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

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

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. Led by Cynthia Rosenzweig, James W. Jones, Jerry Hatfield & John Antle

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 AFM

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

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

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

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

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’

Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014 Modeling CO 2

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

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

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

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 EMS incoming light interception

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 GCB +CO 2 = 550ppm (by 2050)

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 GCB +CO 2 = 550ppm (by 2050) Observed grain yield – CO 2 effect

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 FCR Observed & simulated grain yield – CO 2 effect

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

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

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

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

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

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 ?

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

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

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 Nature CC

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

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

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

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

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 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

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 Nature CC

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 Nature CC Impact model uncertainty

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 T response (extrapolated): Amthor 2001, Singh et al. 2008, Xiao et al Argentina Australia Line = median Box = 50% Bars = 80% Asseng et al Nature CC

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 Nature CC

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

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

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

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

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 Nature CC

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

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 Nature CC

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 Nature CC

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

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 GCB; Ottman et al AJ

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,