Methane emissions from Australia estimated by inverse analysis using in-situ and Satellite (GOSAT) atmospheric observations

Australia has significant sources of atmospheric methane (CH₄), driven by extensive coal and natural gas production, livestock, and large-scale fires. Accurate quantification and characterization of CH₄ emissions are critical for effective climate mitigation strategies in Australia. In this study, w...

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Main Authors: Fenjuan Wang, Shamil Maksyutov, Rajesh Janardanan, Akihiko Ito, Isamu Morino, Yukio Yoshida, Yu Someya, Yasunori Tohjima, Bryce F.J. Kelly, Johannes W. Kaiser, Xin Lan, Ivan Mammarella, Tsuneo Matsunaga
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:GIScience & Remote Sensing
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Online Access:https://www.tandfonline.com/doi/10.1080/15481603.2025.2488595
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author Fenjuan Wang
Shamil Maksyutov
Rajesh Janardanan
Akihiko Ito
Isamu Morino
Yukio Yoshida
Yu Someya
Yasunori Tohjima
Bryce F.J. Kelly
Johannes W. Kaiser
Xin Lan
Ivan Mammarella
Tsuneo Matsunaga
author_facet Fenjuan Wang
Shamil Maksyutov
Rajesh Janardanan
Akihiko Ito
Isamu Morino
Yukio Yoshida
Yu Someya
Yasunori Tohjima
Bryce F.J. Kelly
Johannes W. Kaiser
Xin Lan
Ivan Mammarella
Tsuneo Matsunaga
author_sort Fenjuan Wang
collection DOAJ
description Australia has significant sources of atmospheric methane (CH₄), driven by extensive coal and natural gas production, livestock, and large-scale fires. Accurate quantification and characterization of CH₄ emissions are critical for effective climate mitigation strategies in Australia. In this study, we employed an inverse analysis of atmospheric CH₄ observations from the GOSAT satellite and surface measurements from 2016 to 2021 to assess CH₄ emissions in Australia. The inversion process integrates anthropogenic and natural emissions as prior estimates, optimizing them with the NIES-TM-FLEXPART-variational model (NTFVAR) at a resolution of up to 0.1° × 0.1°. We validated the performance of our inverse model using data obtained from the United Nations Environment Program Methane Science (UNEP), Airborne Research Australia 2018 aircraft-based atmospheric CH₄ measurement campaigns. Compared to prior emission estimates, optimized emissions dramatically enhanced the accuracy of modeled concentrations, aligning them much better with observations. Our results indicate that the estimated inland CH4 emissions in Australia amount to 6.84 ± 0.51 Tg CH4 yr−1 and anthropogenic emissions amount to 4.20 ± 0.08 Tg CH4 yr−1, both slightly lower than the values reported in existing inventories. Moreover, our results unveil noteworthy spatiotemporal characteristics, such as upward corrections during the warm season, particularly in Southeastern Australia. During the three most severe months of the 2019–2020 bushfire season, emissions from biomass burning surged by 0.68 Tg, constituting over 71% of the total emission increase. These results highlight the importance of continuous observation and analysis of sectoral emissions, particularly near major sources, to guide targeted emission reduction strategies. The spatiotemporal characteristics identified in this study underscore the need for adaptive and region-specific approaches to CH₄ emission management in Australia.
