Articles | Volume 25, issue 21
https://doi.org/10.5194/acp-25-15121-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-25-15121-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Nov 2025
Research article |
| 07 Nov 2025
| 07 Nov 2025
High-resolution regional inversion reveals overestimation of anthropogenic methane emissions in China
Shuzhuang Feng
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing, 210023, China
Yongguang Zhang
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
Huilin Chen
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Honglin Zhuang
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
Shumin Wang
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Shanxi Province Institute of Meteorological Sciences, Taiyuan 030002, China
Shengxi Bai
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
Hengmao Wang
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
Weimin Ju
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
Related authors
Yu Mao, Weimin Ju, Hengmao Wang, Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang
Atmos. Chem. Phys., 25, 14187–14204, https://doi.org/10.5194/acp-25-14187-2025, https://doi.org/10.5194/acp-25-14187-2025, 2025
Short summary
Short summary
The Russia-Ukraine war caused significant NOx emission reductions in Ukraine, exceeding COVID-19 impacts. Using TROPOMI data, we found a 15% drop in 2022 and 8% in 2023, with Eastern Ukraine (frontline) seeing declines of 29% and 17%. Industry emissions fell most (34% in 2022), while war-related NOx partially offset reductions (8-10%). Emissions closely tracked military actions, revealing conflict’s disruptive effects on socio-economic activity and air pollution.
Shuzhuang Feng, Fei Jiang, Tianlu Qian, Nan Wang, Mengwei Jia, Songci Zheng, Jiansong Chen, Fang Ying, and Weimin Ju
Atmos. Chem. Phys., 24, 7481–7498, https://doi.org/10.5194/acp-24-7481-2024, https://doi.org/10.5194/acp-24-7481-2024, 2024
Short summary
Short summary
We developed a multi-air-pollutant inversion system to estimate non-methane volatile organic compound (NMVOC) emissions using TROPOMI formaldehyde retrievals. We found that the inversion significantly improved formaldehyde simulations and reduced NMVOC emission uncertainties. The optimized NMVOC emissions effectively corrected the overestimation of O3 levels, mainly by decreasing the rate of the RO2 + NO reaction and increasing the rate of the NO2 + OH reaction.
Shuzhuang Feng, Fei Jiang, Zheng Wu, Hengmao Wang, Wei He, Yang Shen, Lingyu Zhang, Yanhua Zheng, Chenxi Lou, Ziqiang Jiang, and Weimin Ju
Geosci. Model Dev., 16, 5949–5977, https://doi.org/10.5194/gmd-16-5949-2023, https://doi.org/10.5194/gmd-16-5949-2023, 2023
Short summary
Short summary
We document the system development and application of a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0). This system is developed to optimize gridded source emissions of CO, SO2, NOx, primary PM2.5, and coarse PM10 on a regional scale via simultaneously assimilating surface measurements of CO, SO2, NO2, PM2.5, and PM10. A series of sensitivity experiments demonstrates the advantage of the “two-step” inversion strategy and the robustness of the system in estimating the emissions.
Fei Jiang, Weimin Ju, Wei He, Mousong Wu, Hengmao Wang, Jun Wang, Mengwei Jia, Shuzhuang Feng, Lingyu Zhang, and Jing M. Chen
Earth Syst. Sci. Data, 14, 3013–3037, https://doi.org/10.5194/essd-14-3013-2022, https://doi.org/10.5194/essd-14-3013-2022, 2022
Short summary
Short summary
A 10-year (2010–2019) global monthly terrestrial NEE dataset (GCAS2021) was inferred from the GOSAT ACOS v9 XCO2 product. It shows strong carbon sinks over eastern N. America, the Amazon, the Congo Basin, Europe, boreal forests, southern China, and Southeast Asia. It has good quality and can reflect the impacts of extreme climates and large-scale climate anomalies on carbon fluxes well. We believe that this dataset can contribute to regional carbon budget assessment and carbon dynamics research.
Fei Jiang, Hengmao Wang, Jing M. Chen, Weimin Ju, Xiangjun Tian, Shuzhuang Feng, Guicai Li, Zhuoqi Chen, Shupeng Zhang, Xuehe Lu, Jane Liu, Haikun Wang, Jun Wang, Wei He, and Mousong Wu
Atmos. Chem. Phys., 21, 1963–1985, https://doi.org/10.5194/acp-21-1963-2021, https://doi.org/10.5194/acp-21-1963-2021, 2021
Short summary
Short summary
We present a 6-year inversion from 2010 to 2015 for the global and regional carbon fluxes using only the GOSAT XCO2 retrievals. We find that the XCO2 retrievals could significantly improve the modeling of atmospheric CO2 concentrations and that the inferred interannual variations in the terrestrial carbon fluxes in most land regions have a better relationship with the changes in severe drought area or leaf area index, or are more consistent with the previous estimates about drought impact.
Yu Mao, Weimin Ju, Hengmao Wang, Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang
Atmos. Chem. Phys., 25, 14187–14204, https://doi.org/10.5194/acp-25-14187-2025, https://doi.org/10.5194/acp-25-14187-2025, 2025
Short summary
Short summary
The Russia-Ukraine war caused significant NOx emission reductions in Ukraine, exceeding COVID-19 impacts. Using TROPOMI data, we found a 15% drop in 2022 and 8% in 2023, with Eastern Ukraine (frontline) seeing declines of 29% and 17%. Industry emissions fell most (34% in 2022), while war-related NOx partially offset reductions (8-10%). Emissions closely tracked military actions, revealing conflict’s disruptive effects on socio-economic activity and air pollution.
Johannes C. Laube, Tanja J. Schuck, Sophie Baartman, Huilin Chen, Markus Geldenhuys, Steven van Heuven, Timo Keber, Maria Elena Popa, Elinor Tuffnell, Florian Voet, Bärbel Vogel, Thomas Wagenhäuser, Alessandro Zanchetta, and Andreas Engel
Atmos. Meas. Tech., 18, 4087–4102, https://doi.org/10.5194/amt-18-4087-2025, https://doi.org/10.5194/amt-18-4087-2025, 2025
Short summary
Short summary
A large balloon was launched in summer 2021 in the Arctic to carry instruments for trace gas measurements up to 32 km, above the reach of aircraft. The main aims were to evaluate different techniques and atmospheric processes. We focus on halogenated greenhouse gases and ozone-depleting substances. For this, air was collected with the AirCore technique and a cryogenic air sampler and measured after the flight. A companion paper reports observations of major greenhouse gases.
Paul Waldmann, Max Eckl, Leon Knez, Klaus-Dirk Gottschaldt, Alina Fiehn, Christian Mallaun, Michal Galkowski, Christoph Kiemle, Ronald Hutjes, Thomas Röckmann, Huilin Chen, and Anke Roiger
EGUsphere, https://doi.org/10.5194/egusphere-2025-3297, https://doi.org/10.5194/egusphere-2025-3297, 2025
Short summary
Short summary
Nitrous oxide and methane emissions from agriculture need to be reduced, therefore emissions must be understood to effectively mitigate them. This is the first approach to measure those emissions aircraft-based, to assess their magnitude and drivers. We identified emission hotspots and temporal changes in agricultural emissions in the Netherlands. Our approach is applicable to further greenhouse gas emitters, therefore it builds a step towards more comprehensive emission quantification.
Alessandro Zanchetta, Steven van Heuven, Joram Hooghiem, Rigel Kivi, Thomas Laemmel, Michel Ramonet, Markus Leuenberger, Peter Nyfeler, Sophia Louise Baartman, Maarten Krol, and Huilin Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-3079, https://doi.org/10.5194/egusphere-2025-3079, 2025
Short summary
Short summary
Continuous vertical profiles and discrete stratospheric samples of carbonyl sulfide (COS) were collected deploying the balloon-borne AirCore, LISA and BigLISA samplers and measured on a Quantum Cascade Laser Spectrometer (QCLS). Our measurements show good accordance with previous COS observations. Moreover, laboratory tests of ozone (O3) scrubbers proved squalene to remove O3 very efficiently without biasing the measurements of other trace gases.
Rong Shang, Xudong Lin, Jing M. Chen, Yunjian Liang, Keyan Fang, Mingzhu Xu, Yulin Yan, Weimin Ju, Guirui Yu, Nianpeng He, Li Xu, Liangyun Liu, Jing Li, Wang Li, Jun Zhai, and Zhongmin Hu
Earth Syst. Sci. Data, 17, 3219–3241, https://doi.org/10.5194/essd-17-3219-2025, https://doi.org/10.5194/essd-17-3219-2025, 2025
Short summary
Short summary
Forest age is critical for carbon cycle modeling and effective forest management. Existing datasets, however, have low spatial resolutions or limited temporal coverage. This study introduces China's annual forest age dataset (CAFA), spanning 1986–2022 at a 30 m resolution. By tracking forest disturbances, we annually update ages. Validation shows small errors for disturbed forests and larger errors for undisturbed forests. CAFA can enhance carbon cycle modeling and forest management in China.
