Presentation on theme: "STARDEX The lessons learned …..so far….."— Presentation transcript:
STARDEX The lessons learned …..so far…..
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.
Assembling data sets is time consuming… but STARDEX now has good data & software resources, publicly available wherever possible
Defining extremes so that everyone is happy is not easy …
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…….
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 (pre.al-fic.st.eth.obs.nc) DODS NetCDF CDL (ASCII)DODSNetCDFCDL (ASCII) Indices example file (pind.al-fic.st.eth.obs.nc) 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
Identification of methodologies for ensuring consistent and fair comparisons requires a lot of thinking and planning…
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
Although STARDEX is about downscaling we have also had to upscale...
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
D11 example: French part of Alpine Region Winter (DJF) Summer (JJA) Precipitation Indices Juerg Schmidli, ETH
Spatially coherent changes in extremes have occurred over the last 40 years...
trend in frost days Scale is days per year. Red is decreasing Malcolm Haylock, UEA/STARDEX
trend in heavy summer (JJA) rain events Scale is days per year. Blue is increasing Malcolm Haylock, UEA/STARDEX
Some of these changes/patterns are consistent with predictor relationships...
Heavy winter rainfall and links with North Atlantic Oscillation/SLP CC1: Heavy rainfall (R90N) CC1: mean sea level pressure Malcolm Haylock, UEA/STARDEX
In general, predictors are well simulated by HadAM3P... HadAM3P
Winter EOFs of winter Z500 HadAM3P (left) and NCEP (right) ARPA-SMR
But when identifying the best predictors, it is easier to make recommendations about methodologies for doing this than the predictors themselves...
’Traditional’ methods work best, e.g., step-wise regression, correlation, PCA/CCA. Automated methods (neural networks, genetic algorithm) are less suitable
Probability of precipitation at station conditioned to wet and dry CPs Andras Bardossy, USTUTT-IWS
Handling many combinations of different methods (20+), regions (7), indices (13) & seasons (4) is difficult
But results from more detailed regional analyses will allow us to draw clearer conclusions...
Iberia (16 stations) – Spearman correlations for each model and season averaged across 7 rainfall indices
Iberia (16 stations) Averaged across all seasons, indices and stations 5 th & 95 th percentiles are also shown Spearman correlationRank of abs(bias)
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
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
But the challenge now is to synthesise everything and present it in a usable way...
D9: Observed trends D16: Recommendations on robust methods D18: Summary of changes in extremes D19: Assessment of uncertainties D20: Final project report
Robustness criteria for statistical downscaling
Application criteria for statistical and dynamical downscaling
Performance criteria for statistical and dynamical downscaling
What will we learn in the next 10 months? How will this feed into ENSEMBLES?