Strong regional influence of climatic forcing datasets on global crop model ensembles
Strong regional influence of climatic forcing datasets on global crop model ensembles
复制标题
DOI：
10.1016/j.agrformet.2020.108313
复制
发表时间：
2021-04
影响因子：
6.2
通讯作者：
A. Ruane;Meridel Phillips;C. Müller;J. Elliott;J. Jägermeyr;A. Arneth;J. Balkovič;D. Deryng;C. Folberth;T. Iizumi;R. C. Izaurralde;N. Khabarov;P. Lawrence;Wenfeng Liu;S. Olin;T. Pugh;C. Rosenzweig;G. Sakurai;E. Schmid;B. Sultan;Xuhui Wang;A. de Wit;Hong Yang
中科院分区：
文献类型：
--
作者：
A. Ruane;Meridel Phillips;C. Müller;J. Elliott;J. Jägermeyr;A. Arneth;J. Balkovič;D. Deryng;C. Folberth;T. Iizumi;R. C. Izaurralde;N. Khabarov;P. Lawrence;Wenfeng Liu;S. Olin;T. Pugh;C. Rosenzweig;G. Sakurai;E. Schmid;B. Sultan;Xuhui Wang;A. de Wit;Hong Yang
我们介绍了农业模型相互比较和改进项目（AgMIP）全球网格作物模型相互比较（GGCMI）第一阶段的结果，该阶段将14个全球网格作物模型（GGCM）和11个气候强迫数据集（CFD）对齐，以了解气候数据的选择如何影响玉米，小麦，水稻和大豆的模拟历史作物生产力。结果表明，CFD表现出极端事件概率的平均偏差和差异，在极端降水和气候和作物系统观测数据稀缺的地区具有更大的不确定性。模拟结果与粮农组织报告的国家产量异常高度相关的国家往往在大多数差价合约中具有高度相关性，我们使用每个差价合约的多GGCM集合来隔离其影响。与其他研究中检测到的气候信号相比，相关性是有利的，尽管许多国家的生产主要不受气候限制（特别是水稻）。偏差调整后的差价合约通常是最高的模型-观察相关性，尽管所有差价合约在至少一个顶级生产国产生最高的相关性。对更大的多CFD-多GGCM集合（最多91个成员）的分析显示，在某些地区和农业系统中，使用较小的模型子集是有益的，尽管更大并不总是更好。我们的分析表明，全球评估应优先考虑基于多个作物模型的集合，而不是多个CFD，只要重点区域使用表现最好的CFD。
We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.