Tanja J. Schuck, Johannes Degen, Timo Keber, Katharina Meixner, Thomas Wagenhäuser, Mélanie Ghysels, Georges Durry, Nadir Amarouche, Alessandro Zanchetta, Steven van Heuven, Huilin Chen, Johannes C. Laube, Sophie L. Baartman, Carina van der Veen, Maria Elena Popa, and Andreas Engel
Atmos. Chem. Phys., 25, 4333–4348, https://doi.org/10.5194/acp-25-4333-2025, https://doi.org/10.5194/acp-25-4333-2025, 2025
Short summary
Short summary
A balloon was launched in 2021 in the Arctic to carry instruments for trace gas measurements up to 32 km. One purpose was to compare measurement techniques. We focus on the major greenhouse gases. To measure these, air was sampled with the AirCore technique and with flask sampling, and samples were analysed after the flight. In flight, observations were done with an optical method. In a companion paper, we report on observations of chlorine and bromine containing trace gases.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Xingyu Wang, Fei Jiang, Hengmao Wang, Zhengqi Zhang, Mousong Wu, Jun Wang, Wei He, Weimin Ju, and Jing M. Chen
Atmos. Chem. Phys., 25, 867–880, https://doi.org/10.5194/acp-25-867-2025, https://doi.org/10.5194/acp-25-867-2025, 2025
Short summary
Short summary
The role of OCO-3 XCO2 retrievals in estimating global terrestrial carbon fluxes is unclear. We investigate this by assimilating OCO-3 XCO2 retrievals alone and in combination with OCO-2 XCO2. The assimilation of OCO-3 XCO2 alone underestimates global land sinks, mainly at high latitudes, due to the lack of observations beyond 52° S and 52° N, large variations in the number of data, and varying observation times, while the joint assimilation of OCO-2 and OCO-3 XCO2 has the best performance.
Ran Yan, Jun Wang, Weimin Ju, Xiuli Xing, Miao Yu, Meirong Wang, Jingye Tan, Xunmei Wang, Hengmao Wang, and Fei Jiang
Biogeosciences, 21, 5027–5043, https://doi.org/10.5194/bg-21-5027-2024, https://doi.org/10.5194/bg-21-5027-2024, 2024
Short summary
Short summary
Our study reveals that the effects of the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on China's gross primary production (GPP) are basically opposite, with obvious seasonal changes. Soil moisture primarily influences GPP during ENSO events (except spring) and temperature during IOD events (except fall). Quantitatively, China's annual GPP displays modest positive anomalies during La Niña and negative anomalies in El Niño years, driven by significant seasonal variations.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Huajie Zhu, Xiuli Xing, Mousong Wu, Weimin Ju, and Fei Jiang
Biogeosciences, 21, 3735–3760, https://doi.org/10.5194/bg-21-3735-2024, https://doi.org/10.5194/bg-21-3735-2024, 2024
Short summary
Short summary
Ecosystem carbonyl sulfide (COS) fluxes were employed to optimize GPP estimation across ecosystems with the Biosphere-atmosphere Exchange Process Simulator (BEPS), which was developed for simulating the canopy COS uptake under its state-of-the-art two-leaf modeling framework. Our results showcased the efficacy of COS in improving model prediction and reducing prediction uncertainty of GPP and enhanced insights into the sensitivity, identifiability, and interactions of parameters related to COS.
Shuzhuang Feng, Fei Jiang, Tianlu Qian, Nan Wang, Mengwei Jia, Songci Zheng, Jiansong Chen, Fang Ying, and Weimin Ju
Atmos. Chem. Phys., 24, 7481–7498, https://doi.org/10.5194/acp-24-7481-2024, https://doi.org/10.5194/acp-24-7481-2024, 2024
Short summary
Short summary
We developed a multi-air-pollutant inversion system to estimate non-methane volatile organic compound (NMVOC) emissions using TROPOMI formaldehyde retrievals. We found that the inversion significantly improved formaldehyde simulations and reduced NMVOC emission uncertainties. The optimized NMVOC emissions effectively corrected the overestimation of O3 levels, mainly by decreasing the rate of the RO2 + NO reaction and increasing the rate of the NO2 + OH reaction.
Joshua L. Laughner, Geoffrey C. Toon, Joseph Mendonca, Christof Petri, Sébastien Roche, Debra Wunch, Jean-Francois Blavier, David W. T. Griffith, Pauli Heikkinen, Ralph F. Keeling, Matthäus Kiel, Rigel Kivi, Coleen M. Roehl, Britton B. Stephens, Bianca C. Baier, Huilin Chen, Yonghoon Choi, Nicholas M. Deutscher, Joshua P. DiGangi, Jochen Gross, Benedikt Herkommer, Pascal Jeseck, Thomas Laemmel, Xin Lan, Erin McGee, Kathryn McKain, John Miller, Isamu Morino, Justus Notholt, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Haris Riris, Constantina Rousogenous, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Steven C. Wofsy, Minqiang Zhou, and Paul O. Wennberg
Earth Syst. Sci. Data, 16, 2197–2260, https://doi.org/10.5194/essd-16-2197-2024, https://doi.org/10.5194/essd-16-2197-2024, 2024
Short summary
Short summary
This paper describes a new version, called GGG2020, of a data set containing column-integrated observations of greenhouse and related gases (including CO2, CH4, CO, and N2O) made by ground stations located around the world. Compared to the previous version (GGG2014), improvements have been made toward site-to-site consistency. This data set plays a key role in validating space-based greenhouse gas observations and in understanding the carbon cycle.
Michael Steiner, Wouter Peters, Ingrid Luijkx, Stephan Henne, Huilin Chen, Samuel Hammer, and Dominik Brunner
Atmos. Chem. Phys., 24, 2759–2782, https://doi.org/10.5194/acp-24-2759-2024, https://doi.org/10.5194/acp-24-2759-2024, 2024
Short summary
Short summary
The Paris Agreement increased interest in estimating greenhouse gas (GHG) emissions of individual countries, but top-down emission estimation is not yet considered policy-relevant. It is therefore paramount to reduce large errors and to build systems that are based on the newest atmospheric transport models. In this study, we present the first application of ICON-ART in the inverse modeling of GHG fluxes with an ensemble Kalman filter and present our results for European CH4 emissions.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Minqiang Zhou, Bavo Langerock, Mahesh Kumar Sha, Christian Hermans, Nicolas Kumps, Rigel Kivi, Pauli Heikkinen, Christof Petri, Justus Notholt, Huilin Chen, and Martine De Mazière
Atmos. Meas. Tech., 16, 5593–5608, https://doi.org/10.5194/amt-16-5593-2023, https://doi.org/10.5194/amt-16-5593-2023, 2023
Short summary
Short summary
Atmospheric N2O and CH4 columns are successfully retrieved from low-resolution FTIR spectra recorded by a Bruker VERTEX 70. The 1-year measurements at Sodankylä show that the N2O total columns retrieved from 125HR and VERTEX 70 spectra are −0.3 ± 0.7 % with an R value of 0.93. The relative differences between the CH4 total columns retrieved from the 125HR and VERTEX spectra are 0.0 ± 0.8 % with an R value of 0.87. Such a technique can help to fill the gap in NDACC N2O and CH4 measurements.
Shanlei Sun, Zaoying Bi, Jingfeng Xiao, Yi Liu, Ge Sun, Weimin Ju, Chunwei Liu, Mengyuan Mu, Jinjian Li, Yang Zhou, Xiaoyuan Li, Yibo Liu, and Haishan Chen
Earth Syst. Sci. Data, 15, 4849–4876, https://doi.org/10.5194/essd-15-4849-2023, https://doi.org/10.5194/essd-15-4849-2023, 2023
Short summary
Short summary
Based on various existing datasets, we comprehensively considered spatiotemporal differences in land surfaces and CO2 effects on plant stomatal resistance to parameterize the Shuttleworth–Wallace model, and we generated a global 5 km ensemble mean monthly potential evapotranspiration (PET) dataset (including potential transpiration PT and soil evaporation PE) during 1982–2015. The new dataset may be used by academic communities and various agencies to conduct various studies.
