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Building capacity to assess the impact of climate change/variability and

Building capacity to assess the impact of climate change/variability and develop adaptive responses for the mixed crop/livestock production systems in the Argentinean , Brazilian and Uruguayan Pampas. Principal Scientists  Graciela Magrin, INTA, Argentina

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Building capacity to assess the impact of climate change/variability and

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  1. Building capacity to assess the impact of climate change/variability and develop adaptive responses for the mixed crop/livestock production systems in the Argentinean , Brazilian and Uruguayan Pampas • Principal Scientists  • Graciela Magrin, INTA, Argentina • María I. Travasso, INTA, Argentina • Osvaldo Canziani, Argentina • Gilberto Cunha, Brazil • Mauricio Fernandes, Brazil • Agustin Gimenez, GRAS- INIA, Uruguay • Walter E. Baethgen, IFDC, Uruguay • Holger Meinke, APSRU, DPI, Australia

  2. AIACC Projects: “System” Under Current Conditions “System” Under Future Conditions IMPACTS ADAPTATION Future Conditions: Include Climate Change Scenarios Include Scenarios of Other Changes

  3. Climate Scenarios Great uncertainty of climate change at regional or local scales AIACC Globally: Produce Scientific Results (e.g., IPCC’s FAR ) AIACC Regionally and Locally: Introduce CC in the Political Agenda. (Planning and Decision Making) Uncertainty must be “managed” to still be useful

  4. Climate Scenarios Due to uncertainty of climate change at regional or local scales, we are considering a range of possible climates for the assessment of impacts of climate change on agricultural production including: • GCM outputs (based on IPCC SRES) • Sensitivity (ranges of +Temp, % Precip, Variability) • Project changes in the future considering changes in the last century • Generate daily data sets (for crop and pasture simulation models) IMPACTS and ADAPTATION

  5. Methods for Generating Climate Change Scenarios Needed for the Simulation Models (Crops, Pastures) GCMs: 1. Use Generated Climate Change Scenarios (Direct Output from GCM runs) (No) 2. Use RCM nested in GCM (hopefully) 3. Use GCM to get atmospheric variables and “generate” (e.g.) rainfall 4. Use Projected GCM Anomalies (Projected GCM – Climatology GCM) and Modify Observed Climatic Data Evaluate GCM’s Ability to Represent Current Conditions (Start with Climatology)

  6. Climate Scenarios 1. GCM outputs (based on IPCC SRES) 2. Sensitivity (ranges of +Temp, % Precip, Variability) SCENARIOS are a COMBINATION of: +1, 2 …. oC (different months) Tmax and Tmin +/- 10, 20 …. % Rainfall (different months) Changes in Means, Variability and Extremes

  7. Sensitivity (ranges of +Temp, % Precip, Variability) Pro: Range will likely cover reality Con: Range might be too wide to be useful

  8. Climate Scenarios 1. GCM outputs (based on IPCC SRES) • Sensitivity (ranges of +Temp, % Precip, Variability) 3. Project changes in the future considering changes in the last century

  9. Changes in CLIMATE during the 20th century in the Argentinean and Uruguayan Pampas Region (will add South Brazil)

  10. Differences in three-monthly Precipitation (mm) between 1900-1930 and 1970-2000 in nine sites of the Argentinean Pampas Region Mean Values

  11. Differences in three-monthly Precipitation (mm) between 1930 and 2002 in eleven sites of the Uruguayan Pampas Region Mean Values JAS OND JFM AMJ JAS OND JFM AMJ

  12. Differences in three-monthly Minimum Temperature (ºC) between 1950-1970 and 1970-2000 in nine sites of the Argentinean Pampas Region Mean Values

  13. Differences in three-monthly Minimum Temperature (ºC) between 1915-1950 and 1950-2000 in one site of the Uruguayan Pampas Region JAS OND JFM AMJ

  14. Differences in three-monthly Maximum Temperature (ºC) between 1950-1970 and 1970-2000 in nine sites of the Argentinean Pampas Region Mean Values

  15. Differences in three-monthly Maximum Temperature (ºC) between 1915-1950 and 1950-2000 in one site of the Uruguayan Pampas Region JAS OND JFM AMJ

  16. Differences in Number of Storms between 1911-1970 and 1980-2000 in eigth sites of the Argentinean Pampas Region >60mm >80mm >100mm >240% >350% >530%

  17. Changes in FROST Frequency and Intensity in eigth sites of the Argentinean Pampas Region

  18. ARGENTINA SUMMARY OF CHANGES

  19. URUGUAY SUMMARY OF CHANGES (1915-1970 vs 1980-2002)

  20. Changes in Crop yields During the 20th century Yield increases (%) between 1950-70 and 1970-00

  21. Characterize Climate Changes In the last Century to Generate “Synthetic” Weather Scenarios Projecting Observed Climate Changes

  22. Changes in median values between 1930-1960 and 1970-2000

  23. LARS-WG A Stochastic Wheather Generator for use in Climate Impact Studies M.A. Semenov

  24. Scenarios Comparison : Precipitation 1- Last 30 years 2- Synthetic serie 3- Future scenario (quarter)

  25. October Rainfall in SW Uruguay 1915 - 2002

  26. October Rainfall in SW Uruguay 1915 - 2002 Linear regression and Binomial smoothing

  27. October Rainfall in SW Uruguay 1915 - 2002 y = 0.74x - 1359 R2 = 0.49 (7.4 mm cada 10 años) Linear regression and Binomial smoothing

  28. Method 2: Characterize Changes in Statistical Parameters During the last Century Use TRENDS for Generating Future Climate Scenarios [SERIES WET and DRY] [WET and DRY series: mean and sd] [DISTRIBUTIONS OF RAIN] [RAIN MONTHLY max, min, N, mean and sd] [MAX MONTHLY max, min, N, mean and sd] [MAX DAILY max, min, N, mean and sd] [MIN MONTHLY max, min, N, mean and sd] [MIN DAILY max, min, N, mean and sd] [SPELLS of FROST and HOT TEMPERATURE] [WET MIN] [WET MAX] [DRY MIN] [DRY MAX]

  29. 1. Characterize changes in statistical parameters 2. Project the trend for the next 30 years

  30. Climate Scenarios • GCM outputs (based on IPCC SRES) • Sensitivity (ranges of +Temp, % Precip, Variability) • Project changes in the future considering changes in the last century

  31. Climatic Scenarios Past Changes GCM Sensitivity analysys (daily data) Crop/Pasture Models

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