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 12 Events > +1 std 3 events <-1std 1 event > +1 std 8 events <-1std 7 events > +1 std1 event > +1 std 6 events<-1std7 events<-1std

4. Argentina Study area Córdoba Province

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

6. Pcp Laboulaye, 1961 -2003

7. Risk space. Veracruz. JJA

8. Risk space. Laboulaye, Cba.

9. 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. Some advantages of these Climatic Risk Spaces

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

13. Downscaling. JJA. GFDL T = F(Z). (Used for electricity rates) T 0corr = - k1 – k2 Z + k3 T1 Model r = 0.966; r 2 =93.4  T corr = b 1  T Temperature Base Scenario Palma, B. 2004

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

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

20. “Risk Space”. Veracruz. 2020

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