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: 13-27 per gpT: 8-30 per gp

German Rhine (USTUTT)P: ~500 per gpT: ~150 per gp

Greece (AUTH)P: 5-10 per gpT: 5-10 per gp

Emilia-Rom. (ARPA)P: 10-20 per gpT: 5-10 per gp

Europe (FIC)481 stations in total

Alps (ETH)P: ~500 per gp

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)

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

Example: German Rhine Basin

DJF JJA

GCM scaleStation scale

Precipitation Indices

Example: Cold Winter Days (TQ10)

R2 < 0.3

R2 > 0.55

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.

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