Shuzhuang Feng, Fei Jiang, Zheng Wu, Hengmao Wang, Wei He, Yang Shen, Lingyu Zhang, Yanhua Zheng, Chenxi Lou, Ziqiang Jiang, and Weimin Ju
Geosci. Model Dev., 16, 5949–5977, https://doi.org/10.5194/gmd-16-5949-2023, https://doi.org/10.5194/gmd-16-5949-2023, 2023
Short summary
Short summary
We document the system development and application of a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0). This system is developed to optimize gridded source emissions of CO, SO2, NOx, primary PM2.5, and coarse PM10 on a regional scale via simultaneously assimilating surface measurements of CO, SO2, NO2, PM2.5, and PM10. A series of sensitivity experiments demonstrates the advantage of the “two-step” inversion strategy and the robustness of the system in estimating the emissions.
Foteini Stavropoulou, Katarina Vinković, Bert Kers, Marcel de Vries, Steven van Heuven, Piotr Korbeń, Martina Schmidt, Julia Wietzel, Pawel Jagoda, Jaroslav M. Necki, Jakub Bartyzel, Hossein Maazallahi, Malika Menoud, Carina van der Veen, Sylvia Walter, Béla Tuzson, Jonas Ravelid, Randulph Paulo Morales, Lukas Emmenegger, Dominik Brunner, Michael Steiner, Arjan Hensen, Ilona Velzeboer, Pim van den Bulk, Hugo Denier van der Gon, Antonio Delre, Maklawe Essonanawe Edjabou, Charlotte Scheutz, Marius Corbu, Sebastian Iancu, Denisa Moaca, Alin Scarlat, Alexandru Tudor, Ioana Vizireanu, Andreea Calcan, Magdalena Ardelean, Sorin Ghemulet, Alexandru Pana, Aurel Constantinescu, Lucian Cusa, Alexandru Nica, Calin Baciu, Cristian Pop, Andrei Radovici, Alexandru Mereuta, Horatiu Stefanie, Alexandru Dandocsi, Bas Hermans, Stefan Schwietzke, Daniel Zavala-Araiza, Huilin Chen, and Thomas Röckmann
Atmos. Chem. Phys., 23, 10399–10412, https://doi.org/10.5194/acp-23-10399-2023, https://doi.org/10.5194/acp-23-10399-2023, 2023
Short summary
Short summary
In this study, we quantify CH4 emissions from onshore oil production sites in Romania at source and facility level using a combination of ground- and drone-based measurement techniques. We show that the total CH4 emissions in our studied areas are much higher than the emissions reported to UNFCCC, and up to three-quarters of the detected emissions are related to operational venting. Our results suggest that oil and gas production infrastructure in Romania holds a massive mitigation potential.
Alessandro Zanchetta, Linda M. J. Kooijmans, Steven van Heuven, Andrea Scifo, Hubertus A. Scheeren, Ivan Mammarella, Ute Karstens, Jin Ma, Maarten Krol, and Huilin Chen
Biogeosciences, 20, 3539–3553, https://doi.org/10.5194/bg-20-3539-2023, https://doi.org/10.5194/bg-20-3539-2023, 2023
Short summary
Short summary
Carbonyl sulfide (COS) has been suggested as a tool to estimate carbon dioxide (CO2) uptake by plants during photosynthesis. However, understanding its sources and sinks is critical to preventing biases in this estimate. Combining observations and models, this study proves that regional sources occasionally influence the measurements at the 60 m tall Lutjewad tower (1 m a.s.l.; 53°24′ N, 6°21′ E) in the Netherlands. Moreover, it estimates nighttime COS fluxes to be −3.0 ± 2.6 pmol m−2 s−1.
Truls Andersen, Zhao Zhao, Marcel de Vries, Jaroslaw Necki, Justyna Swolkien, Malika Menoud, Thomas Röckmann, Anke Roiger, Andreas Fix, Wouter Peters, and Huilin Chen
Atmos. Chem. Phys., 23, 5191–5216, https://doi.org/10.5194/acp-23-5191-2023, https://doi.org/10.5194/acp-23-5191-2023, 2023
Short summary
Short summary
The Upper Silesian Coal Basin, Poland, is one of the hot spots of methane emissions in Europe. Using an uncrewed aerial vehicle (UAV), we performed atmospheric measurements of methane concentrations downwind of five ventilation shafts in this region and determined the emission rates from the individual shafts. We found a strong correlation between quantified shaft-averaged emission rates and hourly inventory data, which also allows us to estimate the methane emissions from the entire region.
Joshua L. Laughner, Sébastien Roche, Matthäus Kiel, Geoffrey C. Toon, Debra Wunch, Bianca C. Baier, Sébastien Biraud, Huilin Chen, Rigel Kivi, Thomas Laemmel, Kathryn McKain, Pierre-Yves Quéhé, Constantina Rousogenous, Britton B. Stephens, Kaley Walker, and Paul O. Wennberg
Atmos. Meas. Tech., 16, 1121–1146, https://doi.org/10.5194/amt-16-1121-2023, https://doi.org/10.5194/amt-16-1121-2023, 2023
Short summary
Short summary
Observations using sunlight to measure surface-to-space total column of greenhouse gases in the atmosphere need an initial guess of the vertical distribution of those gases to start from. We have developed an approach to provide those initial guess profiles that uses readily available meteorological data as input. This lets us make these guesses without simulating them with a global model. The profiles generated this way match independent observations well.
Tianqi Shi, Zeyu Han, Ge Han, Xin Ma, Huilin Chen, Truls Andersen, Huiqin Mao, Cuihong Chen, Haowei Zhang, and Wei Gong
Atmos. Chem. Phys., 22, 13881–13896, https://doi.org/10.5194/acp-22-13881-2022, https://doi.org/10.5194/acp-22-13881-2022, 2022
Short summary
Short summary
CH4 works as the second-most important greenhouse gas, its reported emission inventories being far less than CO2. In this study, we developed a self-adjusted model to estimate the CH4 emission rate from strong point sources by the UAV-based AirCore system. This model would reduce the uncertainty in CH4 emission rate quantification accrued by errors in measurements of wind and concentration. Actual measurements on Pniówek coal demonstrate the high accuracy and stability of our developed model.
Peter Bergamaschi, Arjo Segers, Dominik Brunner, Jean-Matthieu Haussaire, Stephan Henne, Michel Ramonet, Tim Arnold, Tobias Biermann, Huilin Chen, Sebastien Conil, Marc Delmotte, Grant Forster, Arnoud Frumau, Dagmar Kubistin, Xin Lan, Markus Leuenberger, Matthias Lindauer, Morgan Lopez, Giovanni Manca, Jennifer Müller-Williams, Simon O'Doherty, Bert Scheeren, Martin Steinbacher, Pamela Trisolino, Gabriela Vítková, and Camille Yver Kwok
Atmos. Chem. Phys., 22, 13243–13268, https://doi.org/10.5194/acp-22-13243-2022, https://doi.org/10.5194/acp-22-13243-2022, 2022
Short summary
Short summary
We present a novel high-resolution inverse modelling system, "FLEXVAR", and its application for the inverse modelling of European CH4 emissions in 2018. The new system combines a high spatial resolution of 7 km x 7 km with a variational data assimilation technique, which allows CH4 emissions to be optimized from individual model grid cells. The high resolution allows the observations to be better reproduced, while the derived emissions show overall good consistency with two existing models.
Malika Menoud, Carina van der Veen, Dave Lowry, Julianne M. Fernandez, Semra Bakkaloglu, James L. France, Rebecca E. Fisher, Hossein Maazallahi, Mila Stanisavljević, Jarosław Nęcki, Katarina Vinkovic, Patryk Łakomiec, Janne Rinne, Piotr Korbeń, Martina Schmidt, Sara Defratyka, Camille Yver-Kwok, Truls Andersen, Huilin Chen, and Thomas Röckmann
Earth Syst. Sci. Data, 14, 4365–4386, https://doi.org/10.5194/essd-14-4365-2022, https://doi.org/10.5194/essd-14-4365-2022, 2022
Short summary
Short summary
Emission sources of methane (CH4) can be distinguished with measurements of CH4 stable isotopes. We present new measurements of isotope signatures of various CH4 sources in Europe, mainly anthropogenic, sampled from 2017 to 2020. The present database also contains the most recent update of the global signature dataset from the literature. The dataset improves CH4 source attribution and the understanding of the global CH4 budget.
