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Dr. Konstantia Tolika

ESF-MedCLIVAR Workshop: Climate Change Modeling for the Mediterranean region Trieste, Italy, 13-15 October 2008 Future Projections of Extreme Rainfall and Temperature Conditions over the Mediterranean Region: Scenarios from Three Updated Regional Climate Models Dr. Konstantia Tolika

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Dr. Konstantia Tolika

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  1. ESF-MedCLIVAR Workshop: Climate Change Modeling for the Mediterranean region Trieste, Italy, 13-15 October 2008 Future Projections of Extreme Rainfall and Temperature Conditions over the Mediterranean Region: Scenarios from Three Updated Regional Climate Models Dr. Konstantia Tolika Co – authors: Efthimia Kostopoulou, Ioannis Tegoulias, Christina Anagnostopoulou and Panagiotis Maheras Department of Meteorology and Climatology Aristotle University of Thessaloniki, Greece

  2. Main scope: study the occurrence of specific climate extremes in the Mediterranean region • Outline: • Description of Regional Climate Models used • Evaluation of Regional Climate Models vs. Observational Gridded data • Spatial analysis of Climate extreme Indices for reference and future periods • Maximum and minimum values of Indices • Trend Analysis of Climate Indices • Conclusions

  3. Description of Regional Climate Models used • C4IRCA3Community Climate Change Consortium for Ireland (C4I). • Based on IPCC SRES A2 • Driven by ECHAM5 (the 5th generation of the ECHAM General Circulation Model (GCM)) developed at the Max Planck Institute for Meteorology in Hamburg. • RCA3 the third version of the Rossby Centre Atmospheric model (Kjellström et al., 2005). • Spatial resolution 25x25km • 206x206 horizontal grid-points • 31 vertical levels. • Time period 1960-2050.

  4. Description of Regional Climate Models used • CNRM-RM4 Météo-France/CNRM (Centre National de Recherches Météorologiques) (Déqué and Somot (2007) and Radu et al. (2008)). • SRES A1B scenario. • 120x128 horizontal grid points • 31 vertical levels. • Spatial analysis 25x25km • Time period 1960-2050. • “Parent” modelALADIN (AireLimitéeAdaptationDynamiqueINitialisation) (http://www.cnrm.meteo.fr/aladin,).

  5. Description of Regional Climate Models used • KNMI-RACMO2:  RoyalNetherlandsMeteorologicalInstitute (KNMI- Koninklijk Nederlands Meteorologisch Instituut) (Lenderink et al., 2003; van den Hurk et al., 2006) • ‘Parent’ECHAM5 • Time period 1960-2100 • SRESA1B. • Physical parameterizations of ΕCMWF (EuropeanCentreforMedium – RangeWeatherForecasts) used also forERA-40 (http://www.ecmwf.int/research/ifsdocs). • Spatial Resolution 25x25km. • 114 grid points in longitudinal directionand 100in latitudinal direction. • 40 vertical levels

  6. Description of Regional Climate Models used

  7. Climate extreme indices studied for the Mediterranean region

  8. Evaluation of Regional Climate Models • ENSEMBLES-RT5 daily gridded observational datasets • Daily precipitation - temperature have been developed on the basis of a European network of high quality station series (http://eca.knmi.nl). • Period 1950-2006 (Haylock et al., 2008). • 1961-1990 period used • Domain of study (12.5oW - 37.5oE and 30oN - 45oN). • Only grids with 80% temporal coverage of data for the reference period were utilised.

  9. Evaluation of Regional Climate Models A. B. Grids with 80% temporal coverage of temperature (a) and precipitation (b) data for the 1961-1990 reference period.

  10. Evaluation of Regional Climate Models Differences of TXQ90 between CNRM (left), KNMI (right) and gridded observations, for summer. Summer:CNRM, extreme temperatures underestimated in E. Mediterranean (-6o C western Balkans), whereas positive differences are observed (overestimation) in the west of the Iberian Peninsula. In contrast, KNMI model underestimates TXQ90 in the Iberian Peninsula while in Italy and Greece simulates better the index (small differences with the observational gridded data). Similar behaviour of the three models in autumn (underestimation)

  11. Evaluation of Regional Climate Models Seasonal differences of TNQ10 between C4I and gridded observations TNQ10 is generally underestimated C4I: the winter spring and autumn similar patterns,  more pronounced in winter. In contrast, during summer, the index is underestimated (KNMI best simulations, while CNRM shows large negative biases.)

  12. Evaluation of Regional Climate Models Winter differences of FD between models and gridded observations. C4I and KNMI: Similar patterns in all seasons underestimating the index especially in winter and in high altitude regions

  13. Evaluation of Regional Climate Models Winter differences of PQ95 between models and gridded observations CNRM underestimates PQ95 more than the other two models All three models reveal wetter conditions along the western coast of the Balkan Peninsula in winter. Such wet conditions are also found in spring and autumn at the mountainous western part of the Balkans.

  14. Evaluation of Regional Climate Models Winter differences of PX5D between models and gridded observations All models overestimate PX5D in large parts of the study region C4I and KNMI present similar patterns in winter spring and autumn with increased values for the index, in several scattered sub-regions over the Iberian Peninsula and the western coast of the Balkan Peninsula. CNRM shows drier conditions compared to the other two models.

  15. Evaluation of Regional Climate Models Summer differences of CDD between models and gridded observations CDD is underestimated in the majority of the study region (negative differences). Summer  the KNMI model displays a reverse behaviour with large positive differences (overestimation) especially in the entire Italian and Balkan Peninsula, as well as in the northern part of Turkey.