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spelling doaj-art-1f72106a09014c6e8a03d705b8d9a23a2025-08-20T02:27:00ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262025-12-0162110.1080/15481603.2025.2488595Methane emissions from Australia estimated by inverse analysis using in-situ and Satellite (GOSAT) atmospheric observationsFenjuan Wang0Shamil Maksyutov1Rajesh Janardanan2Akihiko Ito3Isamu Morino4Yukio Yoshida5Yu Someya6Yasunori Tohjima7Bryce F.J. Kelly8Johannes W. Kaiser9Xin Lan10Ivan Mammarella11Tsuneo Matsunaga12Earth System Division, National Institute for Environmental Studies (NIES), JapanEarth System Division, National Institute for Environmental Studies (NIES), JapanEarth System Division, National Institute for Environmental Studies (NIES), JapanGraduate School of Agricultural and Life Sciences, The University of Tokyo, JapanEarth System Division, National Institute for Environmental Studies (NIES), JapanEarth System Division, National Institute for Environmental Studies (NIES), JapanEarth System Division, National Institute for Environmental Studies (NIES), JapanEarth System Division, National Institute for Environmental Studies (NIES), JapanSchool of Biological, Earth and Environmental Sciences, UNSW, AustraliaAtmosphere and Climate, The Climate and Environmental Research Institute NILU, Kjeller, NorwayThe Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, USAInstitute for Atmospheric and Earth System Research (INAR), The University of Helsinki, FinlandEarth System Division, National Institute for Environmental Studies (NIES), JapanAustralia has significant sources of atmospheric methane (CH₄), driven by extensive coal and natural gas production, livestock, and large-scale fires. Accurate quantification and characterization of CH₄ emissions are critical for effective climate mitigation strategies in Australia. In this study, we employed an inverse analysis of atmospheric CH₄ observations from the GOSAT satellite and surface measurements from 2016 to 2021 to assess CH₄ emissions in Australia. The inversion process integrates anthropogenic and natural emissions as prior estimates, optimizing them with the NIES-TM-FLEXPART-variational model (NTFVAR) at a resolution of up to 0.1° × 0.1°. We validated the performance of our inverse model using data obtained from the United Nations Environment Program Methane Science (UNEP), Airborne Research Australia 2018 aircraft-based atmospheric CH₄ measurement campaigns. Compared to prior emission estimates, optimized emissions dramatically enhanced the accuracy of modeled concentrations, aligning them much better with observations. Our results indicate that the estimated inland CH4 emissions in Australia amount to 6.84 ± 0.51 Tg CH4 yr−1 and anthropogenic emissions amount to 4.20 ± 0.08 Tg CH4 yr−1, both slightly lower than the values reported in existing inventories. Moreover, our results unveil noteworthy spatiotemporal characteristics, such as upward corrections during the warm season, particularly in Southeastern Australia. During the three most severe months of the 2019–2020 bushfire season, emissions from biomass burning surged by 0.68 Tg, constituting over 71% of the total emission increase. These results highlight the importance of continuous observation and analysis of sectoral emissions, particularly near major sources, to guide targeted emission reduction strategies. The spatiotemporal characteristics identified in this study underscore the need for adaptive and region-specific approaches to CH₄ emission management in Australia.https://www.tandfonline.com/doi/10.1080/15481603.2025.2488595Methane emissionsinverse modelingGOSATAustralia
spellingShingle Fenjuan Wang
Shamil Maksyutov
Rajesh Janardanan
Akihiko Ito
Isamu Morino
Yukio Yoshida
Yu Someya
Yasunori Tohjima
Bryce F.J. Kelly
Johannes W. Kaiser
Xin Lan
Ivan Mammarella
Tsuneo Matsunaga
Methane emissions from Australia estimated by inverse analysis using in-situ and Satellite (GOSAT) atmospheric observations
GIScience & Remote Sensing
Methane emissions
inverse modeling
GOSAT
Australia
title Methane emissions from Australia estimated by inverse analysis using in-situ and Satellite (GOSAT) atmospheric observations
title_full Methane emissions from Australia estimated by inverse analysis using in-situ and Satellite (GOSAT) atmospheric observations
title_fullStr Methane emissions from Australia estimated by inverse analysis using in-situ and Satellite (GOSAT) atmospheric observations
title_full_unstemmed Methane emissions from Australia estimated by inverse analysis using in-situ and Satellite (GOSAT) atmospheric observations
title_short Methane emissions from Australia estimated by inverse analysis using in-situ and Satellite (GOSAT) atmospheric observations
title_sort methane emissions from australia estimated by inverse analysis using in situ and satellite gosat atmospheric observations
topic Methane emissions
inverse modeling
GOSAT
Australia
url https://www.tandfonline.com/doi/10.1080/15481603.2025.2488595
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