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Climate variability, trends and scenarios for Mexico and Argentina .

Climate variability, trends and scenarios for Mexico and Argentina. Cecilia Conde, Marta Vinocur, Carlos Gay, Roberto Seiler. AIACC LA-29. Integrated Assessment of Social Vulnerability and Adaptation to Climate Variability and Change Among Farmers in Mexico and Argentina.

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Climate variability, trends and scenarios for Mexico and Argentina .

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  1. Climate variability, trends and scenarios for Mexico and Argentina. Cecilia Conde, Marta Vinocur, Carlos Gay, Roberto Seiler. AIACC LA-29 Integrated Assessment of Social Vulnerability and Adaptation to Climate Variability and Change Among Farmers in Mexico and Argentina

  2. Uribe, 2002. Douglas, 1993. Douglas, 1993. Palma, 2004

  3. Central Region Veracruz 1 event > +1 std 12 Events > +1 std 8 events <-1std 3 events <-1std 1 event > +1 std 7 events > +1 std 7 events<-1std 6 events<-1std

  4. Córdoba Province Argentina Study area

  5. IPCC vs Observed data (3 stations)Southern Córdoba Pcp. Obs. DEF. r = 0.86

  6. Pcp Laboulaye, 1961 -2003

  7. Risk space. Veracruz. JJA

  8. Risk space. Laboulaye, Cba.

  9. Some advantages of these Climatic Risk Spaces • Relation between climate – specific crops • Allows us to differentiate seasonal climatic impacts from other stressors • Relation to current governmental programs (example: FAPRACC, Mexico). • Helps communication. Decision makers and regional experts. • Helps to decide between climate change scenarios.

  10. Uncertainties • Spatial: Regional, local? • Temporal: annual, seasonal, monthly, daily data (frost, hail, strong winds)? Future? • “Risk” to whom? to what? Different crop sensitivity

  11. Climate Change scenarios • Magicc /Scengenoutputs • SRES: A2 and B2 • Medium and High Sensitivity • Echam, Hadley, GFDL • 2020, 2050 (monthly and seasonal) • Temperature and Precipitation • Simple interpolation in 1ºx1º grid (Mexico). • For study sites: scatter plots (simple interpolation) • Downscaling techniques for Veracruz (Mexico). No SRES. 2xCO2 C. Conde, A. Tejeda, C. Gay, O. Sánchez*, R. Araujo, B. Palma, Vinocur.

  12. Selected GCMs • ECHAM model: Lowest differences with observed data. México (Magaña, 2003;Conde, 2003). • GFDL (and CC) models: used in Country Study: Mexico(1994 – 1996) • HADLEY model: used in LA • These models are used also for Córdoba, Argentina, as suggested by LA-26

  13. Downscaling. JJA. GFDL Temperature Base Scenario • T = F(Z). (Used for electricity rates) • T0corr=- k1 – k2 Z + k3 T1Model • r = 0.966; r2=93.4 • Tcorr = b1T Palma, B. 2004

  14. Examples for Mexico. (12%,- 8%) (16%, 8%) (-8,-2) Sánchez, Araujo, Conde “user friendly” ECHAM98. A2 MES. 2020. PRECIPITATION. JULY

  15. MEXICO. Temperature Climate Change Scenarios. A2, B2. 2020, 2050. 3 GCMs. July

  16. ARGENTINA. Temperature Climate Change Scenarios. A2, B2. 2020, 2050. 3 GCMs. Jan.

  17. MEXICO. Precipitation Climate Change Scenarios. A2, B2. 2020, 2050. 3 GCMs. July

  18. Argentina. Precipitation Climate Change Scenarios.A2, B2. 2020, 2050. 3 GCMs. Jan.

  19. Decisions? Which of the multiple combinations represent future climatic risk? Or an opportunity? Pcp: -35% to +40% T: 1.5ºC to 3.8ºC

  20. “Risk Space”. Veracruz. 2020

  21. What about changes in variability? Summer Temperature 1969-2050 E=Echam, H=Hadley, sm=Clim Sen. Med., sa=Clim. Sen. High, trend=tendency (aleatory numeric generator). Gay,C., F. Estrada, C. Conde, 2004

  22. Conclusions • Regional climatic variability and trends analysis helps defining climatic risk • Climatic “risk spaces” can be use as a tool to communicate risk, related to crops and defining other stressors. • Regional climate change scenarios can be compared to “risk spaces” to define future climatic risk and/or opportunities. • Changes in climate variability are fundamental for agriculture

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