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Climate Change Scenarios for Southeastern South America Mario Bidegain

Climate Change Scenarios for Southeastern South America Mario Bidegain Facultad de Ciencias – Univ.República - URUGUAY AIACC LA32/LA26 Ines Camilloni Facultad de Ciencias – Univ.Buenos Aires - ARGENTINA AIACC LA26 AIACC II Latin America/Caribbean Regional Workshop

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Climate Change Scenarios for Southeastern South America Mario Bidegain

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  1. Climate Change Scenarios for Southeastern South America Mario Bidegain Facultad de Ciencias – Univ.República - URUGUAY AIACC LA32/LA26 Ines Camilloni Facultad de Ciencias – Univ.Buenos Aires - ARGENTINA AIACC LA26 AIACC II Latin America/Caribbean Regional Workshop 24-27 August 2004, Buenos Aires, Argentina

  2. OUTLINE • What GCMs outputs were selected from IPCC DDC and why? • What SRES scenarios were selected? • What variables were selected for this study? • Climate Change scenarios for the south of South America

  3. Spatial Correlation for Sea Level Pressure between GCMs outputs and NCEP reanalysis

  4. Sea Level Pressure anomalies: GCMs vs.NCEP reanalysis CSIRO-NCEP (1961-2000) HADCM3-NCEP (1950-2000) NCEP (1950-2000) ECHAM4-NCEP (1990-2000)

  5. Spatial Correlation for Temperature between GCMs and University Delaware reanalysis

  6. Spatial Correlation for Precipitation between GCMs and Univ. Delaware reanalysis

  7. What GCM outputs were selected from IPCC DDC? ECHAM4 1990-2100 CSIRO-Mk2 1961-2100

  8. Global temperature change (2000-2100)

  9. Future temperature changes for the south of South America (HADCM3 SRES A2)

  10. Future temperature changes for the south of South America (ECHAM4 SRES A2) 2020 SRES-A2a 2050 SRES A2a 2080 SRESA2a

  11. Future precipitation changes for the south of South America (HADCM3 SRES A2) 2020 SRES-A2a 2050 SRES A2a 2080 SRESA2a

  12. Future precipitation changes for the south of South America (ECHAM4 SRES A2)

  13. Precipitation and Temperature Changes Argentina 4 GCMs (2070-99 vs. 1961-90)

  14. Precipitation and Temperature Changes Uruguay 4 GCMs (2070-99 vs. 1961-90)

  15. CONCLUSIONS • Good performance of GCMs in temperature (very good in winter) and SLP, poor in rainfall (underestimation in the southeast of SA), but improved performance respect to previous GCMs versions • After performance experiment 3 GCMs was selected to construct climate scenarios over region (HADCM3, ECHAM4 and CSIRO). • The selected variables are: precipitation, temperature and SLP (surface winds). • Statistical (SDSM) downscaling will be applied to improve the local climate change regional scenarios for selected locations. • Dynamical downscaling (PRECIS?), will be applied to improve future regional climate scenarios.

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