Articles | Volume 22, issue 16
https://doi.org/10.5194/acp-22-10769-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-10769-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Aug 2022
Research article |
| 25 Aug 2022
| 25 Aug 2022
Global and regional carbon budget for 2015–2020 inferred from OCO-2 based on an ensemble Kalman filter coupled with GEOS-Chem
Yawen Kong
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
China
Institute of Environment and Ecology, Tsinghua Shenzhen International
Graduate School, Tsinghua University, Shenzhen 518055, China
State Environmental Protection Key Laboratory of Sources and Control
of Air Pollution Complex, Beijing 100084, China
Qiang Zhang
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
China
Kebin He
State Environmental Protection Key Laboratory of Sources and Control
of Air Pollution Complex, Beijing 100084, China
State Key Joint Laboratory of Environment Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing 100084, China
Related authors
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Kjetil Aas, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Nicolas Bellouin, Alice Benoit-Cattin, Carla F. Berghoff, Raffaele Bernardello, Laurent Bopp, Ida B. M. Brasika, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Nathan O. Collier, Thomas H. Colligan, Margot Cronin, Laique Djeutchouang, Xinyu Dou, Matt P. Enright, Kazutaka Enyo, Michael Erb, Wiley Evans, Richard A. Feely, Liang Feng, Daniel J. Ford, Adrianna Foster, Filippa Fransner, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Jefferson Goncalves De Souza, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Bertrand Guenet, Özgür Gürses, Kirsty Harrington, Ian Harris, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Akihiko Ito, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Steve D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Yawen Kong, Jan Ivar Korsbakken, Charles Koven, Taro Kunimitsu, Xin Lan, Junjie Liu, Zhiqiang Liu, Zhu Liu, Claire Lo Monaco, Lei Ma, Gregg Marland, Patrick C. McGuire, Galen A. McKinley, Joe Melton, Natalie Monacci, Erwan Monier, Eric J. Morgan, David R. Munro, Jens D. Müller, Shin-Ichiro Nakaoka, Lorna R. Nayagam, Yosuke Niwa, Tobias Nutzel, Are Olsen, Abdirahman M. Omar, Naiqing Pan, Sudhanshu Pandey, Denis Pierrot, Zhangcai Qin, Pierre A. G. Regnier, Gregor Rehder, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, Ingunn Skjelvan, T. Luke Smallman, Victoria Spada, Mohanan G. Sreeush, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Didier Swingedouw, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Xiangjun Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Erik van Ooijen, Guido van der Werf, Sebastiaan J. van de Velde, Anthony Walker, Rik Wanninkhof, Xiaojuan Yang, Wenping Yuan, Xu Yue, and Jiye Zeng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-659, https://doi.org/10.5194/essd-2025-659, 2025
Preprint under review for ESSD
Short summary
Short summary
The Global Carbon Budget 2025 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 (1750–2025). 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.
Yawen Kong, Bo Zheng, and Yuxi Liu
Atmos. Chem. Phys., 25, 5959–5976, https://doi.org/10.5194/acp-25-5959-2025, https://doi.org/10.5194/acp-25-5959-2025, 2025
Short summary
Short summary
Current high-resolution satellite remote sensing technologies provide a unique opportunity to derive timely high-resolution emission data. We developed an emission inversion system to assimilate satellite NO2 data to obtain daily kilometer-scale NOx emission inventories. Our results enhance inventory accuracy, allowing us to capture the effects of pollution control policies on daily emissions (e.g., during COVID-19 lockdowns) and improve fine-scale air quality modeling.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
Short summary
Short summary
The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Kjetil Aas, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Nicolas Bellouin, Alice Benoit-Cattin, Carla F. Berghoff, Raffaele Bernardello, Laurent Bopp, Ida B. M. Brasika, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Nathan O. Collier, Thomas H. Colligan, Margot Cronin, Laique Djeutchouang, Xinyu Dou, Matt P. Enright, Kazutaka Enyo, Michael Erb, Wiley Evans, Richard A. Feely, Liang Feng, Daniel J. Ford, Adrianna Foster, Filippa Fransner, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Jefferson Goncalves De Souza, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Bertrand Guenet, Özgür Gürses, Kirsty Harrington, Ian Harris, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Akihiko Ito, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Steve D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Yawen Kong, Jan Ivar Korsbakken, Charles Koven, Taro Kunimitsu, Xin Lan, Junjie Liu, Zhiqiang Liu, Zhu Liu, Claire Lo Monaco, Lei Ma, Gregg Marland, Patrick C. McGuire, Galen A. McKinley, Joe Melton, Natalie Monacci, Erwan Monier, Eric J. Morgan, David R. Munro, Jens D. Müller, Shin-Ichiro Nakaoka, Lorna R. Nayagam, Yosuke Niwa, Tobias Nutzel, Are Olsen, Abdirahman M. Omar, Naiqing Pan, Sudhanshu Pandey, Denis Pierrot, Zhangcai Qin, Pierre A. G. Regnier, Gregor Rehder, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, Ingunn Skjelvan, T. Luke Smallman, Victoria Spada, Mohanan G. Sreeush, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Didier Swingedouw, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Xiangjun Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Erik van Ooijen, Guido van der Werf, Sebastiaan J. van de Velde, Anthony Walker, Rik Wanninkhof, Xiaojuan Yang, Wenping Yuan, Xu Yue, and Jiye Zeng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-659, https://doi.org/10.5194/essd-2025-659, 2025
Preprint under review for ESSD
Short summary
Short summary
The Global Carbon Budget 2025 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 (1750–2025). 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.
