PRUDENCE

1. http://www.cru.uea.ac.uk/projects/mps/ PRUDENCE STARDEX MICE

2. STARDEXSTAtisical and Regional dynamical Downscaling of EXtremes for European regionsClare GoodessClimatic Research Unit, UK http://www.cru.uea.ac.uk/projects/stardex/

3. 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

4. The STARDEX objectives • To rigorously & systematically inter-compare & evaluate statistical & dynamical downscaling methods for the reconstruction of observed extremes & the construction of scenarios of extremes for selected European regions. • 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.

5. Statistical downscaling Relationships between larger-scale climate variables & local surface climate variables, derived from observed data, are applied to climate model output……... • based on the two assumptions that: • larger-scale variables are more reliably simulated • relationships remain valid in a changed climate

6. Spatially coherent changes in extremes have occurred over the last 40 years...

7. Western Europe August 2003 1958-2000 trend JJA heat wave duration Property damage: US$ 13 bn Fatalities: 27,000 (14,800 in France) Scale is days per year. Red is increasing Malcolm Haylock, UEA

8. 1958-2000 trend in JJA heavy rain events Fatalities: > 100 Economic losses: > US$18 bn Insured losses: > US$3 bn Central and Eastern Europe August 2002 Scale is days per year. Blue is increasing Malcolm Haylock, UEA

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

10. The STARDEX SDS methods • multiple linear regression • canonical correlation analysis • artificial neural networks • multivariate autoregressive model • conditional re-sampling & other analogue-based methods • methods based on a potential precipitation circulation index & critical circulation patterns • conditional weather generator • local & dynamical scaling

11. Handling many combinations of different methods (20+), regions (7), indices (13) & seasons (4) was difficult!

12. Key messages from the STARDEX verification work Skill varies station-station, season-season, index-index, method-method Winter Spring SE England: Haylock et al., IJC Summer Autumn

13. In majority of cases no consistently superior model, so a major recommendation is to use a range of the better SDS methods – just as the recommendation is to use multiple GCMs/RCMs Winter A2 scenario changes for the W Alps: Schmidli et al., GRL

14. Uncertainties are larger/skill lower – so use scenarios of summer rainfall with care Winter Summer A2 scenario changes for the W Alps: Schmidli et al., GRL

15. Projected changes in flow duration Cochem gauge, Mosel(USTUTT-IWS) % change in greatest 5-day winter rainfall A2, 2080s Black: present Green: B2, 2080s Red: A2, 2080s % change in greatest 5-day winter rainfall, 1958-2001, 611 stations

16. We have produced recommendations and guidelines for those wanting to undertake SDS – and to help users identify the most suitable methods • For example: • Robustness criteria • Applications criteria • Performance criteria

17. We have produced outputs in a wide range of formats: reports, papers and information sheets http://www.cru.uea.ac.uk/projects/stardex

18. So STARDEX provides a sound scientific starting point for ENSEMBLES….. http://www.cru.uea.ac.uk/projects/stardex/