{"title":"CO2 has significant implications for hourly ambient temperature: Evidence from Hawaii","authors":"Kevin F. Forbes","doi":"10.1002/env.2803","DOIUrl":null,"url":null,"abstract":"<p>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 CO<sub>2</sub> in the atmosphere has consequences for the climate but strongly deny that the magnitude of the effect is significant. Using hourly CO<sub>2</sub> 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. CO<sub>2</sub> 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.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 6","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2803","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmetrics","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/env.2803","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
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.
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.