Tracing Diurnal Variations of Atmospheric CO2, O2, and δ13CO2 Over a Tropical and a Temperate Forest

IF 4.6 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Kim A. P. Faassen, Raquel González-Armas, Gerbrand Koren, Getachew Agmuas Adnew, Hella van Asperen, Hugo de Boer, Santiago Botía, Vincent S. de Feiter, Oscar Hartogensis, Bert G. Heusinkveld, Lucas M. Hulsman, Ronald W. A. Hutjes, Sam P. Jones, Bert A. M. Kers, Shujiro Komiya, Luiz A. T. Machado, Giordane Martins, John B. Miller, Wouter Mol, Michiel van der Molen, Robbert Moonen, Cléo Q. Dias-Junior, Thomas Röckmann, Henk Snellen, Ingrid T. Luijkx, Jordi Vilà-Guerau de Arellano
{"title":"Tracing Diurnal Variations of Atmospheric CO2, O2, and δ13CO2 Over a Tropical and a Temperate Forest","authors":"Kim A. P. Faassen, Raquel González-Armas, Gerbrand Koren, Getachew Agmuas Adnew, Hella van Asperen, Hugo de Boer, Santiago Botía, Vincent S. de Feiter, Oscar Hartogensis, Bert G. Heusinkveld, Lucas M. Hulsman, Ronald W. A. Hutjes, Sam P. Jones, Bert A. M. Kers, Shujiro Komiya, Luiz A. T. Machado, Giordane Martins, John B. Miller, Wouter Mol, Michiel van der Molen, Robbert Moonen, Cléo Q. Dias-Junior, Thomas Röckmann, Henk Snellen, Ingrid T. Luijkx, Jordi Vilà-Guerau de Arellano","doi":"10.1029/2025gl118016","DOIUrl":null,"url":null,"abstract":"We analyze the diurnal variability of atmospheric <span data-altimg=\"/cms/asset/a658395f-3271-4d62-9595-626ed1be051a/grl71345-math-0003.png\"></span><mjx-container ctxtmenu_counter=\"203\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/grl71345-math-0003.png\"><mjx-semantics><mjx-mrow><mjx-msub data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"unknown\" data-semantic-speech=\"CO Subscript 2\" data-semantic-type=\"subscript\"><mjx-mtext data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"text\"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mtext><mjx-script style=\"vertical-align: -0.15em;\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msub></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00948276:media:grl71345:grl71345-math-0003\" display=\"inline\" location=\"graphic/grl71345-math-0003.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><msub data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"unknown\" data-semantic-speech=\"CO Subscript 2\" data-semantic-type=\"subscript\"><mtext data-semantic-=\"\" data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"text\">CO</mtext><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\">2</mn></msub></mrow>${\\text{CO}}_{2}$</annotation></semantics></math></mjx-assistive-mml></mjx-container>, <span data-altimg=\"/cms/asset/81fd8fee-ffae-4bff-8b24-61a07df31724/grl71345-math-0004.png\"></span><mjx-container ctxtmenu_counter=\"204\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/grl71345-math-0004.png\"><mjx-semantics><mjx-mrow><mjx-msub data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"normal upper O 2\" data-semantic-type=\"subscript\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-script style=\"vertical-align: -0.15em;\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msub></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00948276:media:grl71345:grl71345-math-0004\" display=\"inline\" location=\"graphic/grl71345-math-0004.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><msub data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"latinletter\" data-semantic-speech=\"normal upper O 2\" data-semantic-type=\"subscript\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\" mathvariant=\"normal\">O</mi><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\">2</mn></msub></mrow>${\\mathrm{O}}_{2}$</annotation></semantics></math></mjx-assistive-mml></mjx-container>, and <span data-altimg=\"/cms/asset/06a49b7e-604d-4b15-83d8-50ad3c6bc7bc/grl71345-math-0005.png\"></span><mjx-container ctxtmenu_counter=\"205\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/grl71345-math-0005.png\"><mjx-semantics><mjx-mrow><mjx-msup data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"greekletter\" data-semantic-speech=\"delta Superscript 13\" data-semantic-type=\"superscript\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"greekletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-script style=\"vertical-align: 0.363em;\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msup></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00948276:media:grl71345:grl71345-math-0005\" display=\"inline\" location=\"graphic/grl71345-math-0005.