1. Translating Climate Forecasts into Agricultural Terms: Advances and Challenges James Hansen, Andrew Challinor, Amor Ines, Tim Wheeler, Vincent Moron presented at the International Workshop on Climate Prediction and Agriculture – Advances and Challenges WMO, Geneva, 11 May 2005

2. Motivation Information relevant to decisions Ex-ante assessment for credibility and targeting Fostering and guiding management

3. Overview Six years ago –Dominance of historic analogs –Doubts about crop predictability Recent advances –The challenge, and potential approaches –Synthetic weather conditioned on climate forecasts –Use of daily climate model output –Statistical prediction of crop simulations –Downscaling and upscaling Opportunities and challenges –Embedding crop models within climate models –Enhanced use of remote sensing, spatial data bases –Robustness of alternative coupling approaches –Forecast assessment and uncertainty –Climate research questions

4. Six Years Ago: Dominance of Historic Analogs Advantages –Intuitive probabilistic interpretation –Accounts for any differences in “signal strength” –May incorporate useful higher-order statistics Concerns –Small sample size, confidence, artificial skill –Are differences in distribution real? –How to use with dynamic prediction systems without discarding information?

5. Six Years Ago: Doubts About Crop Predictability Spatial variability of rainfall limits predic- tability at farm scale Accumulation of error from SSTs, to local climatic means, to crop response Impact of wrong fore- cast on farmers’ risk Barrett, 1998. Am. J. Agric. Econ. 80:1109-1112

6. The Challenge Nonlinearities. Crop response to environment can be nonlinear, non-monotonic. Dynamics. Crops respond not to mean conditions but to dynamic interactions: –Soil water balance –Phenology The scale mismatch problem.

7. The Scale Mismatch Problem Crop models: –Homogeneous plot spatial scale –Daily time step (w.r.t. weather) GCMs: –Spatial scale 10,000-100,000 km 2 –Sub-daily time step, BUT... Output meaningful only at (sub)seasonal scale –Tend to over-predict rainfall frequency, under-predict mean intensity Temporal scale problem more difficult than spatial scale.

8. Effect of Spatial Averaging Inverse-distance interpolation of daily weather data, north Florida, at a scale comparable to a GCM grid cell. Hansen & Jones, 2000. Agric. Syst. 65:43-72.

9. Effect of Spatial Averaging Spatial averaging distorts variability, increases frequency, decreases mean intensity. Similar spatial averaging occurs within GCM.

10. Effect of Spatial Averaging Simulated maize yields, CERES-Maize

11. Information Pathways predicted crop yields observed climate predictors ?

12. Information Pathways crop model analog years predicted crop yields observed climate predictors categorize

13. Information Pathways downscaled dynamic model stochastic generator crop model (hindcast weather) analog years predicted crop yields statistical climate model observed climate predictors categorize

14. Information Pathways downscaled dynamic model stochastic generator crop model (observed weather) crop model (hindcast weather) analog years predicted crop yields statistical climate model statistical yield model observed climate predictors categorize

15. Approaches Classification and selection of historic analogs (e.g., ENSO phases) Synthetic daily weather conditioned on forecast: stochastic disaggregation Statistical function of simulated response –Nonlinear regression –Linear regression with transformation or GLM –Probability-weighted historic analogs (Corrected) daily climate model output

16. Advances: Synthetic Weather Inputs Two Approaches: Adjusting generator input parameters: –Flexibility to produce statistics of interest –Assumed role of intensity vs. frequency Constraining generator outputs: –No assumptions re. frequency vs. intensity

17. Option 1: Conditioning input parameters Mean rainfall = frequency * mean intensity Conditioning intensity parameters: Conditioning frequency parameters: μ' = R' m / π α Adjust mixing probability α in hyperexponential intensity model π' = R' m / μ p 001, p 101, p 11 Adjust Markov transition probabilities p 001, p 101, p 11 in the occurrence model

18. Option 2. Constraining generated output First step: - - Iterative procedure – Using climatological parameters, accept the first realization with R m near target: |1-R m /R m,S | j <= 5% Second step: - - Apply multiplicative rescaling to exactly match target monthly target. Hansen & Ines, Submitted. Agric. For. Meteorol.

19. Constraining generator outputs reproduces correlations better than adjusting inputs. Tifton, Georgia Gainesville, Florida ScenarioR M vs. π R M vs. μ I μ I vs. π R M vs. πR M vs. μ I μ I vs. π Observed daily rainfall 0.6490.577-0.165 0.6680.7060.046 Disaggregated monthly rainfall constrain R M 0.6810.676-0.004 0.6490.6970.014 condition π0.8220.4730.013 0.8310.1210.052 condition μ I 0.4910.8560.071 0.4580.8370.052

20. Constraining generator output requires fewer replicates for given accuracy. GainesvilleKatumani constrain R M condition μ I condition π d b f ac e Tifton Number of realizations