Zhenyu Luo, Li Peng, Zhaofeng Lv, Junchao Zhao, Tingkun He, Wen Yi, Yongyue Wang, Kebin He, and Huan Liu
Atmos. Chem. Phys., 25, 13635–13649, https://doi.org/10.5194/acp-25-13635-2025, https://doi.org/10.5194/acp-25-13635-2025, 2025
Short summary
Short summary
This study explores how shipping emissions affect ozone pollution in China. By combining atmospheric simulation and machine learning, we show that shipping emissions increase ozone levels by an average of 3.5 ppb nationwide, with large differences depending on location and season. Our findings highlight that controlling shipping emissions together with land-based sources is critical for improving air quality.
Ruochong Xu, Hanchen Ma, Jingxian Li, Dan Tong, Liu Yan, Lanyuan Wang, Xinying Qin, Qingyang Xiao, Xizhe Yan, Hanwen Hu, Yujia Fu, Nana Wu, Huaxuan Wang, Yuexuanzi Wang, Xiaodong Liu, Guannan Geng, Kebin He, and Qiang Zhang
Atmos. Chem. Phys., 25, 12675–12700, https://doi.org/10.5194/acp-25-12675-2025, https://doi.org/10.5194/acp-25-12675-2025, 2025
Short summary
Short summary
In this study, we developed a new global emission inventory for non-methane volatile organic compounds (NMVOC) for the period of 1970–2020, with a focus on improving the representation of NMVOC-emission-related technologies. Our analysis revealed that activity growth, technology advancements, and policy-driven emission controls were key driving forces of NMVOC emission changes, but their roles were different across sectors and regions.
Pramod Kumar, Grégoire Broquet, Didier Hauglustaine, Maureen Beaudor, Lieven Clarisse, Martin Van Damme, Pierre Coheur, Anne Cozic, Bo Zheng, Beatriz Revilla Romero, Antony Delavois, and Philippe Ciais
Atmos. Chem. Phys., 25, 12379–12407, https://doi.org/10.5194/acp-25-12379-2025, https://doi.org/10.5194/acp-25-12379-2025, 2025
Short summary
Short summary
Global maps of the NH3 emissions over 2019–2022 are derived using IASI NH3 spaceborne observations, the LMDZ-INCA chemistry transport model at 1.27°×2.5° resolution, and the mass-balance approach. The average global NH3 emissions over the period are ~97 Tg NH3 yr-1, which is significantly higher than three reference inventories. The analysis provides confidence in the seasonal variability and regional budgets and provides new insights into NH3 emissions at global and regional scales.
Jie Fang, Yunjiang Zhang, Didier Hauglustaine, Bo Zheng, Ming Wang, Jingyi Li, Yong Sun, Haiwei Li, Junfeng Wang, Yun Wu, Mindong Chen, and Xinlei Ge
EGUsphere, https://doi.org/10.5194/egusphere-2025-4014, https://doi.org/10.5194/egusphere-2025-4014, 2025
Short summary
Short summary
Surface ozone pollution is a pressing global challenge driven by human activities and a warming climate. Using nationwide observations (2013–2023) across China together with satellite data, we developed a new machine learning approach to separate the impacts of emission controls and weather changes. Our results show that while emission reductions improved ozone in some regions, climate change is increasingly offsetting these gains, underscoring the need for joint air quality and climate actions.
