1. UNDERSTANDING, DETECTING AND COMPARING EXTREME PRECIPITATION CHANGES OVER MEDITERRANEAN USING CLIMATE MODELS Dr. Christina Anagnostopoulou Department of Meteorology-Climatology, School of Geology Aristotle University of Thessaloniki Greece

2. To assess the ability of RCMs datasets to simulate extreme daily precipitation To produce estimates of predicted changes in return levels by future time periods (2031-2050 and 2081-2100) Detection of extreme precipitation assuming that model predictions are accurate Aim

3. Data and methods Results for selected grid points Spatial distribution of the extreme precipitation indices Differences of the extreme precipitation indices between future and reference time period Outline

4. KNMI Data C4I RCMs data for Mediterranean region Window: 10 o W – 35 o E 31 o N - 45 o N

5. Description of RCMs used KNMI-RACMO2: Royal Netherlands Meteorological Institute ( KNMI, Lenderink et al., 2003; van den Hurk et al., 2006 ) Parent ECHAM5 Time period 1950-2100 SRES A1B Physical parameterizations of ΕCMWF (European Centre for Medium – Range Weather Forecasts) used also for ERA-40 (http://www.ecmwf.int/research/ifsdocs).http://www.ecmwf.int/research/ifsdocs Spatial Resolution 25x25km.

6. Description of RCMs used C4IRCA3 : Community Climate Change Consortium for Ireland (C4I). Parent ECHAM5 Time period 1950-2050 SRES A2 RCA3 the third version of the Rossby Centre Atmospheric model (Kjellström et al., 2005) Spatial Resolution 25x25km.

7. Methodology Geveralized Extreme Value Distribution μ: location parameter σ: scale parameter ξ: shape parameter Return level for ξ 0 for ξ = 0

8. Estimation for GEV distribution 1. Maximum Likelihood Estimation-MLE 2. Bayesian Method

9. Methodology Reference period:1951-2000 20year period: 2031-2050 20year period: 2081-2100 Indices P m : medianP m (t)=X 0.5 (t) P 20 : 20-year return valueP 20 (t)=X 0.95 (t) P 100 : 100-year return value P 100 (t)=X 0.99 (t)

10. Western Mediterranean Central Mediterranean Eastern Mediterranean

11. Maximum Likelihood Estimation-MLE KNMI C4I Eastern Mediterranean Central Mediterranean Western Mediterranean

12. Bayesian Method Eastern Mediterranean Central Mediterranean Western Mediterranean location scaleshapeReturn level

13. Spatial distribution of maximum annual precipitation Max Min Mean

14. Spatial distribution of the extreme precipitation indices KNMI-MLE

15. KNMI - MLE

16. Spatial distribution of the extreme precipitation indices C4I - MLE

17. Spatial distribution of the extreme precipitation indices C4I - MLE

18. Spatial distribution of the extreme precipitation indices KNMI-Bayes

19. KNMI - Bayes

20. Spatial distribution of the extreme precipitation indices C4I - Bayes

21. Spatial distribution of the extreme precipitation indices C4I - Bayes

22. Differences of the extreme precipitation indices between the two time period (2031-2050 & 2081-2100) and the reference period (1951-2000) KNMI-MLE Differences of the extreme precipitation indices between the two time period (2031-2050 & 2081-2100) and the reference period (1951-2000) KNMI-MLE

23. Differences of the extreme precipitation indices between the time period (2031-2050) and the reference period C4I-MLE

24. Differences of the extreme precipitation indices between the two time period (2031-2050 & 2081-2100) and the reference period (1951-2000) KNMI-Bayes

25. Differences of the extreme precipitation indices between the time period (2031-2050) and the reference period C4I-Bayes

26. Concluding remarks The two RCMs datasets simulate reasonably well the extreme annual daily precipitation Pm index presents no change or a slight decrease for the future time period, in Mediterranean region P 20, an index that locates in the tail of the GEV distribution, present increase especially in central Mediterranean The two estimators (MLE and Bayesian) present similar results for the reference period but different for the future time-period. The Bayesian method present a practical advantage.