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><msup data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"greekletter\" data-semantic-speech=\"delta Superscript 13\" data-semantic-type=\"superscript\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-parent=\"2\" data-semantic-role=\"greekletter\" data-semantic-type=\"identifier\">δ</mi><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\">13</mn></msup></mrow>${\\delta }^{13}$</annotation></semantics></math></mjx-assistive-mml></mjx-container>CO<sub>2</sub> above the canopies of two contrasting ecosystems: the Amazon tropical forest and the Loobos temperate forest. Using a coupled forest-atmosphere model constrained by tower-based and aircraft observations, we quantify the role of atmospheric processes—including entrainment, subsidence, and cloud ventilation—in shaping the diurnal amplitude, or diurnal range (DR), of carbon-cycle tracers. Our results show that atmospheric processes can contribute more than twice as much as surface processes to DR. Misrepresenting these influences leads to substantial errors in interpreting observations and modeling tracer variability. We propose using DR as a metric to evaluate atmospheric tracer transport models and to compare site-level measurements. We present a roadmap to identify which atmospheric or surface processes are poorly represented when modeled and observed DR diverge.","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"23 1","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geophysical Research Letters","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1029/2025gl118016","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

We analyze the diurnal variability of atmospheric CO2${\text{CO}}_{2}$, O2${\mathrm{O}}_{2}$, and δ13${\delta }^{13}$CO2 above the canopies of two contrasting ecosystems: the Amazon tropical forest and the Loobos temperate forest. Using a coupled forest-atmosphere model constrained by tower-based and aircraft observations, we quantify the role of atmospheric processes—including entrainment, subsidence, and cloud ventilation—in shaping the diurnal amplitude, or diurnal range (DR), of carbon-cycle tracers. Our results show that atmospheric processes can contribute more than twice as much as surface processes to DR. Misrepresenting these influences leads to substantial errors in interpreting observations and modeling tracer variability. We propose using DR as a metric to evaluate atmospheric tracer transport models and to compare site-level measurements. We present a roadmap to identify which atmospheric or surface processes are poorly represented when modeled and observed DR diverge.
热带和温带森林大气CO2、O2和δ13CO2的日变化追踪
本文分析了亚马逊热带森林和Loobos温带森林两种不同生态系统冠层以上大气CO2${\text{CO}}_{2}$、O2${\ mathm {O}}_{2}$和δ13${\delta}^{13}$CO2的日变率。利用受塔台观测和飞机观测约束的森林-大气耦合模型,我们量化了大气过程(包括夹带、沉降和云通风)在形成碳循环示踪剂的日振幅或日范围(DR)中的作用。我们的研究结果表明,大气过程对dr的贡献是地表过程的两倍多,对这些影响的错误表述导致在解释观测结果和模拟示踪剂变率方面存在重大误差。我们建议使用DR作为一种度量来评估大气示踪剂运输模式,并比较现场水平的测量结果。我们提出了一个路线图,以确定当模型和观测到的DR偏离时,哪些大气或地表过程表现不佳。
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
Geophysical Research Letters
Geophysical Research Letters 地学-地球科学综合
CiteScore
9.00
自引率
9.60%
发文量
1588
审稿时长
2.2 months
期刊介绍: Geophysical Research Letters (GRL) publishes high-impact, innovative, and timely research on major scientific advances in all the major geoscience disciplines. Papers are communications-length articles and should have broad and immediate implications in their discipline or across the geosciences. GRLmaintains the fastest turn-around of all high-impact publications in the geosciences and works closely with authors to ensure broad visibility of top papers.
×
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:604180095
Book学术官方微信