Hui Li, Philippe Ciais, Pramod Kumar, Didier A. Hauglustaine, Frédéric Chevallier, Grégoire Broquet, Dylan B. Millet, Kelley C. Wells, Jinghui Lian, and Bo Zheng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-424, https://doi.org/10.5194/essd-2025-424, 2025
Preprint under review for ESSD
Short summary
Short summary
We present the first global, multi-year maps of monthly isoprene emissions (2013–2020) derived from satellite isoprene observations, averaging 456 TgC yr-1. The dataset reveals two emission peaks linked to 2015–2016 El Niño and 2019–2020 extreme heat events, driven mainly by tropical regions such as the Amazon. It highlights the region-specific sensitivity of biogenic isoprene emissions to temperature anomalies, providing new insights into their roles in air quality and climate feedbacks.
Rimal Abeed, Audrey Fortems-Cheiney, Grégoire Broquet, Robin Plauchu, Isabelle Pison, Antoine Berchet, Elise Potier, Bo Zheng, Gaëlle Dufour, Adriana Coman, Dilek Savas, Guillaume Siour, Henk Eskes, Beatriz Revilla-Romero, Antony Delavois, and Philippe Ciais
EGUsphere, https://doi.org/10.5194/egusphere-2025-3329, https://doi.org/10.5194/egusphere-2025-3329, 2025
Short summary
Short summary
We investigate changes in air pollution from nitrogen oxides NOx (=NO+NO2) across Eastern China from 2019 to 2021, using a satellite-based modelling approach. Our results show a drop in pollution in 2020 in most provinces, and along the China-Mongolia-Russia Economic Corridor. The analysis also captures emission variations during the Lunar New Year. By estimating emissions at the provincial level, the study provides insights into how major events and policy measures influence local air quality.
Yawen Kong, Bo Zheng, and Yuxi Liu
Atmos. Chem. Phys., 25, 5959–5976, https://doi.org/10.5194/acp-25-5959-2025, https://doi.org/10.5194/acp-25-5959-2025, 2025
Short summary
Short summary
Current high-resolution satellite remote sensing technologies provide a unique opportunity to derive timely high-resolution emission data. We developed an emission inversion system to assimilate satellite NO2 data to obtain daily kilometer-scale NOx emission inventories. Our results enhance inventory accuracy, allowing us to capture the effects of pollution control policies on daily emissions (e.g., during COVID-19 lockdowns) and improve fine-scale air quality modeling.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter A. Raymond, Pierre Regnier, Josep G. Canadell, Robert B. Jackson, Prabir K. Patra, Philippe Bousquet, Philippe Ciais, Edward J. Dlugokencky, Xin Lan, George H. Allen, David Bastviken, David J. Beerling, Dmitry A. Belikov, Donald R. Blake, Simona Castaldi, Monica Crippa, Bridget R. Deemer, Fraser Dennison, Giuseppe Etiope, Nicola Gedney, Lena Höglund-Isaksson, Meredith A. Holgerson, Peter O. Hopcroft, Gustaf Hugelius, Akihiko Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul B. Krummel, Ronny Lauerwald, Tingting Li, Xiangyu Liu, Kyle C. McDonald, Joe R. Melton, Jens Mühle, Jurek Müller, Fabiola Murguia-Flores, Yosuke Niwa, Sergio Noce, Shufen Pan, Robert J. Parker, Changhui Peng, Michel Ramonet, William J. Riley, Gerard Rocher-Ros, Judith A. Rosentreter, Motoki Sasakawa, Arjo Segers, Steven J. Smith, Emily H. Stanley, Joël Thanwerdas, Hanqin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido R. van der Werf, Douglas E. J. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, https://doi.org/10.5194/essd-17-1873-2025, 2025
Short summary
Short summary
Methane (CH4) is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). A consortium of multi-disciplinary scientists synthesise and update the budget of the sources and sinks of CH4. This edition benefits from important progress in estimating emissions from lakes and ponds, reservoirs, and streams and rivers. For the 2010s decade, global CH4 emissions are estimated at 575 Tg CH4 yr-1, including ~65 % from anthropogenic sources.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
Short summary
Short summary
The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Xiaolin Duan, Guangjie Zheng, Chuchu Chen, Qiang Zhang, and Kebin He
Atmos. Chem. Phys., 25, 3919–3928, https://doi.org/10.5194/acp-25-3919-2025, https://doi.org/10.5194/acp-25-3919-2025, 2025
Short summary
Short summary
Aerosol acidity is an important parameter in atmospheric chemistry, while its driving factors, especially chemical profiles versus meteorological conditions, are not yet fully understood. Here, we established a hierarchical quantitative analysis framework to understand the driving factors of aerosol acidity on different timescales. Its application in Changzhou, China, revealed distinct driving factors and corresponding mechanisms of aerosol acidity from annual trends to random residuals.