Jing M. Chen, Rong Wang, Yihong Liu, Liming He, Holly Croft, Xiangzhong Luo, Han Wang, Nicholas G. Smith, Trevor F. Keenan, I. Colin Prentice, Yongguang Zhang, Weimin Ju, and Ning Dong
Earth Syst. Sci. Data, 14, 4077–4093, https://doi.org/10.5194/essd-14-4077-2022, https://doi.org/10.5194/essd-14-4077-2022, 2022
Short summary
Short summary
Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit chlorophyll fluorescence as a byproduct. Both chlorophyll pigments and fluorescence can be measured by Earth-orbiting satellite sensors. Here we demonstrate that leaf photosynthetic capacity can be reliably derived globally using these measurements. This new satellite-based information overcomes a bottleneck in global ecological research where such spatially explicit information is currently lacking.
Matthias Schneider, Benjamin Ertl, Qiansi Tu, Christopher J. Diekmann, Farahnaz Khosrawi, Amelie N. Röhling, Frank Hase, Darko Dubravica, Omaira E. García, Eliezer Sepúlveda, Tobias Borsdorff, Jochen Landgraf, Alba Lorente, André Butz, Huilin Chen, Rigel Kivi, Thomas Laemmel, Michel Ramonet, Cyril Crevoisier, Jérome Pernin, Martin Steinbacher, Frank Meinhardt, Kimberly Strong, Debra Wunch, Thorsten Warneke, Coleen Roehl, Paul O. Wennberg, Isamu Morino, Laura T. Iraci, Kei Shiomi, Nicholas M. Deutscher, David W. T. Griffith, Voltaire A. Velazco, and David F. Pollard
Atmos. Meas. Tech., 15, 4339–4371, https://doi.org/10.5194/amt-15-4339-2022, https://doi.org/10.5194/amt-15-4339-2022, 2022
Short summary
Short summary
We present a computationally very efficient method for the synergetic use of level 2 remote-sensing data products. We apply the method to IASI vertical profile and TROPOMI total column space-borne methane observations and thus gain sensitivity for the tropospheric methane partial columns, which is not achievable by the individual use of TROPOMI and IASI. These synergetic effects are evaluated theoretically and empirically by inter-comparisons to independent references of TCCON, AirCore, and GAW.
Fei Jiang, Weimin Ju, Wei He, Mousong Wu, Hengmao Wang, Jun Wang, Mengwei Jia, Shuzhuang Feng, Lingyu Zhang, and Jing M. Chen
Earth Syst. Sci. Data, 14, 3013–3037, https://doi.org/10.5194/essd-14-3013-2022, https://doi.org/10.5194/essd-14-3013-2022, 2022
Short summary
Short summary
A 10-year (2010–2019) global monthly terrestrial NEE dataset (GCAS2021) was inferred from the GOSAT ACOS v9 XCO2 product. It shows strong carbon sinks over eastern N. America, the Amazon, the Congo Basin, Europe, boreal forests, southern China, and Southeast Asia. It has good quality and can reflect the impacts of extreme climates and large-scale climate anomalies on carbon fluxes well. We believe that this dataset can contribute to regional carbon budget assessment and carbon dynamics research.
Randulph Morales, Jonas Ravelid, Katarina Vinkovic, Piotr Korbeń, Béla Tuzson, Lukas Emmenegger, Huilin Chen, Martina Schmidt, Sebastian Humbel, and Dominik Brunner
Atmos. Meas. Tech., 15, 2177–2198, https://doi.org/10.5194/amt-15-2177-2022, https://doi.org/10.5194/amt-15-2177-2022, 2022
Short summary
Short summary
Mapping trace gas emission plumes using in situ measurements from unmanned aerial vehicles (UAVs) is an emerging and attractive possibility to quantify emissions from localized sources. We performed an extensive controlled-release experiment to develop an optimal quantification method and to determine the related uncertainties under various environmental and sampling conditions. Our approach was successful in quantifying local methane sources from drone-based measurements.
Stephanie G. Stettz, Nicholas C. Parazoo, A. Anthony Bloom, Peter D. Blanken, David R. Bowling, Sean P. Burns, Cédric Bacour, Fabienne Maignan, Brett Raczka, Alexander J. Norton, Ian Baker, Mathew Williams, Mingjie Shi, Yongguang Zhang, and Bo Qiu
Biogeosciences, 19, 541–558, https://doi.org/10.5194/bg-19-541-2022, https://doi.org/10.5194/bg-19-541-2022, 2022
Short summary
Short summary
Uncertainty in the response of photosynthesis to temperature poses a major challenge to predicting the response of forests to climate change. In this paper, we study how photosynthesis in a mountainous evergreen forest is limited by temperature. This study highlights that cold temperature is a key factor that controls spring photosynthesis. Including the cold-temperature limitation in an ecosystem model improved its ability to simulate spring photosynthesis.
Linda M. J. Kooijmans, Ara Cho, Jin Ma, Aleya Kaushik, Katherine D. Haynes, Ian Baker, Ingrid T. Luijkx, Mathijs Groenink, Wouter Peters, John B. Miller, Joseph A. Berry, Jerome Ogée, Laura K. Meredith, Wu Sun, Kukka-Maaria Kohonen, Timo Vesala, Ivan Mammarella, Huilin Chen, Felix M. Spielmann, Georg Wohlfahrt, Max Berkelhammer, Mary E. Whelan, Kadmiel Maseyk, Ulli Seibt, Roisin Commane, Richard Wehr, and Maarten Krol
Biogeosciences, 18, 6547–6565, https://doi.org/10.5194/bg-18-6547-2021, https://doi.org/10.5194/bg-18-6547-2021, 2021
Short summary
Short summary
The gas carbonyl sulfide (COS) can be used to estimate photosynthesis. To adopt this approach on regional and global scales, we need biosphere models that can simulate COS exchange. So far, such models have not been evaluated against observations. We evaluate the COS biosphere exchange of the SiB4 model against COS flux observations. We find that the model is capable of simulating key processes in COS biosphere exchange. Still, we give recommendations for further improvement of the model.
Luis Guanter, Cédric Bacour, Andreas Schneider, Ilse Aben, Tim A. van Kempen, Fabienne Maignan, Christian Retscher, Philipp Köhler, Christian Frankenberg, Joanna Joiner, and Yongguang Zhang
Earth Syst. Sci. Data, 13, 5423–5440, https://doi.org/10.5194/essd-13-5423-2021, https://doi.org/10.5194/essd-13-5423-2021, 2021
Short summary
Short summary
Sun-induced chlorophyll fluorescence (SIF) is an electromagnetic signal emitted by plants in the red and far-red parts of the spectrum. It has a functional link to photosynthesis and can be measured by satellite instruments, which makes it an important variable for the remote monitoring of the photosynthetic activity of vegetation ecosystems around the world. In this contribution we present a SIF dataset derived from the new Sentinel-5P TROPOMI missions.
Fei Jiang, Hengmao Wang, Jing M. Chen, Weimin Ju, Xiangjun Tian, Shuzhuang Feng, Guicai Li, Zhuoqi Chen, Shupeng Zhang, Xuehe Lu, Jane Liu, Haikun Wang, Jun Wang, Wei He, and Mousong Wu
Atmos. Chem. Phys., 21, 1963–1985, https://doi.org/10.5194/acp-21-1963-2021, https://doi.org/10.5194/acp-21-1963-2021, 2021
Short summary
Short summary
We present a 6-year inversion from 2010 to 2015 for the global and regional carbon fluxes using only the GOSAT XCO2 retrievals. We find that the XCO2 retrievals could significantly improve the modeling of atmospheric CO2 concentrations and that the inferred interannual variations in the terrestrial carbon fluxes in most land regions have a better relationship with the changes in severe drought area or leaf area index, or are more consistent with the previous estimates about drought impact.
Camille Yver-Kwok, Carole Philippon, Peter Bergamaschi, Tobias Biermann, Francescopiero Calzolari, Huilin Chen, Sebastien Conil, Paolo Cristofanelli, Marc Delmotte, Juha Hatakka, Michal Heliasz, Ove Hermansen, Kateřina Komínková, Dagmar Kubistin, Nicolas Kumps, Olivier Laurent, Tuomas Laurila, Irene Lehner, Janne Levula, Matthias Lindauer, Morgan Lopez, Ivan Mammarella, Giovanni Manca, Per Marklund, Jean-Marc Metzger, Meelis Mölder, Stephen M. Platt, Michel Ramonet, Leonard Rivier, Bert Scheeren, Mahesh Kumar Sha, Paul Smith, Martin Steinbacher, Gabriela Vítková, and Simon Wyss
Atmos. Meas. Tech., 14, 89–116, https://doi.org/10.5194/amt-14-89-2021, https://doi.org/10.5194/amt-14-89-2021, 2021
Short summary
Short summary
The Integrated Carbon Observation System (ICOS) is a pan-European research infrastructure which provides harmonized and high-precision scientific data on the carbon cycle and the greenhouse gas (GHG) budget. All stations have to undergo a rigorous assessment before being labeled, i.e., receiving approval to join the network. In this paper, we present the labeling process for the ICOS atmospheric network through the 23 stations that were labeled between November 2017 and November 2019.
