Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles. Maiorano, A., Martre, P., Asseng, S., Ewert, F., Mueller, C., Rotter, R., Ruane, A., Semenov, M., Wallach, D., Wang, E., Alderman, P., Kassie, B., Biernath, C., Basso, B., Cammarano, D., Challinor, A., Doltra, J., Dumont, B., Rezaei, E., Gayler, S., Kersebaum, K., Kimball, B., Koehler, A., Liu, B., O’Leary, G., Olesen, J., Ottman, M., Priesack, E., Reynolds, M., Stratonovitch, P., Streck, T., Thorburn, P., Waha, K., Wall, G., White, J., Zhao, Z., & Zhu, Y. Field Crops Research, 202:5–20, 2017. MACSUR or FACCE acknowledged.
doi  abstract   bibtex   
To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMES) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT worldwide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 degrees C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMES to be used effectively. (C)2016 Elsevier B.V. All rights reserved.
@Article {Maiorano2017,
author = {Maiorano, A. and Martre, P. and Asseng, S. and Ewert, F. and Mueller, C. and Rotter, R.P. and Ruane, A.C. and Semenov, M.A. and Wallach, D. and Wang, E. and Alderman, P.D. and Kassie, B.T. and Biernath, C. and Basso, B. and Cammarano, D. and Challinor, A.J. and Doltra, J. and Dumont, B. and Rezaei, E.E. and Gayler, S. and Kersebaum, K.C. and Kimball, B.A. and Koehler, A.-K. and Liu, B. and O’Leary, G.J. and Olesen, J.E. and Ottman, M.J. and Priesack, E. and Reynolds, M. and Stratonovitch, P. and Streck, T. and Thorburn, P.J. and Waha, K. and Wall, G.W. and White, J.W. and Zhao, Z. and Zhu, Y.}, 
title = {Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles}, 
journal = {Field Crops Research}, 
volume = {202}, 
pages = {5--20}, 
year = {2017}, 
doi = {10.1016/j.fcr.2016.05.001}, 
abstract = {To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMES) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT worldwide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39\% in the calibration data set and by 26\% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 degrees C. MME mean squared error in simulating grain yield decreased by 37\%. A reduction in MME uncertainty range by 27\% increased MME prediction skills by 47\%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMES to be used effectively. (C)2016 Elsevier B.V. All rights reserved.}, 
note = { MACSUR or FACCE acknowledged.}, 
keywords = {Impact uncertainty, High temperature, Model improvement, Multi-model ensemble, Wheat crop model; TRITICUM-AESTIVUM-L; CLIMATE-CHANGE; SIMULATION-MODEL; SPRING WHEAT; NITROGEN UPTAKE; WINTER-WHEAT; YIELD; GROWTH; WATER; IMPACTS}, 
type = {CropM}}
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