The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter‐annual scales
High‐quality atmospheric CO2 measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO2. In this study, we present the first 6 years (2014–2019) of continuous, high‐precision measurements of atmospheric CO2 at the Amaz...
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Veröffentlicht in: | Global change biology 2022-01, Vol.28 (2), p.588-611 |
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creator | Botía, Santiago Komiya, Shujiro Marshall, Julia Koch, Thomas Gałkowski, Michał Lavric, Jost Gomes‐Alves, Eliane Walter, David Fisch, Gilberto Pinho, Davieliton M. Nelson, Bruce W. Martins, Giordane Luijkx, Ingrid T. Koren, Gerbrand Florentie, Liesbeth Carioca de Araújo, Alessandro Sá, Marta Andreae, Meinrat O. Heimann, Martin Peters, Wouter Gerbig, Christoph |
description | High‐quality atmospheric CO2 measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO2. In this study, we present the first 6 years (2014–2019) of continuous, high‐precision measurements of atmospheric CO2 at the Amazon Tall Tower Observatory (ATTO, 2.1°S, 58.9°W). After subtracting the simulated background concentrations from our observational record, we define a CO2 regional signal (ΔCO2obs) that has a marked seasonal cycle with an amplitude of about 4 ppm. At both seasonal and inter‐annual scales, we find differences in phase between ΔCO2obs and the local eddy covariance net ecosystem exchange (EC‐NEE), which is interpreted as an indicator of a decoupling between local and non‐local drivers of ΔCO2obs. In addition, we present how the 2015–2016 El Niño‐induced drought was captured by our atmospheric record as a positive 2σ anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter‐annual variability of ΔCO2obs together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO2 exchange. We use both non‐optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO2 from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data‐driven non‐optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales.
By analyzing a six‐year record of CO2 mole fractions at the Amazon Tall Tower Observatory (ATTO), we found that the seasonal cycle amplitude is ~4 ppm and that the controls at seasonal and inter‐annual scales shift from local to non‐local drivers. We show that the 2015/2016 ENSO was captured as a strong positive anomaly mainly in 2016. Finally, we use a suit of biosphere models in an atmospheric transport model to simulated CO2 mole fractions at ATTO. We found that the seasonal cycle was not cap |
doi_str_mv | 10.1111/gcb.15905 |
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By analyzing a six‐year record of CO2 mole fractions at the Amazon Tall Tower Observatory (ATTO), we found that the seasonal cycle amplitude is ~4 ppm and that the controls at seasonal and inter‐annual scales shift from local to non‐local drivers. We show that the 2015/2016 ENSO was captured as a strong positive anomaly mainly in 2016. Finally, we use a suit of biosphere models in an atmospheric transport model to simulated CO2 mole fractions at ATTO. We found that the seasonal cycle was not captured by our simulations but we show that including the contribution of CO2 from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data‐driven non‐optimized NEE product.</description><identifier>ISSN: 1354-1013</identifier><identifier>EISSN: 1365-2486</identifier><identifier>DOI: 10.1111/gcb.15905</identifier><language>eng</language><publisher>Oxford: Blackwell Publishing Ltd</publisher><subject>Amplitude ; Amplitudes ; Annual variations ; Atmospheric models ; atmospheric transport ; Carbon cycle ; Carbon dioxide ; Decoupling ; Drought ; Dry season ; El Nino ; El Nino phenomena ; Exchanging ; Fluxes ; net ecosystem exchange ; Observatories ; Photosynthesis ; Rainy season ; river evasion ; Rivers ; Seasonal variation ; Shape ; Simulation ; Temporal variations ; Towers ; Wet season</subject><ispartof>Global change biology, 2022-01, Vol.28 (2), p.588-611</ispartof><rights>2021 The Authors. published by John Wiley & Sons Ltd.</rights><rights>2021. