Dr. Christina Anagnostopoulou Department of Meteorology-Climatology, School of Geology

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

Aim • 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

Outline • 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

Data KNMI RCMs data for Mediterranean region Window: 10oW – 35oE 31oN - 45oN C4I

Description of RCMs used • KNMI-RACMO2:RoyalNetherlandsMeteorologicalInstitute (KNMI, Lenderink et al., 2003; van den Hurk et al., 2006) • ‘Parent’ECHAM5 • Time period 1950-2100 • SRESA1B • Physical parameterizations of ΕCMWF (EuropeanCentreforMedium – RangeWeatherForecasts) used also forERA-40 (http://www.ecmwf.int/research/ifsdocs). • Spatial Resolution 25x25km.

Description of RCMs used • C4IRCA3:Community Climate Change Consortium for Ireland (C4I). • ‘Parent’ECHAM5 • Time period 1950-2050 • SRESA2 • RCA3 the third version of the Rossby Centre Atmospheric model (Kjellström et al., 2005) • Spatial Resolution 25x25km.

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

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

Methodology • Reference period:1951-2000 • 20year period: 2031-2050 • 20year period: 2081-2100 • Indices • Pm: median Pm(t)=X0.5(t) • P20 : 20-year return value P20(t)=X0.95(t) • P100: 100-year return value P100(t)=X0.99(t)

Central Mediterranean Western Mediterranean Eastern Mediterranean

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

Bayesian Method scale shape Return level location Eastern Mediterranean Central Mediterranean Western Mediterranean

Spatial distribution of maximum annual precipitation Max Min Mean

Spatial distribution of the extreme precipitation indices KNMI-MLE

Spatial distribution of the extreme precipitation indices KNMI - MLE

Spatial distribution of the extreme precipitation indices C4I - MLE

Spatial distribution of the extreme precipitation indices KNMI-Bayes

Spatial distribution of the extreme precipitation indices KNMI - Bayes

Spatial distribution of the extreme precipitation indices C4I - Bayes

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

Differences of the extreme precipitation indicesbetween the time period (2031-2050) and the reference period C4I-MLE

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

Differences of the extreme precipitation indicesbetween the time period (2031-2050) and the reference period C4I-Bayes

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 • P20, 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.

Dr. Christina Anagnostopoulou Department of Meteorology-Climatology, School of Geology