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“. 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.

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.

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

Indices of Extremes TMINMean minimum temperature TMAXMean maximum temperature TQ9090% quantile of daily maximum temperature TQ1010% quantile of daily minimum temperature TFROSTNumber of days with minimum temperature below 0°C THWDIHeat wave duration: Days with 5K above normal T max (> 6 days) PMEANMean precipitation PINTPrecipitation intensity, mean amount on a wet day (>1 mm d -1 ). PQ9090% quantile of daily precipitation on wet days PA90Percentage of precipitation at days with > long-term 90% quantile PN90Number of days with precipitation > long-term 90% quantile P5DMAXSeasonal maximum of 5-day total precipitation PCDDSeasonal maximum number of consecutive dry days (≤ 1 mm d -1 )

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 – , more restricted for some regions Calculate skill scores –Correlation, ratio of variance, RMSE –Visualisation by Taylor diagram

Example: German Rhine Basin DJFJJA GCM scale Station scale Precipitation Indices

Example: Cold Winter Days (TQ10) R 2 < 0.3 R 2 > 0.55

Some Results Correlation for T-indices mostly higher than P-indices. For P-indices: Correlations are mostly not significant (r crit =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= ) compared to Greece (r= ). NCEP is less skillful in mountains than over flatland. Particularly at station scale not so much at GCM scale.

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?

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