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STARDEX The lessons learned

STARDEX The lessons learned. …..so far…. http://www.cru.uea.ac.uk/projects/stardex/. The STARDEX objectives.

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STARDEX The lessons learned

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  1. STARDEX The lessons learned …..so far….. http://www.cru.uea.ac.uk/projects/stardex/

  2. 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.

  3. Assembling data sets is time consuming… but STARDEX now has good data & software resources, publicly available wherever possible

  4. Defining extremes so that everyone is happy is not easy…

  5. We should have given more consideration to dissemination of data deliverables in the proposal… but our new DODS working group has come up with a solution…….

  6. ETH Stardex Central Data Archive Abstract This website provides links and useful information for the development of the STARDEX Central Data Archive. This site is only for development and testing purposes. The entire content will eventually be moved to the STARDEX website. This website contains work in progress. • Documents • Current Draft of the Central Data Archive Description PDFhtml • Data • Top level access to the Central Data Archive DODSNetCDF • Station data example file (pre.al-fic.st.eth.obs.nc) DODSNetCDFCDL (ASCII) • Indices example file (pind.al-fic.st.eth.obs.nc) DODSNetCDFCDL (ASCII) • Links • STARDEX homepage • NetCDF homepage • Climate and Forecast (CF) conventions • History • 2004-08-04 Added CDL files • 2004-07-16 Uploaded corrected FIC station files • 2004-07-15 Initial version

  7. Identification of methodologies for ensuring consistent and fair comparisons requires a lot of thinking and planning…

  8. Principles of verification for D12 • Predictor dataset : NCEP reanalysis • Predictand datasets: “FIC dataset” and regional sets • Regions • Stations within regions • Core indices • Verification period: 1979-1993 (for compatibility with ECMWF-driven regional models) • Training period: 1958-1978 & 1994-2000 • Statistics: RMSE, SPEARMAN-RANK-CORR for each station/index

  9. Although STARDEX is about downscaling we have also had to upscale...

  10. D11 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

  11. D11 example: French part of Alpine Region Precipitation Indices Winter (DJF) Summer (JJA) Juerg Schmidli, ETH

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

  13. 1958-2000 trend in frost days Scale is days per year. Red is decreasing Malcolm Haylock, UEA/STARDEX

  14. 1958-2000 trend in heavy summer (JJA) rain events Scale is days per year. Blue is increasing Malcolm Haylock, UEA/STARDEX

  15. Some of these changes/patterns are consistent with predictor relationships...

  16. Heavy winter rainfall and links with North Atlantic Oscillation/SLP CC1: Heavy rainfall (R90N) CC1: mean sea level pressure Malcolm Haylock, UEA/STARDEX

  17. In general, predictors are well simulated by HadAM3P...

  18. Winter EOFs of winter Z500 HadAM3P (left) and NCEP (right) ARPA-SMR

  19. But when identifying the best predictors, it is easier to make recommendations about methodologies for doing this than the predictors themselves...

  20. ’Traditional’ methods work best, e.g., step-wise regression, correlation, PCA/CCA. Automated methods (neural networks, genetic algorithm) are less suitable

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

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

  23. Partners/regions

  24. Emilia Romagna, N Italy ARPA-SMR

  25. e.g, D12 – NCEP-based predictorsUK – 90th percentile rainday amounts

  26. But results from more detailed regional analyses will allow us to draw clearer conclusions...

  27. Iberia (16 stations) – Spearman correlations for each model and season averaged across 7 rainfall indices

  28. Iberia (16 stations) Averaged across all seasons, indices and stations 5th & 95th percentiles are also shown Spearman correlation Rank of abs(bias)

  29. Stakeholders, policy makers and scientists are interested in what we are doing e.g., State of Baden-Wurttemberg PLANAT Swiss Federal platform on natural disasters SENAMHI, Peru: Climate change scenarios 2004-2050 OURANOS, Canada: Regional climate consortium

  30. So we are trying to publish papers and to provide a range of material on the public web sitesuitable for different users.....

  31. But the challenge now is to synthesise everything and present it in a usable way...

  32. D9: Observed trends D16: Recommendations on robust methods D18: Summary of changes in extremes D19: Assessment of uncertainties D20: Final project report

  33. Robustness criteria for statistical downscaling

  34. Application criteria forstatistical and dynamical downscaling

  35. Performance criteria forstatistical and dynamical downscaling

  36. What will we learn in the next 10 months? How will this feed into ENSEMBLES? http://www.cru.uea.ac.uk/projects/stardex/

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