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加拿大和美国的CMIP6气候变化预测的空间数据集
Daniel W McKenney1, John H Pedlar1, Kevin Lawrence1
1Great Lakes Forestry Centre, Natural Resources Canada, 1219 Queen Street East, Sault Ste Marie, Ontario P6A 2E5, Canada.
Data in brief
|January 20, 2025
概括
现在可以获得加拿大和美国的高分辨率气候变化预测. 这些数据使用先进的建模,为气候影响和适应研究提供关键见解.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 地理空间分析的研究.
背景情况:
- 地理空间气候变化预测对于理解不同科学学科的影响和适应是必不可少的.
- 有效的区域规划和研究需要准确的高分辨率气候数据.
研究的目的:
- 为加拿大和美国提供新的,以2公里分辨率为基础的每月地理空间气候变化预测网格.
- 为未来的气候研究提供一个全面的数据集,包括多种气候模型,排放途径和时间段.
主要方法:
- 从第6个合模型相互比较项目 (CMIP6) 中使用的输出,包括13个通用循环模型 (GCM).
- 采用三角洲缩小方法,将历史气候站数据与GCM衍生变化预测集成在一起.
- 使用ANUSPLIN应用空间插值,并对数据准确性进行严格的质量控制.
主要成果:
- 为加拿大和美国以2公里分辨率生成月度气候预测网格.
- 包括各种共享社会经济路径 (SSP) 下的关键气候变量 (如温度,降水和气候湿度指数) 的预测.
- 数据涵盖了四个不同的30年时间段,从2011年到2100年.
结论:
- 提出的数据产品为研究人员和政策制定者提供了宝贵的资源,用于评估气候变化影响和适应战略.
- 这些预测的高分辨率和全面性质提高了研究区域气候变化影响的能力.
- 通过强有力的质量控制措施确保数据可靠性,为未来的气候估计提供信心.
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