Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles
Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles
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DOI：
10.1016/j.fcr.2016.05.001
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发表时间：
2017-02-15
影响因子：
5.8
通讯作者：
Zhu, Yan
中科院分区：
文献类型：
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作者：
Maiorano, Andrea;Martre, Pierre;Zhu, Yan
为了改进气候变化影响估计并量化其不确定性，有人建议使用多模式集合。改进模型可以提高模拟的准确性，减少气候变化影响评估的不确定性。此外，它们可以减少MME中所需的模型的数量。在本文中，通过重新参数化和/或结合或修改热胁迫对物候、叶片生长和衰老、生物量生长以及谷物数量和大小的影响，使用来自USDA热系列谷物实验（校准数据集）的详细田间实验数据，改进了较大MME的15个小麦生长模型。然后，通过CIMMYT全球田间试验网络（评估数据集）的独立田间试验对模型改进前后的模拟结果进行评估。模型改进使MME模拟的谷物产量的变化（第10至第90模型集合百分位数范围）在校准数据集中平均降低了39%，在平均季节温度>24摄氏度的作物生长的独立评估数据集中平均降低了26%。MME模拟粮食产量的均方误差降低了37%。MME不确定性范围减少27%，MME预测技能增加47%。结果表明，在田间实验中观察到的变化的平均水平，并用作基准，可以达到一半的模型在MME. Improving作物模型的数量，因此，重要的是增加基于模型的影响评估的确定性，并允许更实际的，即更小的MMES被有效地使用。（C）2016 Elsevier B. V.保留所有权利。
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.