A Bayesian Model for 20th Century Antarctic Sea Ice Extent Reconstruction

IF 2.9 3区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS
T. J. Maierhofer, M. N. Raphael, R. L. Fogt, M. S. Handcock
{"title":"A Bayesian Model for 20th Century Antarctic Sea Ice Extent Reconstruction","authors":"T. J. Maierhofer,&nbsp;M. N. Raphael,&nbsp;R. L. Fogt,&nbsp;M. S. Handcock","doi":"10.1029/2024EA003577","DOIUrl":null,"url":null,"abstract":"<p>Antarctic sea ice, a key component in the complex Antarctic climate system, is an important driver and indicator of the global climate. In the relatively short satellite-observed period from 1979 to 2022 the sea ice extent has continuously increased (contrasting a major decrease in Arctic sea ice) up to a dramatic decrease between 2014 and 2017. Recent years have seen record sea ice lows in February 2022–February 2023. We use a statistical ensemble reconstruction of Antarctic sea ice to put the observed changes into the historical context of the entire 20th century. We propose a seasonal Vector Auto-Regressive Moving Average (VARMA) model fit in a Bayesian framework using regularized horseshoe priors on the regression coefficients to create a stochastic ensemble reconstruction of monthly Antarctic Sea ice extent from 1900 to 1979. This novel model produces a set of 2,500 plausible sea ice extent reconstructions for the sea ice by sector that incorporate the autocorrelation structure of sea ice over time as well as the dependence of sea ice between the sectors. These fully observed reconstructions exhibit plausible month-to-month changes in reconstructed sea ice as well as plausible interactions between the sectors and the total. We reconstruct an overall higher sea ice extent earlier in the 20th century with a relatively sharp decline in the 1970s. These trends agree well with previous reconstructions of Antarctic sea ice based on ice core data, whaling locations, and climatological data, as well as early satellite observations in the reconstruction period.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"11 10","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003577","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth and Space Science","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024EA003577","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 0

Abstract

Antarctic sea ice, a key component in the complex Antarctic climate system, is an important driver and indicator of the global climate. In the relatively short satellite-observed period from 1979 to 2022 the sea ice extent has continuously increased (contrasting a major decrease in Arctic sea ice) up to a dramatic decrease between 2014 and 2017. Recent years have seen record sea ice lows in February 2022–February 2023. We use a statistical ensemble reconstruction of Antarctic sea ice to put the observed changes into the historical context of the entire 20th century. We propose a seasonal Vector Auto-Regressive Moving Average (VARMA) model fit in a Bayesian framework using regularized horseshoe priors on the regression coefficients to create a stochastic ensemble reconstruction of monthly Antarctic Sea ice extent from 1900 to 1979. This novel model produces a set of 2,500 plausible sea ice extent reconstructions for the sea ice by sector that incorporate the autocorrelation structure of sea ice over time as well as the dependence of sea ice between the sectors. These fully observed reconstructions exhibit plausible month-to-month changes in reconstructed sea ice as well as plausible interactions between the sectors and the total. We reconstruct an overall higher sea ice extent earlier in the 20th century with a relatively sharp decline in the 1970s. These trends agree well with previous reconstructions of Antarctic sea ice based on ice core data, whaling locations, and climatological data, as well as early satellite observations in the reconstruction period.

Abstract Image

20 世纪南极海冰广度重建的贝叶斯模型
南极海冰是复杂的南极气候系统的关键组成部分,是全球气候的重要驱动力和指标。在 1979 年至 2022 年这一相对较短的卫星观测期内,海冰面积持续增加(与之形成鲜明对比的是北极海冰的大幅减少),直至 2014 年至 2017 年期间的急剧减少。近年来,2022 年 2 月至 2023 年 2 月的海冰面积创下了历史新低。我们利用南极海冰的统计集合重建,将观测到的变化置于整个 20 世纪的历史背景中。我们提出了一个季节性矢量自回归移动平均(VARMA)模型,该模型在贝叶斯框架下使用回归系数的正则化马蹄先验值来创建 1900 年至 1979 年南极海冰月度范围的随机集合重建。这一新颖的模型为各扇区的海冰生成了一套 2500 个可信的海冰范围重建,其中包含了海冰随时间变化的自相关结构以及各扇区之间海冰的依赖性。这些完全观测到的重建结果表明,重建海冰的月与月之间的变化是可信的,各区海冰与总海冰之间的相互作用也是可信的。我们重建的海冰范围在 20 世纪早期总体较高,在 20 世纪 70 年代相对急剧下降。这些趋势与之前根据冰芯数据、捕鲸地点和气候学数据重建的南极海冰以及重建时期的早期卫星观测结果非常吻合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Earth and Space Science
Earth and Space Science Earth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
5.50
自引率
3.20%
发文量
285
审稿时长
19 weeks
期刊介绍: Marking AGU’s second new open access journal in the last 12 months, Earth and Space Science is the only journal that reflects the expansive range of science represented by AGU’s 62,000 members, including all of the Earth, planetary, and space sciences, and related fields in environmental science, geoengineering, space engineering, and biogeochemistry.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信