Joram J. D. Hooghiem, Maria Elena Popa, Thomas Röckmann, Jens-Uwe Grooß, Ines Tritscher, Rolf Müller, Rigel Kivi, and Huilin Chen
Atmos. Chem. Phys., 20, 13985–14003, https://doi.org/10.5194/acp-20-13985-2020, https://doi.org/10.5194/acp-20-13985-2020, 2020
Short summary
Short summary
Wildfires release a large quantity of pollutants that can reach the stratosphere through pyro-convection events. In September 2017, a stratospheric plume was accidentally sampled during balloon soundings in northern Finland. The source of the plume was identified to be wildfire smoke based on in situ measurements of carbon monoxide (CO) and stable isotope analysis of CO. Furthermore, the age of the plume was estimated using backwards transport modelling to be ~24 d, with its origin in Canada.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
Short summary
Short summary
Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
Cited articles
Agustí-Panareda, A., Barré, J., Massart, S., Inness, A., Aben, I., Ades, M., Baier, B. C., Balsamo, G., Borsdorff, T., Bousserez, N., Boussetta, S., Buchwitz, M., Cantarello, L., Crevoisier, C., Engelen, R., Eskes, H., Flemming, J., Garrigues, S., Hasekamp, O., Huijnen, V., Jones, L., Kipling, Z., Langerock, B., McNorton, J., Meilhac, N., Noël, S., Parrington, M., Peuch, V.-H., Ramonet, M., Razinger, M., Reuter, M., Ribas, R., Suttie, M., Sweeney, C., Tarniewicz, J., and Wu, L.: Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020, Atmos. Chem. Phys., 23, 3829–3859, https://doi.org/10.5194/acp-23-3829-2023, 2023.
Alexe, M., Bergamaschi, P., Segers, A., Detmers, R., Butz, A., Hasekamp, O., Guerlet, S., Parker, R., Boesch, H., Frankenberg, C., Scheepmaker, R. A., Dlugokencky, E., Sweeney, C., Wofsy, S. C., and Kort, E. A.: Inverse modelling of CH4 emissions for 2010–2011 using different satellite retrieval products from GOSAT and SCIAMACHY, Atmos. Chem. Phys., 15, 113–133, https://doi.org/10.5194/acp-15-113-2015, 2015.
Bai, S., Zhang, Y., Li, F., Yan, Y., Chen, H., Feng, S., Jiang, F., Sun, S., Wang, Z., Zhou, C., Zhou, W., and Zhao, S.: High-resolution satellite estimates of coal mine methane emissions from local to regional scales in Shanxi, China, Science of The Total Environment, 950, 175446, https://doi.org/10.1016/j.scitotenv.2024.175446, 2024.
Bergamaschi, P., Houweling, S., Segers, A., Krol, M., Frankenberg, C., Scheepmaker, R. A., Dlugokencky, E., Wofsy, S. C., Kort, E. A., Sweeney, C., Schuck, T., Brenninkmeijer, C., Chen, H., Beck, V., and Gerbig, C.: Atmospheric CH4 in the first decade of the 21st century: Inverse modeling analysis using SCIAMACHY satellite retrievals and NOAA surface measurements, Journal of Geophysical Research: Atmospheres, 118, 7350–7369, https://doi.org/10.1002/jgrd.50480, 2013.
Bloom, A. A., Bowman, K. W., Lee, M., Turner, A. J., Schroeder, R., Worden, J. R., Weidner, R. J., McDonald, K. C., and Jacob, D. J.: CMS: Global 0.5-deg Wetland Methane Emissions and Uncertainty (WetCHARTs v1.3.1), ORNL Distributed Active Archive Center [data set], https://doi.org/10.3334/ORNLDAAC/1915, 2021.
Bruhwiler, L., Dlugokencky, E., Masarie, K., Ishizawa, M., Andrews, A., Miller, J., Sweeney, C., Tans, P., and Worthy, D.: CarbonTracker-CH4: an assimilation system for estimating emissions of atmospheric methane, Atmos. Chem. Phys., 14, 8269–8293, https://doi.org/10.5194/acp-14-8269-2014, 2014.
Byun, D. and Schere, K. L.: Review of the governing equations, computational algorithms, and other components of the models-3 Community Multiscale Air Quality (CMAQ) modeling system, Applied Mechanics Reviews, 59, 51–77, https://doi.org/10.1115/1.2128636, 2006.
Chen, Z., Jacob, D. J., Nesser, H., Sulprizio, M. P., Lorente, A., Varon, D. J., Lu, X., Shen, L., Qu, Z., Penn, E., and Yu, X.: Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations, Atmos. Chem. Phys., 22, 10809–10826, https://doi.org/10.5194/acp-22-10809-2022, 2022.
Chen, Z., Lin, H., Balasus, N., Hardy, A., East, J. D., Zhang, Y., Runkle, B. R. K., Hancock, S. E., Taylor, C. A., Du, X., Sander, B. O., and Jacob, D. J.: Global Rice Paddy Inventory (GRPI): A High-Resolution Inventory of Methane Emissions From Rice Agriculture Based on Landsat Satellite Inundation Data, Earth's Future, 13, e2024EF005479, https://doi.org/10.1029/2024EF005479, 2025.
Crippa, M., Guizzardi, D., Pagani, F., Schiavina, M., Melchiorri, M., Pisoni, E., Graziosi, F., Muntean, M., Maes, J., Dijkstra, L., Van Damme, M., Clarisse, L., and Coheur, P.: Insights into the spatial distribution of global, national, and subnational greenhouse gas emissions in the Emissions Database for Global Atmospheric Research (EDGAR v8.0), Earth Syst. Sci. Data, 16, 2811–2830, https://doi.org/10.5194/essd-16-2811-2024, 2024.
EPA: Global Non-CO2 Greenhouse Gas Emission Projections & Mitigation Potential: 2015–2050, https://www.epa.gov/global-mitigation-non-co2-greenhouse-gases/global-non-co2-greenhouse-gas-emission-projections (last access: 25 April 2025), 2019.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, Journal Of Geophysical Research-Oceans, 99, 10143–10162, https://doi.org/10.1029/94jc00572, 1994.
Feng, S.: Anthropogenic CH4 Emissions over China in 2022 Inverted Using TROPOMI XCH4 Retrievals, Zenodo [data set], https://doi.org/10.5281/zenodo.15602944, 2025.
Feng, S., Jiang, F., Wu, Z., Wang, H., Ju, W., and Wang, H.: CO Emissions Inferred From Surface CO Observations Over China in December 2013 and 2017, Journal of Geophysical Research-Atmospheres, 125, https://doi.org/10.1029/2019jd031808, 2020.
Feng, S., Jiang, F., Wang, H., Shen, Y., Zheng, Y., Zhang, L., Lou, C., and Ju, W.: Anthropogenic emissions estimated using surface observations and their impacts on PM2.5 source apportionment over the Yangtze River Delta, China, Science of The Total Environment, 828, 154522, https://doi.org/10.1016/j.scitotenv.2022.154522, 2022.
Feng, S., Jiang, F., Wu, Z., Wang, H., He, W., Shen, Y., Zhang, L., Zheng, Y., Lou, C., Jiang, Z., and Ju, W.: A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application, Geosci. Model Dev., 16, 5949–5977, https://doi.org/10.5194/gmd-16-5949-2023, 2023.
Feng, S., Jiang, F., Wang, H., Liu, Y., He, W., Wang, H., Shen, Y., Zhang, L., Jia, M., Ju, W., and Chen, J. M.: China's Fossil Fuel CO2 Emissions Estimated Using Surface Observations of Coemitted NO2, Environmental Science and Technology, 58, 8299-8312, https://doi.org/10.1021/acs.est.3c07756, 2024.
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D. J., Palmer, T. M. M. D., Watanabe, M., Wild, M., and Zhang, H.: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, 923–1054, https://doi.org/10.1017/9781009157896.009, 2021.
Gao, J., Guan, C., and Zhang, B.: China's CH4 emissions from coal mining: A review of current bottom-up inventories, Science of The Total Environment, 725, 138295, https://doi.org/10.1016/j.scitotenv.2020.138295, 2020.
Gao, J., Guan, C., Zhang, B., and Li, K.: Decreasing methane emissions from China's coal mining with rebounded coal production, Environmental Research Letters, 16, 124037, https://doi.org/10.1088/1748-9326/ac38d8, 2021.
