Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields
Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields
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DOI：
10.1038/s43247-021-00266-9
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发表时间：
2021-09
影响因子：
7.9
通讯作者：
David C. Lafferty;R. Sriver;I. Haqiqi;T. Hertel;K. Keller;R. Nicholas
中科院分区：
文献类型：
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作者：
David C. Lafferty;R. Sriver;I. Haqiqi;T. Hertel;K. Keller;R. Nicholas
了解和量化气候变化如何影响农业的努力往往依赖于偏差校正和缩小规模的气候信息，因此量化这种方法的潜在偏差非常重要。在这里，我们使用了一个由统计偏差校正和缩小比例的气候模型组成的多模型集合，以及来自耦合模式比对项目第5阶段（CMIP5）的相应母模型，来驱动一个包含全季节温度和降水测量的美国玉米产量统计面板模型。我们分析了年度产量预测的不确定性，发现CMIP5模型大大高估了历史产量变化，而偏差校正和缩小的模型低估了天气引起的最大产量下降。我们还发现，在整个世纪中，预测产量和其他决策相关指标存在很大差异，这使得利益相关者需要在分辨率、历史准确性和预测信心方面进行导航权衡，从而做出建模选择。
Efforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Here, we use a multi-model ensemble of statistically bias-corrected and downscaled climate models, as well as the corresponding parent models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to drive a statistical panel model of U.S. maize yields that incorporates season-wide measures of temperature and precipitation. We analyze uncertainty in annual yield hindcasts, finding that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest weather-induced yield declines. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.