{"title":"Using Oscillatory Processes in Northern Hemisphere Proxy Temperature Records to Forecast Industrial-era Temperatures","authors":"J. Abbot","doi":"10.11648/J.EARTH.20211003.14","DOIUrl":null,"url":null,"abstract":"The validity and interpretation of differing representations of proxy temperature profiles from the past 2,000 years for the northern hemisphere remains controversial. One perspective of temperatures over the past 1,000 years embodies a major oscillation with a peak corresponding with the Medieval Warm Period (MWP), a trough representing the Little Ice Age (LIA) and subsequent increasing temperatures to the present. An alternate temperature perspective, known as the “hockey stick” exhibits a slow long-term cooling trend downward from about 1000 AD to about 1900 AD, followed by relatively rapid warming in the 20th century and is a prominent feature in describing the apparent climate crisis. The present study, using spectral analysis, shows that both types of profile have a dominant millennial oscillation and a set of lower power centennial and decadal oscillations. The key difference in determination of development of the proxy temperature profile into either a hockey stick or MWP_LIA cycle is the phase alignments of centennial and decadal oscillations with respect to the millennial oscillation. In both cases, the resultant sine waves from spectral analysis up to 1880 AD can be used to train a an artificial neural network using oscillatory data corresponding to the pre-industrial era, then forecasting temperatures into the 20th century, enabling an estimation of natural and anthropogenic contributions to recent warming. The limitations of highly complex general circulation models that do not to adequately incorporate oscillatory patterns in temperatures may be a compelling reason to promote more extensive use of forecasting with established machine learning techniques such as ANNs.","PeriodicalId":50560,"journal":{"name":"Earth Sciences History","volume":"23 1","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2021-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Sciences History","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.11648/J.EARTH.20211003.14","RegionNum":4,"RegionCategory":"哲学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 1
Abstract
The validity and interpretation of differing representations of proxy temperature profiles from the past 2,000 years for the northern hemisphere remains controversial. One perspective of temperatures over the past 1,000 years embodies a major oscillation with a peak corresponding with the Medieval Warm Period (MWP), a trough representing the Little Ice Age (LIA) and subsequent increasing temperatures to the present. An alternate temperature perspective, known as the “hockey stick” exhibits a slow long-term cooling trend downward from about 1000 AD to about 1900 AD, followed by relatively rapid warming in the 20th century and is a prominent feature in describing the apparent climate crisis. The present study, using spectral analysis, shows that both types of profile have a dominant millennial oscillation and a set of lower power centennial and decadal oscillations. The key difference in determination of development of the proxy temperature profile into either a hockey stick or MWP_LIA cycle is the phase alignments of centennial and decadal oscillations with respect to the millennial oscillation. In both cases, the resultant sine waves from spectral analysis up to 1880 AD can be used to train a an artificial neural network using oscillatory data corresponding to the pre-industrial era, then forecasting temperatures into the 20th century, enabling an estimation of natural and anthropogenic contributions to recent warming. The limitations of highly complex general circulation models that do not to adequately incorporate oscillatory patterns in temperatures may be a compelling reason to promote more extensive use of forecasting with established machine learning techniques such as ANNs.
期刊介绍:
Earth Sciences History promotes and publishes historical work on all areas of the earth sciences – including geology, geography, geophysics, oceanography, paleontology, meteorology, and climatology.
The journal honors and encourages a variety of approaches to historical study: biography, history of ideas, social history, and histories of institutions, organizations, and techniques.
Articles are peer reviewed.