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全球月度CMIP6缩小高分辨率 (1公里) 近地表空气温度数据集 (1950-2100年)
Xuewen Lei1,2, Qingyan Meng3,4,5, Ming Luo6
1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
Scientific data
|October 29, 2025
概括
一个新的高分辨率温度数据集MoCHAT提供了详细的全球气候预测. 这一数据集通过克服粗度分辨率气候模型的局限性,支持微细规模的热风险研究.
科学领域:
- 气候科学 气候科学
- 环境建模环境建模
- 数据科学数据科学数据科学
背景情况:
- 一般循环模型 (GCM) 对于理解全球变暖和气候变化至关重要.
- GCM的粗空间分辨率 (~1-3°) 阻碍了微细规模的研究,例如城市内研究.
- 需要高分辨率的气候数据来支持适应和减缓战略.
研究的目的:
- 为了生产MoCHAT,一个全球每月高分辨率 (1公里) 近地表空气温度数据集.
- 克服现有的GCM的空间分辨率限制,用于微型气候研究.
- 为全球细度热风险评估提供可靠的数据.
主要方法:
- 使用了三角形缩小缩小方法.
- 基于NEX-GDDP-CMIP6和WorldClim数据集生成的数据.
- 在三个共享社会经济路径 (SSP) 下,从16个GCM降低了温度预测.
主要成果:
- 莫查特提供了平均,最大和最小空气温度数据.
- 该数据集涵盖了历史时期 (1950-2014) 和未来情景 (2015-2100).
- 验证显示平均绝对误差在1.60-2.38K之间,偏差低于2.0K.
结论:
- 在高分辨率气候数据的可用性方面,MoCHAT提供了显著的进步.
- 该数据集通过克服分辨率限制,支持全球细度热风险研究.
- 莫查特为制定有针对性的气候变化适应和减缓战略提供了必要的数据.
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