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Quarterly Journal of the Royal Meteorological Society, 125, 723–757, https://doi.org/10.1256/smsqj.55416, 1999.
GMP: Global methane Pledge, https://www.globalmethanepledge.org/resources/global-methane-pledge (last access: 12 February 2025), 2023.
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018.
Houtekamer, P. L. and Mitchell, H. L.: A sequential ensemble Kalman filter for atmospheric data assimilation, Monthly Weather Review, 129, 123–137, https://doi.org/10.1175/1520-0493(2001)129<0123:asekff>2.0.co;2, 2001.
Hu, C., Zhang, J., Qi, B., Du, R., Xu, X., Xiong, H., Liu, H., Ai, X., Peng, Y., and Xiao, W.: Global warming will largely increase waste treatment CH4 emissions in Chinese megacities: insight from the first city-scale CH4 concentration observation network in Hangzhou, China, Atmos. Chem. Phys., 23, 4501–4520, https://doi.org/10.5194/acp-23-4501-2023, 2023.
Hu, J., Chen, J., Ying, Q., and Zhang, H.: One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system, Atmos. Chem. Phys., 16, 10333–10350, https://doi.org/10.5194/acp-16-10333-2016, 2016.
IEA: Methane Tracker 2021, https://www.iea.org/reports/methane-tracker-2021 (last access: 5 April 2025), 2021.
Jamali, H., Livesley, S. J., Dawes, T. Z., Cook, G. D., Hutley, L. B., and Arndt, S. K.: Diurnal and seasonal variations in CH4 flux from termite mounds in tropical savannas of the Northern Territory, Australia, Agricultural and Forest Meteorology, 151, 1471–1479, https://doi.org/10.1016/j.agrformet.2010.06.009, 2011.
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., Bergamaschi, P., Pagliari, V., Olivier, J. G. J., Peters, J. A. H. W., van Aardenne, J. A., Monni, S., Doering, U., Petrescu, A. M. R., Solazzo, E., and Oreggioni, G. D.: EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012, Earth Syst. Sci. Data, 11, 959–1002, https://doi.org/10.5194/essd-11-959-2019, 2019.
Kou, X., Peng, Z., Han, X., Li, J., Qin, L., Zhang, M., Parker, R. J., and Boesch, H.: China's methane emissions derived from the inversion of GOSAT observations with a CMAQ and EnKS-based regional data assimilation system, Atmospheric Pollution Research, 16, 102333, https://doi.org/10.1016/j.apr.2024.102333, 2025.
Lambert, J.-C., A. Keppens, S. Compernolle, K.-U. Eichmann, M. de Graaf, D. Hubert, B. Langerock, M.K. Sha, E. van der Plas, T. Verhoelst, T. Wagner, C. Ahn, A. Argyrouli, D. Balis, K.L. Chan, M. Coldewey-Egbers, I. De Smedt, H. Eskes, A.M. Fjæraa, K. Garane, J.F. Gleason, J. Granville, P. Hedelt, K.-P. Heue, G. Jaross, ML. Koukouli, E. Loots, R. Lutz, M.C Martinez Velarte, K. Michailidis, A. Pseftogkas, S. Nanda, S. Niemeijer, A. Pazmiño, G. Pinardi, A. Richter, N. Rozemeijer, M. Sneep, D. Stein Zweers, N. Theys, G. Tilstra, O. Torres, P. Valks, J. van Geffen, C. Vigouroux, P. Wang, and M. Weber.: S5P MPC Routine Operations Consolidated Validation Report series, Issue #27, Version 27.01.00, 227 pp., S5P-MPC-IASB-ROCVR-27.01.00-20250615, 15 June 2025.
Lelieveld, J., Gromov, S., Pozzer, A., and Taraborrelli, D.: Global tropospheric hydroxyl distribution, budget and reactivity, Atmos. Chem. Phys., 16, 12477–12493, https://doi.org/10.5194/acp-16-12477-2016, 2016.
Liang, R., Zhang, Y., Chen, W., Zhang, P., Liu, J., Chen, C., Mao, H., Shen, G., Qu, Z., Chen, Z., Zhou, M., Wang, P., Parker, R. J., Boesch, H., Lorente, A., Maasakkers, J. D., and Aben, I.: East Asian methane emissions inferred from high-resolution inversions of GOSAT and TROPOMI observations: a comparative and evaluative analysis, Atmos. Chem. Phys., 23, 8039–8057, https://doi.org/10.5194/acp-23-8039-2023, 2023.
Liang, R., Zhang, Y., Hu, Q., Li, T., Li, S., Yuan, W., Xu, J., Zhao, Y., Zhang, P., Chen, W., Zhuang, M., Shen, G., and Chen, Z.: Satellite-Based Monitoring of Methane Emissions from China's Rice Hub, Environmental Science and Technology, 58, 23127–23137, https://doi.org/10.1021/acs.est.4c09822, 2024.
Lin, X., Zhang, W., Crippa, M., Peng, S., Han, P., Zeng, N., Yu, L., and Wang, G.: A comparative study of anthropogenic CH4 emissions over China based on the ensembles of bottom-up inventories, Earth Syst. Sci. Data, 13, 1073–1088, https://doi.org/10.5194/essd-13-1073-2021, 2021.
Liu, G., Peng, S., Lin, X., Ciais, P., Li, X., Xi, Y., Lu, Z., Chang, J., Saunois, M., Wu, Y., Patra, P., Chandra, N., Zeng, H., and Piao, S.: Recent Slowdown of Anthropogenic Methane Emissions in China Driven by Stabilized Coal Production, Environmental Science and Technology Letters, 8, 739–746, https://doi.org/10.1021/acs.estlett.1c00463, 2021.
Lorente, A., Borsdorff, T., Martinez-Velarte, M. C., Butz, A., Hasekamp, O. P., Wu, L., and Landgraf, J.: Evaluation of the methane full-physics retrieval applied to TROPOMI ocean sun glint measurements, Atmos. Meas. Tech., 15, 6585–6603, https://doi.org/10.5194/amt-15-6585-2022, 2022.
Lu, X., Jacob, D. J., Zhang, Y., Shen, L., Sulprizio, M. P., Maasakkers, J. D., Varon, D. J., Qu, Z., Chen, Z., Hmiel, B., Parker, R. J., Boesch, H., Wang, H., He, C., and Fan, S.: Observation-derived 2010-2019 trends in methane emissions and intensities from US oil and gas fields tied to activity metrics, Proceedings of the National Academy of Sciences, 120, e2217900120, https://doi.org/10.1073/pnas.2217900120, 2023.
Lu, Y. Y., Zhang, H. D., Zhou, Z., Ge, Z. L., Chen, C. J., Hou, Y. D., and Ye, M. L.: Current Status and Effective Suggestions for Efficient Exploitation of Coalbed Methane in China: A Review, Energy and Fuels, 35, 9102–9123, https://doi.org/10.1021/acs.energyfuels.1c00460, 2021.
Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Scarpelli, T. R., Nesser, H., Sheng, J.-X., Zhang, Y., Hersher, M., Bloom, A. A., Bowman, K. W., Worden, J. R., Janssens-Maenhout, G., and Parker, R. J.: Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015, Atmos. Chem. Phys., 19, 7859–7881, https://doi.org/10.5194/acp-19-7859-2019, 2019.
MECAP: Methane Emission Control Action Plan, https://www.mee.gov.cn/xxgk2018/xxgk/xxgk03/202311/t20231107_1055437.html (last access: 12 May 2025), 2023.
Miller, S. M., Michalak, A. M., Detmers, R. G., Hasekamp, O. P., Bruhwiler, L. M. P., and Schwietzke, S.: China's coal mine methane regulations have not curbed growing emissions, Nature Communications, 10, 303, https://doi.org/10.1038/s41467-018-07891-7, 2019.
Miyazaki, K., Eskes, H., Sudo, K., Boersma, K. F., Bowman, K., and Kanaya, Y.: Decadal changes in global surface NOx emissions from multi-constituent satellite data assimilation, Atmos. Chem. Phys., 17, 807–837, https://doi.org/10.5194/acp-17-807-2017, 2017.
Murguia-Flores, F., Arndt, S., Ganesan, A. L., Murray-Tortarolo, G., and Hornibrook, E. R. C.: Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil, Geosci. Model Dev., 11, 2009–2032, https://doi.org/10.5194/gmd-11-2009-2018, 2018.
Nassar, R., Hill, T. G., McLinden, C. A., Wunch, D., Jones, D. B. A., and Crisp, D.: Quantifying CO2 Emissions From Individual Power Plants From Space, Geophysical Research Letters, 44, 10045–10053, https://doi.org/10.1002/2017GL074702, 2017.
