全球网格气象数据集揭示了每日极端温度和美国农业产量之间的非线性关系
Dylan Hogan1, Wolfram Schlenker2
1Columbia University School of International and Public Affairs, New York, NY, USA. dth2133@columbia.edu.
Nature communications
|May 31, 2024
概括
新的全球天气数据集 (GMFD和ERA5-Land) 准确地捕捉了农业商品的温度-产量关系. 专注于每日极端温度比预测作物产量更重要,而不是特定的天气数据源.
科学领域:
- 农业经济学 农业经济学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 全球农业市场相互关联,价格受总供应的影响.
- 准确估计天气冲击对生产,贸易和价格的影响,需要具有全球代表性的天气数据.
- 最近可用的每日/每小时全球数据集 (GMFD,ERA5-Land) 提供了与旧方法相比的潜在改进.
研究的目的:
- 评估新的全球天气数据集 (GMFD,ERA5-Land) 与美国特定国家数据的有效性,以解释作物产量.
- 为了比较由全局数据集与微量化数据集估计的收益率响应函数.
- 评估GMFD和ERA5-Land在撒哈拉以南非洲等数据稀疏地区的预测能力.
主要方法:
- 使用美国玉米和大豆数据对产量预测模型的比较分析.
- 测试全球数据集 (GMFD,ERA5-Land) 与微细缩放的PRISM数据进行对比.
- 评估基于每日极端温度与平均温度的模型.
- 对撒哈拉以南非洲的CRU数据进行GMFD和ERA5-Land预测性能评估.
主要成果:
- 对于美国的产量,GMFD和ERA5-Land的预测能力低于PRISM,但正确识别了非线性温度关系.
- 使用每日极端温度的模型表现优于使用平均温度的模型,无论天气数据集如何.
- 捕捉每日极端温度的影响比选择天气数据更为重要.
- 与CRU数据集相比,GMFD和ERA5-Land在撒哈拉以南非洲的预测能力更强.
结论:
- 新的全球天气数据集 (GMFD,ERA5-Land) 是农业和气候影响研究的宝贵工具,特别是在数据稀缺的地区.
- 准确的作物产量对天气反应的建模需要关注每日极端温度.
- 这些发现支持使用全球数据集来了解天气对农产品市场的影响.
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