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0002-2275-0713 ; 0000-0002-5447-3968 ; 0000-0001-6185-4366</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fgcb.15905$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fgcb.15905$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>315,782,786,1419,27933,27934,45583,45584</link.rule.ids></links><search><creatorcontrib>Botía, Santiago</creatorcontrib><creatorcontrib>Komiya, Shujiro</creatorcontrib><creatorcontrib>Marshall, Julia</creatorcontrib><creatorcontrib>Koch, Thomas</creatorcontrib><creatorcontrib>Gałkowski, Michał</creatorcontrib><creatorcontrib>Lavric, Jost</creatorcontrib><creatorcontrib>Gomes‐Alves, Eliane</creatorcontrib><creatorcontrib>Walter, David</creatorcontrib><creatorcontrib>Fisch, Gilberto</creatorcontrib><creatorcontrib>Pinho, Davieliton M.</creatorcontrib><creatorcontrib>Nelson, Bruce W.</creatorcontrib><creatorcontrib>Martins, Giordane</creatorcontrib><creatorcontrib>Luijkx, Ingrid T.</creatorcontrib><creatorcontrib>Koren, Gerbrand</creatorcontrib><creatorcontrib>Florentie, Liesbeth</creatorcontrib><creatorcontrib>Carioca de Araújo, Alessandro</creatorcontrib><creatorcontrib>Sá, Marta</creatorcontrib><creatorcontrib>Andreae, Meinrat O.</creatorcontrib><creatorcontrib>Heimann, Martin</creatorcontrib><creatorcontrib>Peters, Wouter</creatorcontrib><creatorcontrib>Gerbig, Christoph</creatorcontrib><title>The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter‐annual scales</title><title>Global change biology</title><description>High‐quality atmospheric CO2 measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO2. In this study, we present the first 6 years (2014–2019) of continuous, high‐precision measurements of atmospheric CO2 at the Amazon Tall Tower Observatory (ATTO, 2.1°S, 58.9°W). After subtracting the simulated background concentrations from our observational record, we define a CO2 regional signal (ΔCO2obs) that has a marked seasonal cycle with an amplitude of about 4 ppm. At both seasonal and inter‐annual scales, we find differences in phase between ΔCO2obs and the local eddy covariance net ecosystem exchange (EC‐NEE), which is interpreted as an indicator of a decoupling between local and non‐local drivers of ΔCO2obs. In addition, we present how the 2015–2016 El Niño‐induced drought was captured by our atmospheric record as a positive 2σ anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter‐annual variability of ΔCO2obs together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO2 exchange. We use both non‐optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO2 from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data‐driven non‐optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales.
By analyzing a six‐year record of CO2 mole fractions at the Amazon Tall Tower Observatory (ATTO), we found that the seasonal cycle amplitude is ~4 ppm and that the controls at seasonal and inter‐annual scales shift from local to non‐local drivers. We show that the 2015/2016 ENSO was captured as a strong positive anomaly mainly in 2016. Finally, we use a suit of biosphere models in an atmospheric transport model to simulated CO2 mole fractions at ATTO. 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Komiya, Shujiro ; Marshall, Julia ; Koch, Thomas ; Gałkowski, Michał ; Lavric, Jost ; Gomes‐Alves, Eliane ; Walter, David ; Fisch, Gilberto ; Pinho, Davieliton M. ; Nelson, Bruce W. ; Martins, Giordane ; Luijkx, Ingrid T. ; Koren, Gerbrand ; Florentie, Liesbeth ; Carioca de Araújo, Alessandro ; Sá, Marta ; Andreae, Meinrat O. ; Heimann, Martin ; Peters, Wouter ; Gerbig, Christoph</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p2915-4c8e3fd5065c2321e3aced868da86deb75e5436490ecca2e4f70c1689f62d2833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Amplitude</topic><topic>Amplitudes</topic><topic>Annual variations</topic><topic>Atmospheric models</topic><topic>atmospheric transport</topic><topic>Carbon cycle</topic><topic>Carbon dioxide</topic><topic>Decoupling</topic><topic>Drought</topic><topic>Dry season</topic><topic>El Nino</topic><topic>El Nino phenomena</topic><topic>Exchanging</topic><topic>Fluxes</topic><topic>net ecosystem exchange</topic><topic>Observatories</topic><topic>Photosynthesis</topic><topic>Rainy season</topic><topic>river evasion</topic><topic>Rivers</topic><topic>Seasonal variation</topic><topic>Shape</topic><topic>Simulation</topic><topic>Temporal variations</topic><topic>Towers</topic><topic>Wet season</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Botía, Santiago</creatorcontrib><creatorcontrib>Komiya, Shujiro</creatorcontrib><creatorcontrib>Marshall, Julia</creatorcontrib><creatorcontrib>Koch, Thomas</creatorcontrib><creatorcontrib>Gałkowski, Michał</creatorcontrib><creatorcontrib>Lavric, Jost</creatorcontrib><creatorcontrib>Gomes‐Alves, Eliane</creatorcontrib><creatorcontrib>Walter, David</creatorcontrib><creatorcontrib>Fisch, Gilberto</creatorcontrib><creatorcontrib>Pinho, Davieliton M.