Zhu Deng, Philippe Ciais, Liting Hu, Adrien Martinez, Marielle Saunois, Rona L. Thompson, Kushal Tibrewal, Wouter Peters, Brendan Byrne, Giacomo Grassi, Paul I. Palmer, Ingrid T. Luijkx, Zhu Liu, Junjie Liu, Xuekun Fang, Tengjiao Wang, Hanqin Tian, Katsumasa Tanaka, Ana Bastos, Stephen Sitch, Benjamin Poulter, Clément Albergel, Aki Tsuruta, Shamil Maksyutov, Rajesh Janardanan, Yosuke Niwa, Bo Zheng, Joël Thanwerdas, Dmitry Belikov, Arjo Segers, and Frédéric Chevallier
Earth Syst. Sci. Data, 17, 1121–1152, https://doi.org/10.5194/essd-17-1121-2025, https://doi.org/10.5194/essd-17-1121-2025, 2025
Short summary
Short summary
This study reconciles national greenhouse gas (GHG) inventories with updated atmospheric inversion results to evaluate discrepancies for three principal GHG fluxes at the national level. Compared to our previous study, new satellite-based CO2 inversions were included and an updated mask of managed lands was used, improving agreement for Brazil and Canada. The proposed methodology can be regularly applied as a check to assess the gap between top-down inversions and bottom-up inventories.
Mingrui Ma, Jiachen Cao, Dan Tong, Bo Zheng, and Yu Zhao
Atmos. Chem. Phys., 25, 2147–2166, https://doi.org/10.5194/acp-25-2147-2025, https://doi.org/10.5194/acp-25-2147-2025, 2025
Short summary
Short summary
We combined two global climate change pathways and three national emission control scenarios to analyze the future evolution of reactive nitrogen (Nr) deposition till the 2060s in China with air quality modeling. We show China’s clean air and carbon neutrality policies would overcome the adverse effects of climate change and efficiently reduce Nr deposition. The outflow of Nr fluxes from mainland China to the west Pacific would also be clearly reduced from continuous stringent emission controls.
Hui Li, Jiaxin Qiu, and Bo Zheng
Atmos. Chem. Phys., 25, 1949–1963, https://doi.org/10.5194/acp-25-1949-2025, https://doi.org/10.5194/acp-25-1949-2025, 2025
Short summary
Short summary
We conduct a sensitivity analysis with 31 tests on various factors including prior emissions, model resolution, satellite constraint, and other system configurations to assess the vulnerability of emission estimates across temporal, sectoral, and regional dimensions. This reveals the robustness of emissions estimated by this air-pollution-satellite-based CO2 emission inversion system, with relative change between tests and base inversion below 4.0 % for national annual NOx and CO2 emissions.
Wen Yi, Xiaotong Wang, Tingkun He, Huan Liu, Zhenyu Luo, Zhaofeng Lv, and Kebin He
Earth Syst. Sci. Data, 17, 277–292, https://doi.org/10.5194/essd-17-277-2025, https://doi.org/10.5194/essd-17-277-2025, 2025
Short summary
Short summary
This study presents a detailed global dataset on ship emissions, covering the years 2013 and 2016–2021, using advanced modeling techniques. The dataset includes emissions data for four types of greenhouse gases and five types of air pollutants. The data, available for research, offer valuable insights into ship emission spatiotemporal patterns by vessel type and age, providing a solid data foundation for fine-scale scientific research and shipping emission mitigation.