Nesser, H., Jacob, D. J., Maasakkers, J. D., Lorente, A., Chen, Z., Lu, X., Shen, L., Qu, Z., Sulprizio, M. P., Winter, M., Ma, S., Bloom, A. A., Worden, J. R., Stavins, R. N., and Randles, C. A.: High-resolution US methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills, Atmos. Chem. Phys., 24, 5069–5091, https://doi.org/10.5194/acp-24-5069-2024, 2024.
Nisbet, E. G.: New hope for methane reduction, Science, 382, 1093–1093, https://doi.org/10.1126/science.adn0134, 2023.
Pandey, S., Houweling, S., Krol, M., Aben, I., Chevallier, F., Dlugokencky, E. J., Gatti, L. V., Gloor, E., Miller, J. B., Detmers, R., Machida, T., and Röckmann, T.: Inverse modeling of GOSAT-retrieved ratios of total column CH4 and CO2 for 2009 and 2010, Atmos. Chem. Phys., 16, 5043–5062, https://doi.org/10.5194/acp-16-5043-2016, 2016.
Pandey, S., Gautam, R., Houweling, S., van der Gon, H. D., Sadavarte, P., Borsdorff, T., Hasekamp, O., Landgraf, J., Tol, P., van Kempen, T., Hoogeveen, R., van Hees, R., Hamburg, S. P., Maasakkers, J. D., and Aben, I.: Satellite observations reveal extreme methane leakage from a natural gas well blowout, Proceedings of the National Academy of Sciences, 116, 26376–26381, https://doi.org/10.1073/pnas.1908712116, 2019.
Peng, S., Lin, X., Thompson, R. L., Xi, Y., Liu, G., Hauglustaine, D., Lan, X., Poulter, B., Ramonet, M., Saunois, M., Yin, Y., Zhang, Z., Zheng, B., and Ciais, P.: Wetland emission and atmospheric sink changes explain methane growth in 2020, Nature, 612, 477–482, https://doi.org/10.1038/s41586-022-05447-w, 2022.
Peng, S., Giron, C., Liu, G., d'Aspremont, A., Benoit, A., Lauvaux, T., Lin, X., de Almeida Rodrigues, H., Saunois, M., and Ciais, P.: High-resolution assessment of coal mining methane emissions by satellite in Shanxi, China, iScience, 26, 108375, https://doi.org/10.1016/j.isci.2023.108375, 2023.
Prather, M. J.: Lifetimes and time scales in atmospheric chemistry, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365, 1705–1726, https://doi.org/10.1098/rsta.2007.2040, 2007.
Qin, K., Hu, W., He, Q., Lu, F., and Cohen, J. B.: Individual coal mine methane emissions constrained by eddy covariance measurements: low bias and missing sources, Atmos. Chem. Phys., 24, 3009–3028, https://doi.org/10.5194/acp-24-3009-2024, 2024.
Qu, Z., Jacob, D. J., Shen, L., Lu, X., Zhang, Y., Scarpelli, T. R., Nesser, H., Sulprizio, M. P., Maasakkers, J. D., Bloom, A. A., Worden, J. R., Parker, R. J., and Delgado, A. L.: Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments, Atmos. Chem. Phys., 21, 14159–14175, https://doi.org/10.5194/acp-21-14159-2021, 2021.
Saunois, M., Martinez, A., Poulter, B., Zhang, Z., Raymond, P. A., Regnier, P., Canadell, J. G., Jackson, R. B., Patra, P. K., Bousquet, P., Ciais, P., Dlugokencky, E. J., Lan, X., Allen, G. H., Bastviken, D., Beerling, D. J., Belikov, D. A., Blake, D. R., Castaldi, S., Crippa, M., Deemer, B. R., Dennison, F., Etiope, G., Gedney, N., Höglund-Isaksson, L., Holgerson, M. A., Hopcroft, P. O., Hugelius, G., Ito, A., Jain, A. K., Janardanan, R., Johnson, M. S., Kleinen, T., Krummel, P. B., Lauerwald, R., Li, T., Liu, X., McDonald, K. C., Melton, J. R., Mühle, J., Müller, J., Murguia-Flores, F., Niwa, Y., Noce, S., Pan, S., Parker, R. J., Peng, C., Ramonet, M., Riley, W. J., Rocher-Ros, G., Rosentreter, J. A., Sasakawa, M., Segers, A., Smith, S. J., Stanley, E. H., Thanwerdas, J., Tian, H., Tsuruta, A., Tubiello, F. N., Weber, T. S., van der Werf, G. R., Worthy, D. E. J., Xi, Y., Yoshida, Y., Zhang, W., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q.: Global Methane Budget 2000–2020, Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, 2025.
Scarpelli, T. R., Jacob, D. J., Grossman, S., Lu, X., Qu, Z., Sulprizio, M. P., Zhang, Y., Reuland, F., Gordon, D., and Worden, J. R.: Updated Global Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas, and coal sectors: evaluation with inversions of atmospheric methane observations, Atmos. Chem. Phys., 22, 3235–3249, https://doi.org/10.5194/acp-22-3235-2022, 2022.
Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm, Atmos. Meas. Tech., 16, 669–694, https://doi.org/10.5194/amt-16-669-2023, 2023.
Sheng, J., Song, S., Zhang, Y., Prinn, R. G., and Janssens-Maenhout, G.: Bottom-Up Estimates of Coal Mine Methane Emissions in China: A Gridded Inventory, Emission Factors, and Trends, Environmental Science and Technology Letters, 6, 473–478, https://doi.org/10.1021/acs.estlett.9b00294, 2019.
Sheng, J., Tunnicliffe, R., Ganesan, A. L., Maasakkers, J. D., Shen, L., Prinn, R. G., Song, S., Zhang, Y., Scarpelli, T., Anthony Bloom, A., Rigby, M., Manning, A. J., Parker, R. J., Boesch, H., Lan, X., Zhang, B., Zhuang, M., and Lu, X.: Sustained methane emissions from China after 2012 despite declining coal production and rice-cultivated area, Environmental Research Letters, 16, 104018, https://doi.org/10.1088/1748-9326/ac24d1, 2021.
Shi, X., Peng, Y., Wang, S., Zhang, Y., Zhang, J., Song, H., Cui, Y., Sun, F., Liu, H., Xiao, Q., Hu, N., Xiao, W., Griffis, T. J., and Hu, C.: Revising the coal mining CH4 emission factor based on multiple inventories and atmospheric inversion approach at one of the world's largest coal production areas: Shanxi province, China, Science of The Total Environment, 965, 178616, https://doi.org/10.1016/j.scitotenv.2025.178616, 2025.
Skamarock, W. C. and Klemp, J. B.: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications, Journal Of Computational Physics, 227, 3465-3485, https://doi.org/10.1016/j.jcp.2007.01.037, 2008.
Soulie, A., Granier, C., Darras, S., Zilbermann, N., Doumbia, T., Guevara, M., Jalkanen, J.-P., Keita, S., Liousse, C., Crippa, M., Guizzardi, D., Hoesly, R., and Smith, S. J.: Global anthropogenic emissions (CAMS-GLOB-ANT) for the Copernicus Atmosphere Monitoring Service simulations of air quality forecasts and reanalyses, Earth Syst. Sci. Data, 16, 2261–2279, https://doi.org/10.5194/essd-16-2261-2024, 2024.
Stavert, A. R., Saunois, M., Canadell, J. G., Poulter, B., Jackson, R. B., Regnier, P., Lauerwald, R., Raymond, P. A., Allen, G. H., Patra, P. K., Bergamaschi, P., Bousquet, P., Chandra, N., Ciais, P., Gustafson, A., Ishizawa, M., Ito, A., Kleinen, T., Maksyutov, S., McNorton, J., Melton, J. R., Müller, J., Niwa, Y., Peng, S., Riley, W. J., Segers, A., Tian, H., Tsuruta, A., Yin, Y., Zhang, Z., Zheng, B., and Zhuang, Q.: Regional trends and drivers of the global methane budget, Global Change Biology, 28, 182–200, https://doi.org/10.1111/gcb.15901, 2022.
Steiner, M., Peters, W., Luijkx, I., Henne, S., Chen, H., Hammer, S., and Brunner, D.: European CH4 inversions with ICON-ART coupled to the CarbonTracker Data Assimilation Shell, Atmos. Chem. Phys., 24, 2759–2782, https://doi.org/10.5194/acp-24-2759-2024, 2024.
