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一个高分辨率的每日全球数据集,用于气候影响分析的统计缩小CMIP6模型
Solomon Gebrechorkos1,2, Julian Leyland3, Louise Slater4
1School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK. S.H.Gebrechorkos@soton.ac.uk.
Scientific data
|September 11, 2023
概括
本研究引入了一种新的统计缩放方法 (BCCAQ) 来从全球气候模型 (GCM) 创建高分辨率的气候数据. 由此产生的数据集通过准确复制极端天气事件来改善当地气候影响评估.
科学领域:
- 气候科学 气候科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 全球气候模型 (GCM) 提供了有价值的气候预测,但往往缺乏对当地影响评估所需的分辨率.
- GCMs的粗略解决限制了分析当地气候变化及其相关影响的有效性.
- 在地方范围内评估未来的气候变化和极端事件需要比通常由GCMs提供的更高分辨率的数据.
研究的目的:
- 从18个合模型相互比较项目第6阶段 (CMIP6) 的全球气候模型 (GCMs) 中降低气候变量,使用一种新的统计方法.
- 在多个共享社会经济路径 (SSP) 中生成历史和未来时期的高分辨率 (0.25°) 每日气候数据集.
- 为推动高分辨率影响评估模型和了解当地气候变化提供可靠的数据集.
主要方法:
- 使用了偏差校正构建模拟与量子映射重排序 (BCCAQ) 统计下调缩放技术.
- 使用高分辨率参考数据集校准的BCCAQ,以确保准确的偏差校正和极端事件再现.
- 缩小每日降水量,空气温度,风速,空气压力和相对湿度.
主要成果:
- BCCAQ方法在消除GCM偏差和准确复制极端天气事件方面表现出强的表现.
- 为历史时期 (1981-2014) 和未来时期 (2015-2100) 生成了0.25°分辨率的全球缩小气候数据集.
- 有三个共享社会经济路径 (SSP2-4.5,SSP5-3.4-OS和SSP5-8.5) 的数据.
结论:
- 新型缩小规模的气候数据集为当地影响研究提供了与粗度分辨率GCM输出相比的显著改进.
- 数据集能够复制极端事件,这使得它对气候适应和风险评估特别有价值.
- 这一资源将提高各个部门未来气候变化影响评估的准确性.
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