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The STARDEX project - background, challenges and successes

The STARDEX project - background, challenges and successes. A project within the EC 5th Framework Programme 1 February 2002 to 31 July 2005 http://www.cru.uea.ac.uk/projects/stardex/ http://www.cru.uea.ac.uk/projects/mps/. Clare Goodess Climatic Research Unit, UEA, Norwich, UK.

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The STARDEX project - background, challenges and successes

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  1. The STARDEX project - background, challenges and successes A project within the EC 5th Framework Programme 1 February 2002 to 31 July 2005 http://www.cru.uea.ac.uk/projects/stardex/ http://www.cru.uea.ac.uk/projects/mps/ Clare Goodess Climatic Research Unit, UEA, Norwich, UK

  2. The STARDEX consortium http://www.cru.uea.ac.uk/projects/stardex/

  3. STARDEX general objectives • To rigorously & systematically inter-compare & evaluate statistical and dynamical downscaling methods for the reconstruction of observed extremes & the construction of scenarios of extremes for selected European regions & Europe as a whole • To identify the more robust downscaling techniques & to apply them to provide reliable & plausible future scenarios of temperature & precipitation-based extremes http://www.cru.uea.ac.uk/projects/stardex/

  4. Consistent approach: e.g., indices of extremes http://www.cru.uea.ac.uk/projects/stardex/

  5. STARDEX Diagnostic extremes indices software • Fortran subroutine: • 19 temperature indices • 35 precipitation indices • least squares linear regression to fit linear trends & Kendall-Tau significance test • Program that uses subroutine to process standard format station data • User information document • All available from public web site http://www.cru.uea.ac.uk/projects/stardex/

  6. STARDEX core indices • 90th percentile of rainday amounts (mm/day) • greatest 5-day total rainfall • simple daily intensity (rain per rainday) • max no. consecutive dry days • % of total rainfall from events > long-term P90 • no. events > long-term 90th percentile of raindays • Tmax 90th percentile • Tmin 10th percentile • number of frost days Tmin < 0 degC • heat wave duration http://www.cru.uea.ac.uk/projects/stardex/

  7. 1958-2000 trend in frost days Days per year Blue is increasing Malcolm Haylock, UEA

  8. 1958-2000 trend in summer rain events > long-term 90th percentile Scale is days/year Blue is increasing Malcolm Haylock, UEA

  9. Region Winter Spring Summer Autumn England ++ -- Germany ++ + -- + N. Italy - - + + Greece - - Switzerland ++ + + ++ French Alps variable variable variable + Local scale trends in extreme heavy precipitation indices Andras Bardossy, USTUTT-IWS

  10. Investigation of causes, focusing on potential predictor variables e.g., SLP, 500 hPa GP, RH, SST, NAO/blocking/ cyclone indices, regional circulation indices http://www.cru.uea.ac.uk/projects/stardex/

  11. Winter R90N relationships with MSLP& NAO, Malcolm Haylock R = 0.64 http://www.cru.uea.ac.uk/projects/stardex/

  12. Winter R90N relationships with MSLP, Malcolm Haylock R90N Canonical Pattern 1. Variance = 11.3%. MSLP Canonical Pattern 1. Variance = 44.4%. http://www.cru.uea.ac.uk/projects/stardex/

  13. Analysis of GCM/RCM output & their ability to simulate extremes and predictor variables and their relationships http://www.cru.uea.ac.uk/projects/stardex/

  14. Annual Cycle RCMs: HadAM3H control (1961-1990). ERA15-driven Domain: 2.25-17.25 °E, 42.25-48.75 °N, All Alps Christoph Frei, ETH

  15. SON Wet-day 90% Quantile (mm/day) RCMs: HadAM3H control (1961-1990). Christoph Frei, ETH

  16. Approach • Use high-resolution observations to evaluate model at its grid scale • „How well can a GCM represent regional climate anomalies in response to changes in large-scale forcings?“ Use interannual variations as a surrogate forcing. • Use Reanalysis as a quasi-perfect surrogate GCM. • Distinguish between resolved (GCM grid-point) and unresolved (single station) scales. Christoph Frei, ETH