Zhaojin An, Rujing Yin, Xinyan Zhao, Xiaoxiao Li, Yuyang Li, Yi Yuan, Junchen Guo, Yiqi Zhao, Xue Li, Dandan Li, Yaowei Li, Dongbin Wang, Chao Yan, Kebin He, Douglas R. Worsnop, Frank N. Keutsch, and Jingkun Jiang
Atmos. Chem. Phys., 24, 13793–13810, https://doi.org/10.5194/acp-24-13793-2024, https://doi.org/10.5194/acp-24-13793-2024, 2024
Short summary
Short summary
Online Vocus-PTR measurements show the compositions and seasonal variations in organic vapors in urban Beijing. With enhanced sensitivity and mass resolution, various species at a level of sub-parts per trillion (ppt) and organics with multiple oxygens (≥ 3) were observed. The fast photooxidation process in summer leads to an increase in both concentration and proportion of organics with multiple oxygens, while, in other seasons, the variations in them could be influenced by mixed sources.
Liu Yan, Qiang Zhang, Bo Zheng, and Kebin He
Earth Syst. Sci. Data, 16, 4497–4509, https://doi.org/10.5194/essd-16-4497-2024, https://doi.org/10.5194/essd-16-4497-2024, 2024
Short summary
Short summary
A new database of fuel-, vehicle-type-, and age-specific CO2 emissions from global on-road vehicles from 1970 to 2020 is developed with the fleet turnover model built in this study. Based on this database, the evolution of the global vehicle stock over 50 years is analyzed, the dominant emission contributors by vehicle and fuel type are identified, and the age distribution of on-road CO2 emissions is characterized further.
Nana Wu, Guannan Geng, Ruochong Xu, Shigan Liu, Xiaodong Liu, Qinren Shi, Ying Zhou, Yu Zhao, Huan Liu, Yu Song, Junyu Zheng, Qiang Zhang, and Kebin He
Earth Syst. Sci. Data, 16, 2893–2915, https://doi.org/10.5194/essd-16-2893-2024, https://doi.org/10.5194/essd-16-2893-2024, 2024
Short summary
Short summary
The commonly used method for developing large-scale air pollutant emission datasets for China faces challenges due to limited availability of detailed parameter information. In this study, we develop an efficient integrated framework to gather such information by harmonizing seven heterogeneous inventories from five research institutions. Emission characterizations are analyzed and validated, demonstrating that the dataset provides more accurate emission magnitudes and spatiotemporal patterns.
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.
Peng Wang, Ruhan Zhang, Shida Sun, Meng Gao, Bo Zheng, Dan Zhang, Yanli Zhang, Gregory R. Carmichael, and Hongliang Zhang
Atmos. Chem. Phys., 23, 2983–2996, https://doi.org/10.5194/acp-23-2983-2023, https://doi.org/10.5194/acp-23-2983-2023, 2023
Short summary
Short summary
In China, the number of vehicles has jumped significantly in the last decade. This caused severe traffic congestion and aggravated air pollution. In this study, we developed a new temporal allocation approach to quantify the impacts of traffic congestion. We found that traffic congestion worsens air quality and the health burden across China, especially in the urban clusters. More effective and comprehensive vehicle emission control policies should be implemented to improve air quality in China.
Yuanhong Zhao, Marielle Saunois, Philippe Bousquet, Xin Lin, Michaela I. Hegglin, Josep G. Canadell, Robert B. Jackson, and Bo Zheng
Atmos. Chem. Phys., 23, 789–807, https://doi.org/10.5194/acp-23-789-2023, https://doi.org/10.5194/acp-23-789-2023, 2023
Short summary
Short summary
The large uncertainties in OH simulated by atmospheric chemistry models hinder accurate estimates of CH4 chemical loss through the bottom-up method. This study presents a new approach based on OH precursor observations and a chemical box model to improve the tropospheric OH distributions simulated by atmospheric chemistry models. Through this approach, both the global OH burden and the corresponding methane chemical loss reach consistency with the top-down method based on MCF inversions.
Zhaofeng Lv, Zhenyu Luo, Fanyuan Deng, Xiaotong Wang, Junchao Zhao, Lucheng Xu, Tingkun He, Yingzhi Zhang, Huan Liu, and Kebin He
Atmos. Chem. Phys., 22, 15685–15702, https://doi.org/10.5194/acp-22-15685-2022, https://doi.org/10.5194/acp-22-15685-2022, 2022
Short summary
Short summary
This study developed a hybrid model, CMAQ-RLINE_URBAN, to predict the urban NO2 concentrations at a high spatial resolution. To estimate the influence of various street canyons on the dispersion of air pollutants, a new parameterization scheme was established based on computational fluid dynamics and machine learning methods. This work created a new method to identify the characteristics of vehicle-related air pollution at both city and street scales simultaneously and accurately.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, 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, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. 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.