</creatorcontrib><creatorcontrib>Nelson, Bruce W.</creatorcontrib><creatorcontrib>Martins, Giordane</creatorcontrib><creatorcontrib>Luijkx, Ingrid T.</creatorcontrib><creatorcontrib>Koren, Gerbrand</creatorcontrib><creatorcontrib>Florentie, Liesbeth</creatorcontrib><creatorcontrib>Carioca de Araújo, Alessandro</creatorcontrib><creatorcontrib>Sá, Marta</creatorcontrib><creatorcontrib>Andreae, Meinrat O.</creatorcontrib><creatorcontrib>Heimann, Martin</creatorcontrib><creatorcontrib>Peters, Wouter</creatorcontrib><creatorcontrib>Gerbig, Christoph</creatorcontrib><collection>Wiley Online Library (Open Access Collection)</collection><collection>Wiley Online Library (Open Access Collection)</collection><collection>Ecology Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>MEDLINE - Academic</collection><jtitle>Global change biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Botía, Santiago</au><au>Komiya, Shujiro</au><au>Marshall, Julia</au><au>Koch, Thomas</au><au>Gałkowski, Michał</au><au>Lavric, Jost</au><au>Gomes‐Alves, Eliane</au><au>Walter, David</au><au>Fisch, Gilberto</au><au>Pinho, Davieliton M.</au><au>Nelson, Bruce W.</au><au>Martins, Giordane</au><au>Luijkx, Ingrid T.</au><au>Koren, Gerbrand</au><au>Florentie, Liesbeth</au><au>Carioca de Araújo, Alessandro</au><au>Sá, Marta</au><au>Andreae, Meinrat O.</au><au>Heimann, Martin</au><au>Peters, Wouter</au><au>Gerbig, Christoph</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter‐annual scales</atitle><jtitle>Global change biology</jtitle><date>2022-01</date><risdate>2022</risdate><volume>28</volume><issue>2</issue><spage>588</spage><epage>611</epage><pages>588-611</pages><issn>1354-1013</issn><eissn>1365-2486</eissn><abstract>High‐quality atmospheric CO2 measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO2. In this study, we present the first 6 years (2014–2019) of continuous, high‐precision measurements of atmospheric CO2 at the Amazon Tall Tower Observatory (ATTO, 2.1°S, 58.9°W). After subtracting the simulated background concentrations from our observational record, we define a CO2 regional signal (ΔCO2obs) that has a marked seasonal cycle with an amplitude of about 4 ppm. At both seasonal and inter‐annual scales, we find differences in phase between ΔCO2obs and the local eddy covariance net ecosystem exchange (EC‐NEE), which is interpreted as an indicator of a decoupling between local and non‐local drivers of ΔCO2obs. In addition, we present how the 2015–2016 El Niño‐induced drought was captured by our atmospheric record as a positive 2σ anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter‐annual variability of ΔCO2obs together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO2 exchange. We use both non‐optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO2 from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data‐driven non‐optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales.
By analyzing a six‐year record of CO2 mole fractions at the Amazon Tall Tower Observatory (ATTO), we found that the seasonal cycle amplitude is ~4 ppm and that the controls at seasonal and inter‐annual scales shift from local to non‐local drivers. We show that the 2015/2016 ENSO was captured as a strong positive anomaly mainly in 2016. Finally, we use a suit of biosphere models in an atmospheric transport model to simulated CO2 mole fractions at ATTO. We found that the seasonal cycle was not captured by our simulations but we show that including the contribution of CO2 from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data‐driven non‐optimized NEE product.</abstract><cop>Oxford</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/gcb.15905</doi><tpages>24</tpages><orcidid>https://orcid.org/0000-0002-2275-0713</orcidid><orcidid>https://orcid.org/0000-0002-5447-3968</orcidid><orcidid>https://orcid.org/0000-0001-6185-4366</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Amplitude Amplitudes Annual variations Atmospheric models atmospheric transport Carbon cycle Carbon dioxide Decoupling Drought Dry season El Nino El Nino phenomena Exchanging Fluxes net ecosystem exchange Observatories Photosynthesis Rainy season river evasion Rivers Seasonal variation Shape Simulation Temporal variations Towers Wet season |
title | The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter‐annual scales |
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