21. RmRm π Maize simulated from disaggregated monthly GCM hindcasts, Katumani, Kenya

22. Advances: Use of Daily Climate Model Output Options Calibrate simulated yields Challinor et al., 2005. Tellus 57A:198-512 Correct GCM mean bias –Additive shift for temperatures –Multiplicative shift for rainfall Rainfall frequency-intensity correction Ines & Hansen, In preparation

23. 1 F(x GCM =0.0) F(x hist =0.0) 0 0 GCM Historical Correcting Bias in Daily GCM Output: Rainfall Frequency calibrated threshold

24. Correcting Bias in Daily GCM Output: Rainfall Intensity GCM Historical 0 Daily rainfall (x), mm 1 0 0 F(x) x'ix'i F(xi)F(xi) xixi

25. RmRm μ π Katumani, Kenya ECHAM4 & observed OND daily rainfall (1970-95) Intensity corrections: EG: empirical (GCM) to gamma (observed) GG: gamma (GCM and observed) Corrects rainfall total, frequency, intensity.

26. Predicts yields from GCM, perhaps better than stochastic disaggregation CERES-Maize simulated with: Disaggregated MOS-corrected monthly hindcasts Gamma-gamma transformation of daily rainfall

27. Advances: Statistical Prediction of Crop Simulations Seasonal predictors of local climate potential predictors of crop response Predictand: Yields simulated with observed weather Eliminates need for daily weather conditioned on climate forecast Poor statistical behavior

28. Nonlinear Regression Katumani maize prediction example: Yields as f(PC1) Mitscherlitch functional form: Cross-validation y=3.33+1.34(1-exp(-0.133x)) R 2 = 0.400

29. K Nearest Neighbor Unequally-weighted analogs Weights w: –Based on rank distance (predictor state space) –Interpreted as probabilities Forecast ŷ a weighted mean: Optimize k A non-parametric regression

30. Linear Regression & Transformation: Regional-Scale Wheat, Qld, Australia Wheat simulations: water satisfaction index ECHAM4.5, persisted SSTs, optimized (MOS) Yield prediction by c-v linear regression Box-Cox normalizing transformation Forecast distribution: –Regression residuals in transformed space –n antecedent X n within-season weather years Hansen et al., 2004. Agric. For. Meteorol. 127:77-92

31. Linear Regression & Transformation: Regional-Scale Wheat, Qld, Australia

32. Grain yield (Mg ha -1 ) Climatology Date of forecast ENSO phase 1989 (neutral) GCM-based 1982 (El Niño) 1988 (La Niña) 1Jun1Jul1AugHarvest1May1Jun1Jul1AugHarvest1May1Jun1Jul1AugHarvest1May Observed 90 th percentile 75 th percentile 50 th percentile 25 th percentile 10 th percentile

33. Advances: Downscaling & Upscaling Spatial climate downscaling: –Methods advancing –Uncertain impact on skill Crop model upscaling: –Understanding and methods for aggregating point models –Increasing set of reduced form large-area models Predictability (r) of groundnut yields with large area model, W India. Challinor et al., 2005. Tellus 57A:198-512 Obs. vs. pred. rainfall, Ceará, NE Brazil, as function of aggregation. Gong et al., 2003. J. Climate 16:3059-71.

34. Opportunities & Challenges: Crop Models Within Climate Models Run crop models within GCM or RCMs Allow crop to influence atmosphere –Alternative land surface scheme –Intended benefit is atmosphere response to crop Likely to require calibration of crop results for foreseeable future Match scale of climate model grid

35. Opportunities & Challenges: Remote Sensing, Spatial Data Bases Enhanced georeferenced soil, land use, cultivar data bases Assimilation of real-time, contiguous antecedent weather into forecasts Estimation of cropped areas, dates Correction of simulated state variables Eventual farm-specific crop forecasts?

36. r = 0.57 r = 0.53 r = 0.58 r = 0.55 Opportunities & Challenges Robustness of Alternative Approaches? Hansen & Indeje, 2004. Agric. For. Meterol. 125:143

37. Opportunities & Challenges: Forecast Assessment and Uncertainty Does predictability (climate and impacts) change from year to year? –Artifact of skewness? –Real impacts of climate state? –Captured by GCM ensembles? Interpretation of forecasts based on categorical vs. continuous predictors? Consistency of hindcast error vs. GCM ensemble distributions?

38. Are differences in dispersion real? Raw Transformed skewness1.243-0.032 p ENSO influence on : means 0.0001 *** 0.0004 *** dispersion 0.0001 *** 0.91 n.s. Junin, Argentina, 1934-2001

39. Opportunities & Challenges: Forecast Assessment and Uncertainty Does predictability (climate and impacts) change from year to year? –Artifact of skewness? –Real impacts of climate state? –Captured by GCM ensembles? Interpretation of forecasts based on categorical vs. continuous predictors? Consistency of hindcast error vs. GCM ensemble distributions?

40. Opportunities & Challenges: Climate Research Questions Past prediction efforts driven by skill –Relative shifts –Large areas –3-month climatic means Stimulating interest in “weather within climate” –Skill at sub-seasonal time scales –Higher-order rainfall statistics –Shifts in timing, onset, cessation –Methods to translate into weather realizations