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一组偏差调整的CMIP6气候模拟,基于高分辨率的北美再分析
Juliette Lavoie1, Pascal Bourgault2, Trevor James Smith2
1Ouranos, Montreal, H3A 1B9, Canada. lavoie.juliette@ouranos.ca.
Scientific data
|January 11, 2024
概括
本研究介绍ESPO-G6-R2 v1.0,从合模型相互比较项目6 (CMIP6) 模型中调整偏差的气候模拟. 这些数据改善了北美历史气候预测,有助于气候变化研究.
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:
- 气候模型提供了关键的预测,但往往包含偏见.
- 统计下调和偏差调整对于完善原始气候模型输出至关重要.
- 结合模型相互比较项目6 (CMIP6) 提供了一套全球气候模型.
研究的目的:
- 介绍和描述ESPO-G6-R2 v1.0数据集,一个偏差调整的气候模拟产品.
- 详细介绍使用偏差调整方法的方法,使用分离量子位映射.
- 评估数据质量和气候变化信号的保存方面的改进.
主要方法:
- 使用合模型相互比较项目6 (CMIP6) 全球气候模型的输出.
- 针对区域确定性预测系统 (RDRS) 的偏差校正应用了分离量子位映射 v2.1.1.
- 使用VALUE项目框架来评估偏差调整性能.
主要成果:
- 偏差调整显著改善了气候数据的边际,时间和多变量方面.
- 与最初的CMIP6模拟相比,调整后的数据保留了类似的气候变化信号.
- ESPO-G6-R2 v1.0数据集涵盖北美上空的1950-2100年,每天提供温度和降水数据.
结论:
- ESPO-G6-R2 v1.0数据集为北美气候变化影响研究提供了可靠的,偏差纠正的资源.
- 应用的偏差调整方法有效提高了CMIP6数据的可用性.
- 为有效利用这种增强的气候模拟数据集提供了指导.
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