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The STARDEX project - background, challenges and successes

Explore the STARDEX project's rigorous evaluation of statistical and dynamical downscaling methods for extremes reconstruction and future scenarios in Europe. Discover robust techniques for reliable temperature and precipitation-based extreme scenarios. Access diagnostic software, core indices, and trends analysis. Investigate scale trends in precipitation and predictor variables, alongside GCM/RCM output analysis. Study regional climate anomalies with high-resolution observations and downscaling methodologies. Evaluate improved downscaling methods emphasizing extremes and predictor selection across study regions.

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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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