CO2 has significant implications for hourly ambient temperature: Evidence from Hawaii

IF 1.5 3区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Environmetrics Pub Date : 2023-05-03 DOI:10.1002/env.2803
Kevin F. Forbes
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引用次数: 1

Abstract

A small group of climate scientists and influencers have vigorously disputed the scientific consensus on climate change. They have contributed to a belief system that has impeded policy actions to reduce emissions. They accept that more CO2 in the atmosphere has consequences for the climate but strongly deny that the magnitude of the effect is significant. Using hourly CO2 data from the Mauna Loa Observatory in Hawaii, this article examines whether the hourly temperature data at the nearby Hilo International Airport support this belief. ARCH/ARMAX methods are employed because the hourly temperature data, even in Hawaii, are both highly autoregressive and volatile. The temperature data are analyzed using an archive of day-ahead hourly weather forecast data to control for expected meteorological outcomes. The model is estimated using 42,928 hourly observations from August 7, 2009, through December 31, 2014. CO2 concentrations are found to have statistically significant implications for hourly temperature. The model is evaluated using hourly data from January 1, 2015, through December 31, 2017. The findings add to the consilience of evidence supporting the scientific consensus on climate change.

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二氧化碳对每小时环境温度有重大影响:来自夏威夷的证据
一小群气候科学家和影响者对气候变化的科学共识提出了强烈质疑。他们促成了一种阻碍减排政策行动的信仰体系。他们承认大气中更多的二氧化碳会对气候产生影响,但强烈否认这种影响的严重性。本文利用夏威夷Mauna Loa天文台的每小时二氧化碳数据,研究了附近希洛国际机场的每小时温度数据是否支持这一观点。之所以采用ARCH/ARMAX方法,是因为即使在夏威夷,每小时的温度数据也是高度自回归和波动的。温度数据使用日前每小时天气预报数据档案进行分析,以控制预期的气象结果。该模型使用2009年8月7日至2014年12月31日的42928个小时观测值进行估计。发现CO2浓度对每小时温度具有统计学意义。该模型使用2015年1月1日至2017年12月31日的每小时数据进行评估。这些发现增加了支持气候变化科学共识的证据的一致性。
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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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