Uncertainties of climate change impacts in agriculture

1. Uncertainties of climate change impacts in agriculture Senthold Asseng F. Ewert, P. Martre, R.P. Rötter, D.B. Lobell, D. Cammarano, B.A. Kimball, M.J. Ottman, G.W. Wall, J.W. White, M.P. Reynolds, P.D. Alderman, P.V.V. Prasad, P.K. Aggarwal, J. Anothai, B. Basso, C. Biernath, A.J. Challinor, G. De Sanctis, J. Doltra, E. Fereres, M. Garcia-Vila, S. Gayler, G. Hoogenboom, L.A. Hunt, R.C. Izaurralde, M. Jabloun, C.D. Jones, K.C. Kersebaum, A.-K. Koehler, C. Müller, S. Naresh Kumar, C. Nendel, G. O’Leary, J. E. Olesen, T. Palosuo, E. Priesack, E. EyshiRezaei, A.C. Ruane, M.A. Semenov, I. Shcherbak, C. Stöckle, P. Stratonovitch, T. Streck, I. Supit, F. Tao, P. Thorburn, K. Waha, E. Wang, D. Wallach, J. Wolf, Z. Zhao and Y. Zhu

2. Overview • AgMIP • Crop models – modeling CO2 • Model uncertainty • What is it? • Quantification • Comparison with other sources • Can it be reduced? • Conclusions

3. AgMIP Agricultural Model Intercomparison and Improvement Project Led by Cynthia Rosenzweig, James W. Jones, Jerry Hatfield & John Antle • Goals • To improve the characterization of risk of hunger and world food security due to climate change, • To enhance adaptation capacity in both developing and developed countries. www.agmip.org

4. AgMIP Wheat AgMIP 30 wheat models • AgMIP : • combines climate – crop – economic models in a multi-model approach • started in 2010, open, > 600 members from around the world, >30 projects AgMIP Wheat Rosenzweig et al. 2013 AFM

5. Wheat yield and climate Temperature H2O Light CO2 Management Genotype Carter 2013 Soil

6. Scale Crop models E x M P G

7. APSIM - NWheat C Nwheat SoilN SoilWAT

8. Model output

9. Models vs observations Model input output observation outputobservation 1. wrong input 2. wrong/poor estimate for input 3. wrong observation 4. wrong model/routine a) wrong number b) wrong unit c) value with large variability d) outside model design c) ‘not a measurement’ - just another ‘model’

10. Modeling CO2

11. Photosynthesis leaf cell CO2 H2O CO2 + H2O + light energy ---> C6H12O6 + O2 + H2O

12. Simple approaches to compute Photosynthesis: RUE - model RUE Sinclair and Weiss (2010) In: Principles of Ecology in Plant Production Monteith1977 PTRSL

13. Climate change - Photosynthesis leaf CO2 H2O CO2 + H2O + light energy ---> C6H12O6 + O2 + H2O • Radiation use effciency (RUE) and transpiration effciency (TE) • both increases with increased CO2

14. RUE model RUE = Radiation use efficiency in g[crop] MJ-1[intercepted light] dW/dt = RUE xFCO2x I0x [1-exp(-k . LAI)] incoming light interception FCO2 Reyanga et al. 1999 EMS

15. Observed grain yield – CO2 effect Grain Yield (t/ha) +CO2 = 550ppm (by 2050) Observed data after Kimball et al. 1995 GCB dry dry+CO2 wet+CO2 wet

16. Observed grain yield – CO2 effect Grain Yield (t/ha) +CO2 = 550ppm (by 2050) Observed data after Kimball et al. 1995 GCB low N low N+CO2

17. Observed & simulated grain yield – CO2 effect Grain Yield (t/ha) observed & simulated Asseng et al. 2004 FCR dry dry+CO2 wet+CO2 wet low N low N+CO2

18. Uncertainty

19. Modeling climate impact Climate models • Climate models (+scenarios) • e.g. Crop model • (or model for: • - hydrology, • biodiversity, • health…) • e.g. Economic model (or model for: • land-use…) Climate models Impact model Impact model Impact model

20. A meta-analysis of crop yields (wheat) Challinor et al. 2014 Nature CC

21. Distinguishing variability from uncertainty Due to model, process, measurements errors Variability = Natural variability in space & time e.g. impact simulation Lehmann & Rillig 2014 Nature CC

22. Distinguishing variability from uncertainty Natural variability Uncertainty After Lehmann & Rillig 2014 Nature CC

23. AgMIP Wheat - Background Crop model = main tool to assess climate change impact But, simulated effect due to chosen crop model ?

