The Analogues Methodology jointly developed by the Walker Institute and CIAT's DAPA program uses a modified Euclidean distance calculation to identify climate analogues under different scenarios. It facilitates farmer knowledge sharing and model validation. Key questions include identifying similar current locations to future sites (backward) and validating results given local variability. Grounding analogues in crop impacts and validating with crop models is important to support adaptation decisions.

1. The Analogues Methodology Julian Ramirez-Villegas

2. The method Jointly developed by the Walker Institute (University of Reading, UK) and the Decision and Policy Analysis program (DAPA) at CIAT Funded by CCAFS Theme 1: Adaptation to progressive climate change And collaborating with some other people in different institutions

4. Where can I find a place that currently looks like how my site would be in the future? (BACKWARD)

6. Permit validation of computational models and trialing of new technologies and techniques

7. Learning from history

8. Ground climate change impacts and adaptation studies

10. How do we calculate dissimilarity? The CCAFS measure: a modified version of the Euclidean distance m: time-step (e.g. month, day, quarter) v: number of variables V: Variable (i.e. temperature, rainfall, etc) W: Weight (any number or variable) f: reference scenario (e.g. future climate) p: target scenario (e.g. current climate) z: exponent (we normally assume 2) Lagging

11. How do we calculate dissimilarity? Accounting for lag Site A (southern) Site B (northern)

12. How do we calculate dissimilarity? The CCAFS measure: weights with other variables W: Weight value X: Variable value i: a given time step j: a given variable

13. How do we calculate dissimilarity? As with the example data, m: month (from 1 to 12) f: reference value (e.g. future climate) p: target value (e.g. present climate) z: exponent (we normally assume 2) T: Temperature P: Precipitation DTR: Diurnal temperature range m-lag: month that produces a maximum match of climate parameters (precip, temp)

14. How do we calculate dissimilarity? Hallegatte et al. (2007): a rule based dissimilarity measure Relative difference between total values less than “a” Mean absolute relative differences between mean values for m steps less than “b” Mean absolute difference between total values for m steps less than “c” m: time-step (e.g. month, day, quarter) V: Variable (i.e. temperature, rainfall, etc) W: Weight (any number or variable) f: reference scenario (e.g. future climate) p: target scenario (e.g. current climate) a, b, c: user defined parameters

17. Not so fast, though So we need to do it “through the eyes of the crop” Temperature and CO2 Avail. water and solar radiation Source: Bates, 2002 Source: Isdo et al., 1995 (sour orange trees)

21. If so, which variables do we need to use?

22. Rainfall + temperature?

23. Rainy days?

24. Days with extreme (very low, very high) temperatures?

25. Evapotranspiration?

26. Total thermal times?

27. Soils?

28. Varietal targeting and testing?

29. Cropland expansion/optimisation/migration?

32. How to calculate climate analogues online and via R

33. The different sensitivities of this method (variables, weights, direction)

35. A couple of clarifications… Similarity between EcoCrop response and other models