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Statistical Downscaling Methods for Future Climate Prediction in Greece

This study focuses on estimating mean maximum summer and mean minimum winter temperatures in Greece for 2070-2100 using statistical downscaling methods. General Circulation Models (GCMs) and Statistical Downscaling (Sd) models are employed to simulate and predict climatic parameters. The validation results show good agreement between observed and estimated temperatures.

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Statistical Downscaling Methods for Future Climate Prediction in Greece

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  1. Estimation of mean maximum summer and mean minimum winter temperatures over Greece in 2070- 2100 using statistical downscaling methods Anastasios Skourkeas1, Fotini Kolyva – Machera1, Pangiotis Maheras2 1Section of Statistics and Operation Research, Mathematics Department, Aristotle University of Thessaloniki. 2Department of Meteorology and Climatology, School of Geology, A.U.Th. 2nd International Scientific Conference ” Energy and Climate Change” 8 – 9 October 2009, Athens, Greece

  2. Goal of the study - Contents estimation of one of the most interesting climatic parameter (mean maximum summer and mean minimum winter temperatures over Greece (20) stations) in 2070-2100, by applying statistical downscaling methods.

  3. General Circulation Models (GCMs) • simulation of the present/future statistics • capable of simulating the global climate • response to the solar forcing, earth rotation etc. • resolutions of hundreds of kilometres • incapable of simulating the regional climate • response of the global climate to regional details • may be as fine as tens of kilometres • complex topography, coastal/island locations, highly heterogeneous land-cover (such as Greece)

  4. Statistical Downscaling methods (Sd) large-scale circulation statistical downscaling models local climates Main Assumptions • relevant to the target predictands • accurate representation by climate models predictors : relationship between the predictors & predictands remains valid for periods outside the fitting period.

  5. The Sd model (data description ) • Predictors (or independent variables) • large scale 1000hPa-500hPa thickness field (NCEP-NCAR) • cover the extended European area 0ο- 32.5ο E and 30ο- 55ο N with a spatial resolution 2.5ο×2.5ο, for the period 1958 to 2000. Predictands (or dependent variables) Mean maximum summer & mean minimum winter temperatures for 20 stations over Greece (National Meteorological Service) for the period 1958 to 2000.

  6. The Sd model (data description ) control run period [1960(61)-1990] predictors : thickness field data (1000-500) hPa from GCM HadAM3P future period [(2070(71)-2100] predictors : thickness field data (1000-500) hPa from IPCC-SRES A2 & B2 IPCC-SRES A2 : increase of CO2 715 ppm [93.2%] summer / winter : predictors : p=154 grid points predictands : q=20 stations period : 1958(59)-2000 n=43 / 42 IPCC-SRES B2 : increase of CO2 562 ppm [52%]

  7. The Sd model (mathematics) ϱ22: the covariance matrix of the standardized dependent variables Q : columns : the eigenvectors of ϱ22 L2(-1/2) : diagonal matrix with eigenvalues of CCA B : canonical weights of the dependent variables V : the estimated scores from regression

  8. Results (the validation period, 1979-1993 ) Correlation coefficients between the observed and the estimated temperatures coefficients (s) > coefficients (w) calibration period (1958-1978 & 1994-2000) validation period (1979-1993)

  9. Results (the validation period, 1979-1993 ) Mean differences between the observed and the estimated temperatures summer mean differences were not found to be statistically significant at 5%

  10. Results (the validation period, 1979-1993 ) Mean differences between the observed and the estimated temperatures winter mean differences were not found to be statistically significant at 5%

  11. Results (the validation period, 1979-1993 ) Variances between the observed and the estimated temperatures summer • variability is underestimated • variance ratios are not statistically significant at 5%

  12. Results (the validation period, 1979-1993 ) Variances between the observed and the estimated temperatures winter • variability is underestimated • variance ratios are statistically significant at 5% (stations of western Greece and southern Peloponnese) • Mean differences : • summer & winter well reproduced • Variances : • better reproduction in summer

  13. Results (the control – run period, 1960(61) – 1990 ) Mean differences between the observed and the simulated (GCM HadAM3P) temperatures st. significant differences at 16/20 stations, with the highest value in Athens and the lowest in Agrinio calibration period (1958-2000) summer validation period (1960-1990)

