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一套基于CMIP6的气候场景数据集,用于对英国气候变化影响评估
Mikhail A Semenov1, Nimai Senapati1, Kevin Coleman2
1Sustainable Soils and Crops, Rothamsted Research, West Common, Harpenden AL5 2JQ, United Kingdom.
Data in brief
|July 30, 2024
概括
英国的新气候变化情景为适应规划提供了重要的当地规模数据. 这个数据集使用缩小的全球气候模型 (GCM) 预测来评估未来的气候变化和极端事件.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 气象学 天气学
背景情况:
- 气候变化需要准确的局部影响评估,以制定有效的适应战略.
- 全球气候模型 (GCM) 预测往往缺乏解决方案,并包含不适合本地气候变化影响研究的偏见.
- 高质量,缩小规模的气候场景对于理解未来的气候变化和极端事件至关重要.
研究的目的:
- 为整个英国的26个代表性地点生成未来气候变化场景的高分辨率数据集.
- 为气候变化影响评估提供一个强大的数据集,并为适应和减缓战略提供信息.
- 通过使用多模型组合和大量模拟年份来考虑气候预测中的不确定性.
主要方法:
- 利用LARS-WG 8.0随机天气发生器将CMIP6多模型合集GCM缩小到局部尺度.
- 根据北欧的性能和气候敏感性选择了五个GCM (ACCESS-ESM1-5,CNRM-CM6-1,HadGEM3-GC31-LL,MPI-ESM1-2-LR,MRI-ESM2-0) 的子集.
- 根据两个共享社会经济路径 (SSP):SSP2-4.5和SSP5-8.5.5,为基线,2030年和2050年的气候生成了1000个每日天气的实现:SSP2-4.5和SSP5-8.5.
主要成果:
- 为英国26个地点创建了本地气候变化场景的综合数据集.
- 缩小规模的场景捕捉了平均气候变化,变化和极端事件 (例如热浪,洪水,干旱) 的变化.
- 该数据集包括1000年的每日天气数据,对于每个场景来说,对于分析年际变化和低频极端事件至关重要.
结论:
- 生成的数据集为研究人员和政策制定者进行气候变化影响评估提供了宝贵的资源.
- 它支持各种部门的适应和减缓决策,包括农业,水资源,生态和公共卫生.
- 这种缩小规模的数据通过解决GCM的不确定性和可变性,提高了本地气候变化影响研究的可靠性.
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