24. AgMIP Wheat Pilot • 27 wheat models • 4 contrasting field experiments (natural variability) • Standardized protocols • “Blind test” • Full calibration • Sensitivity analysis

25. AgMIP Wheat Pilot 27 wheat models 4 contrasting field experiments Wheat area after Monfreda et al. (2008) CIMMYT’s mega-environments (ME) for wheat ME 11, High rainfall; cold temperature, winter wheat ME 2, High rainfall; temperate temperature, spring wheat ME 1, Irrigated; temperate temperature, spring wheat ME 4, Low rainfall; temperate temperature, spring wheat

26. Observations versus simulations Line = median Box = 50% Bars = 80% Asseng et al. 2014 Nature CC

27. Observations versus simulations Fully calibrated “Blind” 13.5% = coefficient of variation for field experimental observation (Taylor et al. 1999) Asseng et al. 2014 Nature CC

28. Observations versus simulations “Blind” Asseng et al. 2014 Nature CC Fully calibrated

29. Model detail

30. Model detail Relative RMSE (%) Challinor et al. 2014 Nature CC

31. Model response to changes in T, rainfall and CO2 to climate change scenario (A2 2100) “Blind” fully calibrated 50% of models with the closest simulations to the observed yields across all location 50% of models with closest simulation per location • e.g. the best models (i.e. smallest RMSE with observations) have smallest CV at 3 locations, but not at AU; • i.e. performance of models with historical data is no guidance for future impact studies Asseng et al. 2014 Nature CC

32. Model response to rainfall Argentina Australia Line = median Box = 50% Bars = 80% Asseng et al. 2014 Nature CC

33. Model response to CO2 and T Argentina Australia CO2 response: Amthor 2001, Ewert et al. 2002, Hogy et al. 2010, Kimball 2011, Ko et al., 2010, Li et al. 2007 T response (extrapolated): Amthor 2001, Singh et al. 2008, Xiao et al. 2005 Simulated % yield change Line = median Box = 50% Bars = 80% Asseng et al. 2013 Nature CC

34. Model response to heat stress 7 x 35 oC after anthesis Line = median Box = 50% Bars = 80% Models with heat stress routine Asseng et al. 2013 Nature CC

35. What about other crops?

36. Maize model response 23 models Bassu et al. 2014 GCB

37. Crop models vs GCMs

38. Modelling climate impact Climate models • Climate models (+scenarios) • e.g. Crop model • (or model for: • - hydrology, • biodiversity, • health…) Impact model

39. Impact uncertainties A2 scenario for Mid-Century Model uncertainty in simulating climate change yield impact Mean exp CV% (Taylor et al. 1999) Uncertainty due to 16 GCM’s scenarios Asseng et al. 2014 Nature CC

40. Reducing uncertainty

41. Multi-model ensembles to reduce uncertainty 13.5% = Mean exp CV% (Taylor et al. 1999) Asseng et al. 2014 Nature CC

42. Multi-model ensembles to reduce uncertainty Colors represent different CO2 levels Mean (+/- STD) of all locations Asseng et al. 2014 Nature CC (13.5% = Mean exp CV% (Taylor et al. 1999))

43. Reducing uncertainty via model improvements

44. Model improvements to reduce uncertainty PD Alderman, E Quilligan, S Asseng, F Ewert and MP Reynolds (Editors) CIMMYT, El Batan, Texcoco, Mexico June 1921, 2013 Bruce Kimball Wall et al. 2011 GCB; Ottman et al. 2012 AJ • Improve high temperature impacts in models

45. Conclusions • Many of the crop models can reproduce observed experiments • However, there is an uncertainty in climate change impact assessments due to crop models • This uncertainty is similar to experimental error, but larger than from GCM’s • Uncertainty in modeling T and T x CO2 interactions • >>> model improvements • Multi-model ensembles can reduce simulated impact uncertainties. Contact: Senthold Asseng, sasseng@ufl.edu