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Objective

D11 Summary: The need for downscaling of extremes: An evaluation of interannual variations in the NCEP reanalysis over European regions. Objective. Provide „recommendations on variables and extremes for which downscaling is required“.

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Objective

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  1. D11 Summary:The need for downscaling of extremes: An evaluation of interannual variations in the NCEP reanalysis over European regions

  2. Objective • Provide „recommendations on variables and extremes for which downscaling is required“. • Quantify skill of a GCM in statistics of extremes in European study regions. • Dependence on • Parameter and statistic • Region • Season • Scale • As a guide to focus downscaling efforts.As a benchmark to quantify ‚added value‘ of downscaling.

  3. 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. (Lüthi et al. 1996, Murphy 1999, Widmann and Bretherton 2001) • Use Reanalysis as a quasi-perfect surrogate GCM. • Distinguish between resolved (GCM grid-point) and unresolved (single station) scales.

  4. 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

  5. Indices of Extremes TMIN Mean minimum temperature TMAX Mean maximum temperature TQ90 90% quantile of daily maximum temperature TQ10 10% quantile of daily minimum temperature TFROST Number of days with minimum temperature below 0°C THWDI Heat wave duration: Days with 5K above normal Tmax (> 6 days) PMEAN Mean precipitation PINT Precipitation intensity, mean amount on a wet day (>1 mm d-1). PQ90 90% quantile of daily precipitation on wet days PA90 Percentage of precipitation at days with > long-term 90% quantile PN90 Number of days with precipitation > long-term 90% quantile P5DMAX Seasonal maximum of 5-day total precipitation PCDD Seasonal maximum number of consecutive dry days (≤ 1 mm d-1)

  6. Procedure • Upscaling of daily station data to 2.5°x2.5° GCM grid • SYMAP analysis (Alps, Emilia-Romagna, Shepard 1984) • Variance correction (England, Osborn and Hulme 1997) • Block kriging (Rhine, Greece, Isaaks and Srivastava 1989) • Calculate seasonal Indices of Extremes • using STARDEX diagnostic software tool (Haylock 2003) • for NCEP and for upscaled observations • for selected single stations and for FIC stations • 1958-2000, more restricted for some regions • Calculate skill scores • Correlation, ratio of variance, RMSE • Visualisation by Taylor diagram

  7. Example: German Rhine Basin Precipitation Indices DJF JJA GCM scale Station scale

  8. Example: Cold Winter Days (TQ10) R2 > 0.55 R2 < 0.3

  9. Some Results • Correlation for T-indices mostly higher than P-indices. • For P-indices: Correlations are mostly not significant (rcrit=0.3) in summer and near significant in winter. Except for PMEA and PCDD. • For T-indices: Performance for extremes is comparable to that for means, except for TFROST and THWDI. • NCEP often seriously under- or overestimates variance. • Correlation with single stations not significant. (Except for some T-indices in some regions). • TQ90 in summer is better represented over England (r=0.8-0.9) compared to Greece (r=0.5-0.8). • NCEP is less skillful in mountains than over flatland. Particularly at station scale not so much at GCM scale.

  10. Some Open Questions • Long-term trends in the NCEP reanalysis. • Model deficiency in representing regional extremes? • Or inhomogeneity in the reanalysis process? • Suitability of skill measures • Correlation and STDEV are inappropriate to deal with count data.(TFROST, THWDI) • Model limitations vs. limited predictability • How much can downscaling improve skill? • Other Reanalyses • Are results specific to NCEP? What about ERA15, ERA40?

  11. General Conclusion • GCMs can be expected to provide valuable information on temperature extremes at the scale of a GCM grid, but this does not exclude that downscaling could improve. • Downscaling is desirable for precipitation extremes in both seasons even on spatial scales resolved by the GCM. • Numbers provide useful benchmarks to test the success of downscaling methods in WP4. • For single stations • Upscaled results from downscaled station series

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