  14. Results (the control – run period, 1960(61) – 1990 ) Mean differences between the observed and the simulated (GCM HadAM3P) temperatures • better simulation in winter • no statistically significant differences at 5% • range of winter smaller than the summer range winter

  15. Results (the control – run period, 1960(61) – 1990 ) Variances between the observed and the simulated (GCM HadAM3P) temperatures summer • variances are underestimated except for Mytilini • no statistically significant variance ratios at 5%

  16. Results (the control – run period, 1960(61) – 1990 ) Variances between the observed and the simulated (GCM HadAM3P) temperatures winter • variances are underestimated • statistically significant variance ratios at 5% • poor reproduction • Mean differences : • better simulation in winter • Variances : • better reproduction in summer

  17. Results (the future period, 2070(71)-2100 ) predictors : • thickness field data (1000-500) hPa from IPCC-SRES [A2] • increase of present greenhouse gas to 715 ppm) • continuously increasing population • thickness field data (1000-500) hPa from IPCC-SRES [B2] • increase of present greenhouse gas to 562 ppm) • local solution to economics and environmental sustaininability

  18. Results (the future period, 2070(71)-2100 ) • Mean differences between the estimated (future) and the simulated (GCM HadAM3P) temperatures • summer, IPCC-SRES A2 • average temp. response 3.3°C • range (1.8°C - 4.9°C) • maximum value in Athens • Kozani, Milos, Tripoli over 4°C • minimum value in Rhodes calibration period (1958-2000) validation period (2070-2100)

  19. Results (the future period, 2070(71)-2100 ) • Mean differences between the estimated (future) and the simulated (GCM HadAM3P) temperatures • summer, IPCC-SRES B2 • average temp. response 2.2°C • range (1.2°C – 3.2°C) • maximum value in Athens • minimum value in Rhodes • Alexandroupoli (2.3°C & 2.5°C) • same order of the stations • except for Alexandroupoli

  20. Results (the future period, 2070(71)-2100 ) • Mean differences between the estimated (future) and the simulated (GCM HadAM3P) temperatures • winter, IPCC-SRES A2 • average temp. response 1.2°C • range (0.8°C – 1.7°C) • maximum value in Alexandroupoli • & Ierapetra • minimum value in Tripoli • Skyros & Tripoli under 1°C

  21. Results (the future period, 2070(71)-2100 ) • Mean differences between the estimated (future) and the simulated (GCM HadAM3P) temperatures • winter, IPCC-SRES B2 • average temp. response 0.9°C • range (0.6°C – 1.2°C) • max value in Alexandroupoli • minimum value in Tripoli • all the stations (exc: • Agrinio, Alexandroupoli, • Ierapetra & Larissa) under 1°C

  22. Results (the future period, 2070(71)-2100 ) • Variances between the estimated (future) and the simulated (GCM HadAM3P) temperatures • summer, IPCC-SRES A2, IPCC-SRES B2, GCM • scenario A2 • variability is overestimated • (exc: Agrinio, Ioannina & Kalamata) • variance ratios are • statistically significant at 5% in Milos, Naxos & Skyros • scenario B2 • variability is underestimated • variance ratios are • statistically significant at most of all the stations

  23. Results (the future period, 2070(71)-2100 ) • Variances between the estimated (future) and the simulated (GCM HadAM3P) temperatures • winter, IPCC-SRES A2, IPCC-SRES B2, GCM • scenario A2 • variance ratios are not • statistically significant at 5% • scenario B2 • variability is overestimated • variance ratios are not • statistically significant at most of all the stations variances (w) are reproduced better than variances (s)

  24. Conclusions • For the training period 1979-1993 • CCA performed sufficiently • reproduce very well both the mean & standard deviation of the local variables • correlation coefficients greater 0.7 (esp. summer) • For the control run period 1960-1990 • winter temperatures better reproduced • variances (summer) better simulated • variances (summer & winter) are underestimated • For the future period 2070-2100 (st. relationships remain valid) • Response temp. 3.3°C (2.2°C) for summer, using IPCC-SRES A2 (B2) • Response temp. 1.2°C (0.9°C) for winter, using IPCC-SRES A2 (B2) winter results found better than in summer FUTURE WORK Optimize the reproductions of the variability

  25. 2nd International Scientific Conference ” Energy and Climate Change” 8 – 9 October 2009, Athens, Greece Thank you for you attention !

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