  17. Study Regions Europe (FIC) 481 stations in total England (UEA) P: 13-27 per gp T: 8-30 per gp German Rhine (USTUTT) P: ~500 per gp T: ~150 per gp Alps (ETH) P: ~500 per gp Greece (AUTH) P: 5-10 per gp T: 5-10 per gp Emilia-Rom. (ARPA) P: 10-20 per gp T: 5-10 per gp Christoph Frei, ETH

  18. Example: German Rhine Basin Precipitation Indices DJF JJA GCM scale Station scale Christoph Frei, ETH

  19. Inter-comparison of improved downscaling methods with emphasis on extremes http://www.cru.uea.ac.uk/projects/stardex/

  20. Downscaling methods • canonical correlation analysis • neural networks • conditional resampling • regression • conditional weather generator • “potential precipitation circulation index”/”critical circulation patterns” Study regions

  21. Predictor selection methods • Correlation • Stepwise multiple regression • PCA/CCA • Compositing • Neural networks • Genetic algorithm • “Weather typing” • Trend analysis http://www.cru.uea.ac.uk/projects/stardex/

  22. Downscaling of Tmax90p Model is constructed on the period 1958-1978/1994-2000 and validated on 1979-1993 PREDICTAND Time series of 90th percentile of maximum temperature (Tmax90p); 30 stations from Emilia-Romagna (1958-2000) that were clusterised in 3 regions(Fig.2) PREDICTORS Exp 1: Seasonal mean (DJF) of first 4 PCs of Z500 over the area: 90°W-60°E, 20°N-90°N ) Exp 2: Seasonal mean (DJF) of WA, EA, EB, SCA, over the area: 90°W-60°E, 20°N-90°N Clusters for Tmax90p (DJF) Tomozeiu et al., ARPA-SMR

  23. Interannual variability of downscaled, Observed and NCEP Tmax90p (DJF), 1979-1993 Tomozeiu et al., ARPA-SMR

  24. 5 4 11 2 3 Downscaling of 692R90N – 2 exp. Downscaling of 692R90N Model is constructed on the period1958-1978/1994-2000and validated on1979-1993 PREDICTAND Time series of observed no.of events greater than 90th percentile of raindays (692R90N);44 stations from Emilia-Romagna (1958-2000) that were clusterised in 5 regions(Fig.1) PREDICTORS Seasonal mean (DJF) of first 4 PCs of Z500 that covers the area: 90°W-60°E, 20°N-90°N (NCEPreanalysis,2.5°x2.5°) Fig.1 Clusters for 692R90N (DJF) Tomozeiu et al., ARPA-SMR

  25. Zone 1_forecast 2_forecast 3_forecast 4_forecast 5_forecast Emilia Romagna region forecast 1_observed 0.21 2_observed 0.30 3_observed 0.37 4_observed 0.27 5_observed 0.26 Emilia Romagna region obs 0.25 Skill of the statistical downscaling model 1979-1993 expressed as correlation coefficient between the observed and estimated 692R90N (bold-5%significance) Tomozeiu et al., ARPA-SMR

  26. Probability of precipitation at station 75103 conditioned to wet and dry CPs Andras Bardossy, USTUTT-IWS

  27. At the end of the project (July 2005) we will have: • Recommendations on the most robust downscaling methods for scenarios of extremes • Downscaled scenarios of extremes for the end of the 21st century • Summary of changes in extremes and comparison with past changes • Assessment of uncertainties associated with the scenarios http://www.cru.uea.ac.uk/projects/stardex/

  28. Dissemination & communication • internal web site (with MICE and PRUDENCE) • public web site • scientific reports and papers • scientific conferences • information sheets, e.g., 2002 floods, 2003 heat wave • powerpoint presentations • external experts • within-country contacts http://www.cru.uea.ac.uk/projects/stardex/

  29. http://www.cru.uea.ac.uk/projects/stardex/ http://www.cru.uea.ac.uk/projects/mps/ c.goodess@uea.ac.uk

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