  16. Spatial analysis of Climate extreme Indices Summer TXQ90 as estimated by KNMI for the future periods 2021-2050 (left) and 2071-2100 (right) Mediterranean will get warmer in the future, especially during 2071-2100. KNMI expects the heat to get worse by the end of the 21st century for summer, and estimates that the majority of the study region will frequently experience temperatures of up or greater to 40oC

  17. Spatial analysis of Climate extreme Indices Number of winter frost days (FD) estimated by all models for the reference and future periods. Decrease of the index most evident in central and western Mediterranean. CNRM is the “coldest” model

  18. Spatial analysis of Climate extreme Indices Winter PQ95 as estimated by all models for the reference and future periods Future projection do not show changes in the patterns: more intense in the last 30 years of the century

  19. Spatial analysis of Climate extreme Indices Consecutive dry days (CDD) in summer as estimated by KNMI for the future periods 2021-2050 (left) and 2071-2100 (right). General swift to drier conditions is predicted by all models under consideration. KNMI presents the longest maximum dry spells. According to this model, it seems that in the future, the southern Iberian Peninsula and Greece will be characterised by a persisting absence of rainfall, since the length of the dry spells approaches 90 days

  20. Maximum and minimum values of the Indices: TXQ90 all seasons The models reveal some sort of “persistence” regarding regions where the extreme values of the indices are found! The extremes occur in the lower part of each zone of latitude

  21. Maximum and minimum values of the Indices: TNQ10 (extreme minimum values) winter and summer Agreement for all seasons for the present and future periods and for all models. Three distinguished regions: Morocco, southern France and eastern Turkey

  22. Maximum and minimum values of the Indices: PQ95 and Px5d winter The extreme values display relatively similar spatial distribution The extreme index values are observed in the same areas for both reference and future period.

  23. Maximum and minimum values of the Indices: CDD Summer. In winter and spring  spatial inconsistency among models results Extreme summer values of the CDD are observed in the easternmost parts of the study area, and in low latitudes of each zone

  24. Trend analysis of Climate Indices Trends of TQX90 for summer as estimated by the three examined models. Trends are statistically significant at 0.05 level of significance Generally positive trends of TXQ90 are seen in the Mediterranean The highest positive trends are found inland, especially for summer, particularly by CNRM and KNMI. In many cases the positive trends exceed the 0.5oC per decade.

  25. Trend analysis of Climate Indices Trends of TQN10 for winter and spring for the three models. Trends are statistically significant at 0.05 level of significance Generally all models show positive trends, with the highest increasing trends during summer (0.4-0.5oC / decade). Maximum of the positive trends in the eastern Mediterranean for all seasons

  26. Trend analysis of Climate Indices Trends of PQ95 for summer and autumn as estimated by KNMI for the 1951-2100 period. Trends are statistically significant at 0.05 level of significance. Less spatial coherence describes the PQ95 trend results, as both positive and negative trends are observed.Negative summer trends cover large part of the study area, indicating that extreme precipitation tends to decrease during the warm part of the year by the end of the 21st century. In contrast this model shows that intense precipitation episodes should be more often expected in autumn

  27. Trend analysis of Climate Indices Trends of PX5D as estimated by KNMI for all seasons for the 1951-2100 period. Trends are statistically significant at 0.05 level of significance. No clear signal.Positive and negative trends are observed for all models and all seasons. Winter KNMI Positive trends at the northern parts of the Iberian, Italian and Balkan Peninsulas

  28. Conclusions • All models underestimate extreme warm temperatures (TXQ90). • The extreme cold temperatures (TNQ10) seemed to be better reproduced especially by KNMI. • The ‘coldest’ model was found to be CNRM, particularly for low temperatures of the transitional seasons. Also more frost days especially in the eastern part of the Mediterranean. • C4I simulates better the low temperatures for spring and autumn than in the other two seasons and high skill for HWD index. • All models  lower skill in simulating precipitation indices. • PQ95 was reproduced better than PX5D • Models show drier conditions, than those defined by the observational data. • Despite the dry characteristics of models, they underestimate the CDD index.

  29. Conclusions • All models marked a shift towards warmer climates • High temperatures (TXQ90) getting warmer in the future. • HWD increase in future summers. • Increase in summer low temperatures (TNQ10). • KNMI and C4I present reduced number of frost days (spring and autumn) • Precipitation indices the models present similar present and future spatial patterns as regards extremes precipitation amounts (PQ95) in winter • the most extreme precipitation observed along the western boarders of all the peninsulas of northern Mediterranean. • CNRM is found to be the drier among models, • KNMI predicts larger dry periods (CDD) in the future, which seem to be more pronounced in the eastern part of the basin.

  30. Conclusions • Large positive trends for both extreme high and low (TXQ90, TNQ10) temperatures • Trends seem to be getting larger in summer • Precipitation trends no clear picture for the future behaviour of precipitation extremes. • All models showed some increasing trends in winter extreme precipitation amounts (PQ95) (northern areas of the domain) • Positive trends  winter consecutive dry days (CDD). • HWD large positive trends in the eastern Mediterranean (CNRM) • Frost days  negative trends in all seasons.

  31. Conclusions • The models appeared sensitive to define regions vulnerable to experience extremes in both present and future periods. In most cases the three models marked the same grids having the maximum (or minimum) values of indices. Models generally agreed regions showing extremes in present are the most vulnerable to experience extreme climate events in the future too.

  32. Thank you! ACKNOWLEDGEMENTS: Work was funded by the European Commission, as part of the ENSEMBLESProject(Contract number GOCE-CT-2003-505539)

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