Stohl, A., Aamaas, B., Amann, M., Baker, L. H., Bellouin, N., Berntsen, T. K., Boucher, O., Cherian, R., Collins, W., Daskalakis, N., Dusinska, M., Eckhardt, S., Fuglestvedt, J. S., Harju, M., Heyes, C., Hodnebrog, Ø., Hao, J., Im, U., Kanakidou, M., Klimont, Z., Kupiainen, K., Law, K. S., Lund, M. T., Maas, R., MacIntosh, C. R., Myhre, G., Myriokefalitakis, S., Olivié, D., Quaas, J., Quennehen, B., Raut, J.-C., Rumbold, S. T., Samset, B. H., Schulz, M., Seland, Ø., Shine, K. P., Skeie, R. B., Wang, S., Yttri, K. E., and Zhu, T.: Evaluating the climate and air quality impacts of short-lived pollutants, Atmos. Chem. Phys., 15, 10529–10566, https://doi.org/10.5194/acp-15-10529-2015, 2015.
Thanwerdas, J., Saunois, M., Berchet, A., Pison, I., Vaughn, B. H., Michel, S. E., and Bousquet, P.: Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate δ13C(CH4) and CH4: a case study with model LMDz-SACS, Geosci. Model Dev., 15, 4831–4851, https://doi.org/10.5194/gmd-15-4831-2022, 2022.
Thompson, R. L., Stohl, A., Zhou, L. X., Dlugokencky, E., Fukuyama, Y., Tohjima, Y., Kim, S.-Y., Lee, H., Nisbet, E. G., Fisher, R. E., Lowry, D., Weiss, R. F., Prinn, R. G., O'Doherty, S., Young, D., and White, J. W. C.: Methane emissions in East Asia for 2000–2011 estimated using an atmospheric Bayesian inversion, Journal of Geophysical Research: Atmospheres, 120, 4352–4369, https://doi.org/10.1002/2014JD022394, 2015.
Thorpe, A. K., Kort, E. A., Cusworth, D. H., Ayasse, A. K., Bue, B. D., Yadav, V., Thompson, D. R., Frankenberg, C., Herner, J., Falk, M., Green, R. O., Miller, C. E., and Duren, R. M.: Methane emissions decline from reduced oil, natural gas, and refinery production during COVID-19, Environmental Research Communications, 5, 021006, https://doi.org/10.1088/2515-7620/acb5e5, 2023.
Tsuruta, A., Aalto, T., Backman, L., Hakkarainen, J., van der Laan-Luijkx, I. T., Krol, M. C., Spahni, R., Houweling, S., Laine, M., Dlugokencky, E., Gomez-Pelaez, A. J., van der Schoot, M., Langenfelds, R., Ellul, R., Arduini, J., Apadula, F., Gerbig, C., Feist, D. G., Kivi, R., Yoshida, Y., and Peters, W.: Global methane emission estimates for 2000–2012 from CarbonTracker Europe-CH4 v1.0, Geosci. Model Dev., 10, 1261–1289, https://doi.org/10.5194/gmd-10-1261-2017, 2017.
Tu, Q., Hase, F., Qin, K., Cohen, J. B., Khosrawi, F., Zou, X., Schneider, M., and Lu, F.: Quantifying CH4 emissions from coal mine aggregation areas in Shanxi, China, using TROPOMI observations and the wind-assigned anomaly method, Atmos. Chem. Phys., 24, 4875–4894, https://doi.org/10.5194/acp-24-4875-2024, 2024.
Turner, A. J., Jacob, D. J., Wecht, K. J., Maasakkers, J. D., Lundgren, E., Andrews, A. E., Biraud, S. C., Boesch, H., Bowman, K. W., Deutscher, N. M., Dubey, M. K., Griffith, D. W. T., Hase, F., Kuze, A., Notholt, J., Ohyama, H., Parker, R., Payne, V. H., Sussmann, R., Sweeney, C., Velazco, V. A., Warneke, T., Wennberg, P. O., and Wunch, D.: Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data, Atmos. Chem. Phys., 15, 7049–7069, https://doi.org/10.5194/acp-15-7049-2015, 2015.
UNFCCC: Greenhouse gas inventory data interface, http://di.unfccc.int/detailed_data_by_party (last access: 10 April 2025), 2020.
van Wees, D., van der Werf, G. R., Randerson, J. T., Rogers, B. M., Chen, Y., Veraverbeke, S., Giglio, L., and Morton, D. C.: Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED), Geosci. Model Dev., 15, 8411–8437, https://doi.org/10.5194/gmd-15-8411-2022, 2022.
Varon, D. J., Jacob, D. J., Sulprizio, M., Estrada, L. A., Downs, W. B., Shen, L., Hancock, S. E., Nesser, H., Qu, Z., Penn, E., Chen, Z., Lu, X., Lorente, A., Tewari, A., and Randles, C. A.: Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations, Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, 2022.
Wang, F., Maksyutov, S., Tsuruta, A., Janardanan, R., Ito, A., Sasakawa, M., Machida, T., Morino, I., Yoshida, Y., Kaiser, J. W., Janssens-Maenhout, G., Dlugokencky, E. J., Mammarella, I., Lavric, J. V., and Matsunaga, T.: Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories, Remote Sensing, 11, 2489, https://doi.org/10.3390/rs11212489, 2019.
Wang, Y., Zhang, Y., Tian, X., Wang, X., Yuan, W., Ding, J., Jiang, F., Jin, Z., Ju, W., Liang, R., Lu, X., Shen, L., Sun, S., Wang, T., Zhang, H., Zhao, M., and Piao, S.: Towards verifying and improving estimations of China's CO2 and CH4 budgets using atmospheric inversions, National Science Review, 12, https://doi.org/10.1093/nsr/nwaf090, 2025.
Whitaker, J. S. and Hamill, T. M.: Ensemble data assimilation without perturbed observations, Monthly Weather Review, 130, 1913–1924, https://doi.org/10.1175/1520-0493(2002)130<1913:Edawpo>2.0.Co;2, 2002.
Wunch, D., Toon, G. C., Blavier, J.-F. L., Washenfelder, R. A., Notholt, J., Connor, B. J., Griffith, D. W. T., Sherlock, V., and Wennberg, P. O.: The Total Carbon Column Observing Network, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369, 2087-2112, https://doi.org/10.1098/rsta.2010.0240, 2011.
Zhang, X., Zhou, C., Zhang, Y., Lu, X., Xiao, X., Wang, F., Song, J., Guo, Y., Leung, K. K. M., Cao, J., and Gao, M.: Where to place methane monitoring sites in China to better assist carbon management, npj Climate and Atmospheric Science, 6, 32, https://doi.org/10.1038/s41612-023-00359-6, 2023.
Zhang, Y., Jacob, D. J., Lu, X., Maasakkers, J. D., Scarpelli, T. R., Sheng, J.-X., Shen, L., Qu, Z., Sulprizio, M. P., Chang, J., Bloom, A. A., Ma, S., Worden, J., Parker, R. J., and Boesch, H.: Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations, Atmos. Chem. Phys., 21, 3643–3666, https://doi.org/10.5194/acp-21-3643-2021, 2021.
Zhang, Y., Fang, S., Chen, J., Lin, Y., Chen, Y., Liang, R., Jiang, K., Parker, R. J., Boesch, H., Steinbacher, M., Sheng, J.-X., Lu, X., Song, S., and Peng, S.: Observed changes in China's methane emissions linked to policy drivers, Proceedings of the National Academy of Sciences, 119, e2202742119, https://doi.org/10.1073/pnas.2202742119, 2022.
Zhang, Z., Feng, S., Chen, Y., Liu, Q., Ju, W., Xiao, W., Huang, C., Wang, Y., Wang, H., Jia, M., Wang, X., and Jiang, F.: Development of a regional carbon assimilation system and its application for estimating fossil fuel carbon emissions in the Yangtze River Delta, China, Science of The Total Environment, 957, 177720, https://doi.org/10.1016/j.scitotenv.2024.177720, 2024.
Zhao, M., Tian, X., Wang, Y., Wang, X., Ciais, P., Jin, Z., Zhang, H., Wang, T., Ding, J., and Piao, S.: Slowdown in China's methane emission growth, National Science Review, 11, https://doi.org/10.1093/nsr/nwae223, 2024.
Short summary
Using satellite data and advanced modeling, this study inverted daily high-resolution anthropogenic CH4 emissions across China and Shanxi Province. We found that China's 2022 CH4 emissions were 45.1 TgCH4·yr⁻¹, significantly lower than previous estimates, especially in coal mining and waste sectors. The inversion substantially reduced emission uncertainties and improved CH4 concentration simulations. These results suggest China’s climate mitigation burden may have been overestimated.
Using satellite data and advanced modeling, this study inverted daily high-resolution...
Special issue
Altmetrics
Final-revised paper
Preprint