs V To investigate whether these changes are related to changes in potential predictor variables

Severe winter storms across Germany and Europe, economic losses and causing circulation types © Caspary

11-year running average for the circulation type "West cyclonic" (Wz) for winter (Dec. - Feb.) for the period Black line: frequency (%), blue line: maximum persistence of Wz-periods (days). © Caspary

STARDEX WP2 deliverables V Summary of the analysis of observed extremes, their causes & damages (November 2002) V Recommendations on the best predictor variables for extreme events (January 2003)

STARDEX WP3: Analysis of GCM/RCM output & their ability to simulate extremes and predictor variables GCMs: HadAM3H (1.25°x1.875°) 150 km ECHAM4.5 (T106) RCMs: 50 km RCMs several models: HadRM, HIRHAM, CHRM,... for all study regions. 14 km RCM for some of the study regions

Observations Heavy Alpine precipitation, 90% Quantile, Sept.-Nov. HadRM3 (GCM-driven 60-90)HadRM3 (ERA-driven) CHRM (ERA-driven) Figure provided by Christoph Frei

ewrain: averages of 7 stations Regional: averages of 31 stations/12 grid points Multi: separate analysis of 31 stations/12 grid points, then averaged Scaled: upscaled to HadRM3 grid points (Osborn & Hulme) Daily rainfall SE England ewrain/badc: observed, HadRM3: simulated

STARDEX WP3 deliverables V Recommendations on variables & extremes for which downscaling is required (March 2003) V Recommendations on the most reliable predictor variables & evaluation of inter-relationships (January 2004)

STARDEX WP4: Inter-comparison of improved downscaling methods with emphasis on extremes V To improve existing circulation-based statistical downscaling methods: to reproduce observed extremes to incorporate additional predictors (stationarity) V To calibrate/validate improved methods using predictor variables from (a) Reanalysis data, & (b) GCM output V To compare results with output from RCMs V To identify the more robust downscaling methods

Multi-site simulation of precipitation by conditional resampling (CR) 20/60-day annual max prec. Sept-Mar,  : CR - deterministic & stochastic,  : CR - deterministic only  : unconditional resampling, solid line: observed Wilby et al., Climate Research, in press.

STARDEX WP4 deliverables V Downscaled extremes based on Reanalysis data (July 2003) V Downscaled extremes based on climate model output, for the present day (July 2004) V Improved statistical downscaling methods for scenarios of extremes(July 2004) V Recommendations on the more robust downscaling methods for scenarios of extremes (July 2004)

WP5: Application of the more robust downscaling techniques to provide scenarios of extremes for European regions for the end of the 21 st century V To use these scenarios to identify changes in extremes V To investigate whether these changes are in accordance with recent observed changes V To consider their potential impacts in terms of losses of life & financial costs V To assess the uncertainties associated with the scenarios

STARDEX WP5 deliverables V Downscaled scenarios for the end of the 21 st century for regions of Europe & locations across Europe (January 2005) V Summary of changes in extremes, comparison with past changes & consideration of impacts/damages (July 2005) V Assessment of uncertainties associated with the scenarios (July 2005)

STARDEX STAtistical and Regional dynamical Downscaling of EXtremes for European regions Co-ordinator: Dr Clare Goodess Climatic Research Unit, UK