1. www.nr.no Evaluation of a dynamic downscaling of precipitation over the Norwegian mainland Orskaug E. a, Scheel I. b, Frigessi A. c,a, Guttorp P. d,a, Haugen J. E. e, Tveito O. E. e, Haug O. a a Norwegian Computing Center, Oslo, Norway b Department of Mathematics, University of Oslo, Oslo, Norway c Department of Biostatistics, University of Oslo, Oslo, Norway d University of Washington, Seattle, USA e The Norwegian Meteorological Institute, Oslo, Norway

2. Motivation ► Climate research produces an increasing number of data sets combining different GCMs, CO2 emission scenarios and downscaling techniques. ► For impact studies, but also as an issue of separate interest, the quality of these data need to be verified.

3. Goal ► We want to compare downscaled ERA-40 reanalysis data (RCM) against observations of Norwegian precipitation. ▪How good are the RCM data? ▪Where (in the distribution) does the RCM differ from the observations? ▪Where (geographically) does the RCM perform best/worst?

4. Why is this work important? ► It assesses the quality of a dynamic downscaled data and highlights which areas these data capture reality and where there are deviations from the truth. ► Another aim is to show how standard methods of statistical testing may be used to assess dynamic downscaling.

5. Data Model data ► RCM model, dynamically downscaled HIRHAM model, forced by ERA-40 reanalysis data from the ENSEMBLES project. ► Spatial resolution of 25 x 25 km 2. ► Reliant on the downscaling, still supposed to possess properties similar to real weather locally over longer time periods. Observations ► Interpolations (1 x 1 km 2 ) from a triangulation of the official measurement stations operated by The Norwegian Meteorological Institute. ► Aggregated to 25 x 25 km 2 scale by collecting 1 x 1 km 2 grid cells with centre points within the RCM cell, the mean is representing the precipitation within that grid cell.

6. Data – The RCM ► The RCM from the ENSEMBLES project

7. Data – properties for both data sets ► Climate variable: precipitation ► Time period: 1961 – 2000 ► Time scale: Daily, seasonal ► Resolution: 25 x 25 km 2 ► Number of grid cells: 777 grid cells covering Norway

8. Methods for comparison ► Evaluate the distributions 1.Global measure: Kolmogorov Smirnov test 2.Local measures:

9. Comments ► Drizzle effect avoided: conditioned on wet days; i.e. days with precipitation below a small, positive threshold (0.5 mm/day) are discarded. ► Day-to-day correlation in the RCM is partly lost due to downscaling, hence the distributions have to be compared instead of comparing day by day. ► Separate tests for each grid cell and each season.

10. Kolmogorov-Smirnov test ► K-S two sample test is used to check whether the empirical distributions from the RCM and the observations are equal. ► To avoid the problem of tied data, a small, random normally distributed number, N(0, σ 2 ), is added to each data point. σ = 1e-7

11. Kolmogorov-Smirnov test – Results ► The null hypothesis of equality of the distributions are rejected for almost all grid cells for all the four seasons. ► Global picture: the RCM does not have the same distribution as the observations. ► Next: want to find out where the distributions differ; local measures.

12. Methods for comparison ► Evaluate the distributions 1.Global measure: Kolmogorov Smirnov test 2.Local measures:

13. Test equality of quantiles Construction of the 2 x 2 contigency table

14. 0.05-quantile – Results ► Hardly any rejections of null hypothesis of equality. ► For low quantiles: the RCM reproduces the observations well both season- and nationwide.

15. 0.95-quantile – Results ► Mainly rejections of the null hypothesis of equality. ► Overall picture: the RCM underestimates high precipitation.

16. Generalized Pareto Distribution (GPD) ►

17. GPD – Results ► One-year return levels from GPD are more similar than expressed through the Kolmogorov-Smirnov test. ► But still: tendency that the RCM underestimates high precipitation.

18. Wet day frequency ► Wet day frequency = Proportion of wet days (among all days in the data) ► A wet day is defined to be above 0.5 mm/day for both data sets. ► The equality of the wet day frequency is tested by permutation testing.

19. Wet day frequency – Results ► Mainly rejections of the null hypothesis of equality. ► Total picture: Wet day frequency of the RCM is greater than for the observations.

20. Summary ► Small amounts of rainfall: the RCM shows good agreement with the observations. ► When rainfall amounts is beyond the first quartile, the agreement disappear. ► The RCM has too many and too small rain events for all seasons. ► This work is accepted for publication in Tellus A. An improvement/correction of the RCM is needed.

21. What to do next? ► We want to add a statistical correction method to the output of the RCM, especially improve the right tail. ► Simple linear regression was tried out, but did not improve the results. ► We are currently working on a more complex transformation with spatial corrections.