1. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 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. Eyshi Rezaei, 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. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Overview 1.AgMIP 2.Crop models – modeling CO 2 3.Model uncertainty a)What is it? b)Quantification c)Comparison with other sources d)Can it be reduced? 4.Conclusions

3. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 AgMIP Agricultural Model Intercomparison and Improvement Project 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 Led by Cynthia Rosenzweig, James W. Jones, Jerry Hatfield & John Antle

4. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 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 AgMIP AgMIP Wheat 30 wheat models Rosenzweig et al. 2013 AFM

5. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 CO 2 Light Temperature H2OH2O Management Carter 2013 Wheat yield and climate Genotype Soil

6. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Scale 6 Crop models P E x M G

7. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 APSIM - NWheat C SoilWAT SoilN Nwheat

8. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Model output

9. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Models vs observations Model input outputobservation 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. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Modeling CO 2

11. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Photosynthesis CO 2 + H 2 O + light energy ---> C 6 H 12 O 6 + O 2 + H 2 O CO 2 H 2 O leaf cell

12. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Monteith 1977 PTRSL Sinclair and Weiss (2010) In: Principles of Ecology in Plant Production Simple approaches to compute Photosynthesis: RUE - model RUE

13. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Climate change - Photosynthesis CO 2 + H 2 O + light energy ---> C 6 H 12 O 6 + O 2 + H 2 O CO 2 H 2 O leaf  Radiation use effciency (RUE) and transpiration effciency (TE) both increases with increased CO 2

14. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 RUE model RUE = Radiation use efficiency in g[crop] MJ -1 [intercepted light] dW/dt = RUE x F CO 2 x I 0 x [1-exp(-k. LAI)] F CO 2 Reyanga et al. 1999 EMS incoming light interception

15. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Grain Yield (t/ha) dry dry+CO 2 wet wet+CO 2 Observed grain yield – CO 2 effect Observed data after Kimball et al. 1995 GCB +CO 2 = 550ppm (by 2050)

16. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 low N low N+CO 2 Grain Yield (t/ha) Observed data after Kimball et al. 1995 GCB +CO 2 = 550ppm (by 2050) Observed grain yield – CO 2 effect

17. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 observed & simulated Grain Yield (t/ha) dry dry+CO 2 wet wet+CO 2 low N low N+CO 2 Asseng et al. 2004 FCR Observed & simulated grain yield – CO 2 effect

18. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Uncertainty

19. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Climate models Impact model Climate models (+scenarios) e.g. Crop model (or model for: - hydrology, - biodiversity, - health…) e.g. Economic model (or model for: - land-use…) Impact model Climate models Modeling climate impact

20. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Challinor et al. 2014 Nature CC A meta-analysis of crop yields (wheat)

21. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Lehmann & Rillig 2014 Nature CC Distinguishing variability from uncertainty Variability = Natural variability in space & time Due to model, process, measurements errors e.g. impact simulation

22. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 After Lehmann & Rillig 2014 Nature CC Distinguishing variability from uncertainty Natural variability Uncertainty

23. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 AgMIP Wheat - Background 1.Crop model = main tool to assess climate change impact 2.But, simulated effect due to chosen crop model ?

24. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014  27 wheat models AgMIP Wheat Pilot  4 contrasting field experiments (natural variability)  Standardized protocols “Blind test” Full calibration Sensitivity analysis

25. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 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 27 wheat models 4 contrasting field experiments AgMIP Wheat Pilot Wheat area after Monfreda et al. (2008) CIMMYT’s mega-environments (ME) for wheat

26. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Observations versus simulations Line = median Box = 50% Bars = 80% Asseng et al. 2014 Nature CC

27. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 13.5% = coefficient of variation for field experimental observation (Taylor et al. 1999) Observations versus simulations “Blind” Fully calibrated Asseng et al. 2014 Nature CC

28. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Observations versus simulations “Blind” Fully calibrated Asseng et al. 2014 Nature CC

29. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Model detail

30. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Model detail Relative RMSE (%) Challinor et al. 2014 Nature CC

31. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Model response to changes in T, rainfall and CO 2 “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 Asseng et al. 2014 Nature CC to climate change scenario (A2 2100)  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

32. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Model response to rainfall Argentina Australia Line = median Box = 50% Bars = 80% Asseng et al. 2014 Nature CC

33. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014  Model uncertainty increases with higher temperature 27 wheat models Asseng et al. 2013 Nature CC Impact model uncertainty

34. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Model response to CO 2 and T Simulated % yield change CO 2 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 Argentina Australia Line = median Box = 50% Bars = 80% Asseng et al. 2013 Nature CC

35. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Model response to heat stress Line = median Box = 50% Bars = 80% Models with heat stress routine 7 x 35 o C after anthesis Asseng et al. 2013 Nature CC

36. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 What about other crops?

37. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Bassu et al. 2014 GCB Maize model response 23 models

38. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Crop models vs GCMs

39. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Impact model Climate models (+scenarios) e.g. Crop model (or model for: - hydrology, - biodiversity, - health…) Climate models Modelling climate impact

40. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Impact uncertainties Uncertainty due to 16 GCM’s scenarios Mean exp CV% (Taylor et al. 1999) Model uncertainty in simulating climate change yield impact A2 scenario for Mid-Century Asseng et al. 2014 Nature CC

41. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Reducing uncertainty

42. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Multi-model ensembles to reduce uncertainty 13.5% = Mean exp CV% (Taylor et al. 1999) Asseng et al. 2014 Nature CC

43. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Multi-model ensembles to reduce uncertainty Colors represent different CO 2 levels (13.5% = Mean exp CV% (Taylor et al. 1999)) Mean (+/- STD) of all locations Asseng et al. 2014 Nature CC

44. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Reducing uncertainty via model improvements

45. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Model improvements to reduce uncertainty CIMMYT, El Batan, Texcoco, Mexico June 1921, 2013 PD Alderman, E Quilligan, S Asseng, F Ewert and MP Reynolds (Editors)  Improve high temperature impacts in models Bruce Kimball Wall et al. 2011 GCB; Ottman et al. 2012 AJ

46. Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Conclusions 1.Many of the crop models can reproduce observed experiments 2.However, there is an uncertainty in climate change impact assessments due to crop models 3.This uncertainty is similar to experimental error, but larger than from GCM’s 4.Uncertainty in modeling T and T x CO 2 interactions >>> model improvements 5.Multi-model ensembles can reduce simulated impact uncertainties. Contact: Senthold Asseng, sasseng@ufl.edu