Testing for galactic cosmic ray warming hypothesis using the notion of block-exogeneity

IF 1.5 3区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES
Environmetrics Pub Date : 2024-03-31 DOI:10.1002/env.2846
Umberto Triacca
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引用次数: 0

Abstract

In this article, we consider the notion of block-exogeneity and establish a characterization of it. We use this characterization to propose a procedure to test for block-exogeneity in a trivariate system. The proposed procedure has been applied to test the so-called galactic cosmic ray warming hypothesis. The galactic cosmic ray warming hypothesis suggests the existence of an indirect solar influence on Earth's climate. Our results seem to imply that this hypothesis does not hold. In particular, we find that the global temperature is block-exogenous with respect to both sunspot numbers (a measure of the solar activity) and galactic cosmic rays. This implies that the supposed indirect causal link from solar activity to temperature (through cosmic rays), postulated by the galactic cosmic ray warming hypothesis, does not appear to exist.

利用块状异质性概念检验银河宇宙射线变暖假说
在本文中,我们考虑了块异质性的概念,并对其进行了表征。我们利用这一表征提出了一种在三变量系统中检验块状异质性的程序。提出的程序已被用于检验所谓的银河宇宙射线变暖假说。银河宇宙射线变暖假说认为太阳对地球气候存在间接影响。我们的结果似乎暗示这一假说并不成立。特别是,我们发现全球温度与太阳黑子数量(太阳活动的测量指标)和银河宇宙射线都是块状外生的。这意味着银河宇宙射线变暖假说所假定的太阳活动与温度(通过宇宙射线)之间的间接因果关系似乎并不存在。
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来源期刊
Environmetrics
Environmetrics 环境科学-环境科学
CiteScore
2.90
自引率
17.60%
发文量
67
审稿时长
18-36 weeks
期刊介绍: Environmetrics, the official journal of The International Environmetrics Society (TIES), an Association of the International Statistical Institute, is devoted to the dissemination of high-quality quantitative research in the environmental sciences. The journal welcomes pertinent and innovative submissions from quantitative disciplines developing new statistical and mathematical techniques, methods, and theories that solve modern environmental problems. Articles must proffer substantive, new statistical or mathematical advances to answer important scientific questions in the environmental sciences, or must develop novel or enhanced statistical methodology with clear applications to environmental science. New methods should be illustrated with recent environmental data.
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