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基于地球有效辐射强迫的观测估计.
Senne Van Loon1, Maria Rugenstein1, Elizabeth A Barnes1
1Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80521.
概括
人类排放对地球气候产生重大影响. 这项研究使用机器学习和观测估计了有效的辐射强迫,显示了自2021年以来的大幅增加,与最近的变暖趋势一致.
科学领域:
- 气候科学 气候科学
- 地球系统科学 地球系统科学
- 大气物理学 大气物理学
背景情况:
- 人类排放是气候变化的主要驱动因素.
- 有效辐射强迫 (ERF) 量化了人类和自然对地球能量平衡的影响.
- 直接观察ERF是一项挑战,需要依赖气候模型.
研究的目的:
- 开发有效辐射强迫的观测估计.
- 利用机器学习来弥合气候模型和观测数据之间的差距.
- 提供对ERF趋势的独立评估.
主要方法:
- 采用机器学习来建模表面温度和来自内部气候变化的辐射之间的关系.
- 集成的多模型组合数据与观察到的表面温度和净辐射不平衡.
- 计算了从2001年到2024年的有效辐射强迫趋势.
主要成果:
- 据估计,2001-2024年每十年的有效辐射强迫趋势为0.71 ± 0.21 Wm-2 .
- 证明自1985年以来对观察到的ERF进行了独立评估.
- 自2021年以来,ERF的大幅增加,直到2024年才被辐射反应抵消.
结论:
- 这项研究提供了一种新的,受观察限制的有效辐射强迫估计.
- 这些发现与对辐射反和近期异常高温的物理理解一致.
- 提升了在年度时间尺度上完成地球能源预算的能力.
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