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STARDEX The lessons learned … far…..

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Presentation on theme: "STARDEX The lessons learned … far….."— Presentation transcript:

1 STARDEX The lessons learned … far…..

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 PDF htmlPDFhtml Data Top level access to the Central Data Archive DODS NetCDFDODSNetCDF Station data example file ( DODS NetCDF CDL (ASCII)DODSNetCDFCDL (ASCII) Indices example file ( DODS NetCDF CDL (ASCII)DODSNetCDFCDL (ASCII) Links STARDEX homepage NetCDF homepage Climate and Forecast (CF) conventions History  Added CDL files  Uploaded corrected FIC station files  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: (for compatibility with ECMWF-driven regional models) Training period: & 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 England (UEA) P: per gp T: 8-30 per gp German Rhine (USTUTT) P: ~500 per gp T: ~150 per gp Greece (AUTH) P: 5-10 per gp T: 5-10 per gp Emilia-Rom. (ARPA) P: per gp T: 5-10 per gp Europe (FIC) 481 stations in total Alps (ETH) P: ~500 per gp Christoph Frei, ETH

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

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

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

14 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... 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 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 IberiaGreeceAlpsGermanyUKItaly UEAxxxxxx KCLxx ARPA-SMRxx ADGBx AUTHxx USTUTT-IWS & FTSxx ETHxx FICxxxxxx DMIxxxxxx UNIBEx CNRSxx

24 Emilia Romagna, N Italy ARPA-SMR

25 e.g, D12 – NCEP-based predictors UK – 90 th 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 5 th & 95 th percentiles are also shown Spearman correlationRank 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 OURANOS, Canada: Regional climate consortium

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

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 for statistical and dynamical downscaling

35 Performance criteria for statistical and dynamical downscaling

36 What will we learn in the next 10 months? How will this feed into ENSEMBLES?

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