改进最佳指纹识别方法需要超越统计科学的视角

IF 6.5 2区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES Advances in Atmospheric Sciences Pub Date : 2024-07-03 DOI:10.1007/s00376-024-4175-x
Jianhua Lu
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引用次数: 0

摘要

最佳指纹法(OFM)虽然在检测和归因气候变化方面取得了成功,但从物理学和动力学的角度来看,它可能存在一些局限性。本文分析了最优指纹法所采用的线性、非交互和静态变量假设。建议进一步发展 OFM 需要超越统计科学的观点,该方法应与地球系统动力学和物理学的理论工具相结合,以便应用于非线性气候变化(包括地球系统中的临界要素)的探测和归因。
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Improving Optimal Fingerprinting Methods Requires a Viewpoint beyond Statistical Science

While being successful in the detection and attribution of climate change, the optimal fingerprinting method (OFM) may have some limitations from a physics-and-dynamics-based viewpoint. Here, an analysis is made on the linearity, non-interaction, and stationary-variability assumptions adopted by OFM. It is suggested that furthering OFM needs a viewpoint beyond statistical science, and the method should be combined with theoretical tools in the dynamics and physics of the Earth system, so as to be applied for the detection and attribution of nonlinear climate change including tipping elements within the Earth system.

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来源期刊
Advances in Atmospheric Sciences
Advances in Atmospheric Sciences 地学-气象与大气科学
CiteScore
9.30
自引率
5.20%
发文量
154
审稿时长
6 months
期刊介绍: Advances in Atmospheric Sciences, launched in 1984, aims to rapidly publish original scientific papers on the dynamics, physics and chemistry of the atmosphere and ocean. It covers the latest achievements and developments in the atmospheric sciences, including marine meteorology and meteorology-associated geophysics, as well as the theoretical and practical aspects of these disciplines. Papers on weather systems, numerical weather prediction, climate dynamics and variability, satellite meteorology, remote sensing, air chemistry and the boundary layer, clouds and weather modification, can be found in the journal. Papers describing the application of new mathematics or new instruments are also collected here.
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