Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios
Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios
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DOI：
10.1088/1748-9326/abd8fc
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发表时间：
2021-03-01
影响因子：
6.7
通讯作者：
Zabel, Florian
中科院分区：
文献类型：
--
作者：
Mueller, Christoph;Franke, James;Zabel, Florian
对气候变化的关切在很大程度上是由于其对人类社会的影响。评估这种不确定性对具体潜在影响的影响是一项艰巨的任务，因为这需要对气候和影响模型进行系统调查。我们提供了一个全面的评估玉米，春小麦和冬小麦，水稻和大豆的作物产量预测的不确定性，使用一套九种作物模型和多达45 CMIP 5和34 CMIP 6气候预测三种不同的强迫情景。为了使这项任务在计算上易于处理，我们使用了一组新的统计作物模型仿真器。我们发现，气候和作物模型对总体不确定性的贡献大致相等。虽然CMIP 5和CMIP 6预测下的产量不确定性范围相似，但CMIP 6预测对总热量生产的中位数影响（+1%至-19%）通常比CMIP 5（+5%至-13%）更为负面。在世纪上半叶，对于单个作物，作物模型之间的差异通常大于气候模型之间的差异，但我们发现作物之间存在明显差异：全球范围内，小麦和玉米的不确定性主要由作物模型决定，但大豆和水稻对气候预测更为敏感。具有非常相似的全球平均变暖的气候模型可能导致非常不同的总体影响，因此气候模型的不确定性仍然是农业影响不确定性的重要因素。这些结果显示了大集合方法的实用性，可以全面评估影响作物产量的因素或气候变化下的其他影响。这里使用的作物模型集合是不平衡的，并且使所有预测都同样合理的假设受到质疑。作物建模界必须开发和应用更好的方法，以便在各个过程的水平上进行一致的模型测试。
Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to -19%) than for CMIP5 (+5% to -13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.