Philippe Ciais, Ana Bastos, Frédéric Chevallier, Ronny Lauerwald, Ben Poulter, Josep G. Canadell, Gustaf Hugelius, Robert B. Jackson, Atul Jain, Matthew Jones, Masayuki Kondo, Ingrid T. Luijkx, Prabir K. Patra, Wouter Peters, Julia Pongratz, Ana Maria Roxana Petrescu, Shilong Piao, Chunjing Qiu, Celso Von Randow, Pierre Regnier, Marielle Saunois, Robert Scholes, Anatoly Shvidenko, Hanqin Tian, Hui Yang, Xuhui Wang, and Bo Zheng
Geosci. Model Dev., 15, 1289–1316, https://doi.org/10.5194/gmd-15-1289-2022, https://doi.org/10.5194/gmd-15-1289-2022, 2022
Short summary
Short summary
The second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP) will provide updated quantification and process understanding of CO2, CH4, and N2O emissions and sinks for ten regions of the globe. In this paper, we give definitions, review different methods, and make recommendations for estimating different components of the total land–atmosphere carbon exchange for each region in a consistent and complete approach.
Youwen Sun, Hao Yin, Xiao Lu, Justus Notholt, Mathias Palm, Cheng Liu, Yuan Tian, and Bo Zheng
Atmos. Chem. Phys., 21, 18589–18608, https://doi.org/10.5194/acp-21-18589-2021, https://doi.org/10.5194/acp-21-18589-2021, 2021
Short summary
Short summary
This study uses high-resolution nested-grid GEOS-Chem simulation, the eXtreme Gradient Boosting (XGBoost) machine learning method, and the exposure–response relationship to determine the drivers and evaluate the health risks of the unexpected surface O3 enhancements over the Sichuan Basin in 2020. These unexpected O3 enhancements were induced by meteorological anomalies and caused dramatically high health risks.
Ruili Wu, Christopher W. Tessum, Yang Zhang, Chaopeng Hong, Yixuan Zheng, Xinyin Qin, Shigan Liu, and Qiang Zhang
Geosci. Model Dev., 14, 7621–7638, https://doi.org/10.5194/gmd-14-7621-2021, https://doi.org/10.5194/gmd-14-7621-2021, 2021
Short summary
Short summary
Reduced-complexity air quality models are less computationally intensive and easier to use. We developed a reduced-complexity air quality Intervention Model for Air Pollution over China (InMAP-China) to rapidly predict the air quality and estimate the health impacts of emission sources in China. We believe that this work will be of great interest to a broad audience, including environmentalists in China and scientists in relevant fields at both national and local institutes.
Yuqiang Zhang, Drew Shindell, Karl Seltzer, Lu Shen, Jean-Francois Lamarque, Qiang Zhang, Bo Zheng, Jia Xing, Zhe Jiang, and Lei Zhang
Atmos. Chem. Phys., 21, 16051–16065, https://doi.org/10.5194/acp-21-16051-2021, https://doi.org/10.5194/acp-21-16051-2021, 2021
Short summary
Short summary
In this study, we use a global chemical transport model to simulate the effects on global air quality and human health due to emission changes in China from 2010 to 2017. By performing sensitivity analysis, we found that the air pollution control policies not only decrease the air pollutant concentration but also bring significant co-benefits in air quality to downwind regions. The benefits for the improved air pollution are dominated by PM2.5.
Gaëlle Dufour, Didier Hauglustaine, Yunjiang Zhang, Maxim Eremenko, Yann Cohen, Audrey Gaudel, Guillaume Siour, Mathieu Lachatre, Axel Bense, Bertrand Bessagnet, Juan Cuesta, Jerry Ziemke, Valérie Thouret, and Bo Zheng
Atmos. Chem. Phys., 21, 16001–16025, https://doi.org/10.5194/acp-21-16001-2021, https://doi.org/10.5194/acp-21-16001-2021, 2021
Short summary
Short summary
The IASI observations and the LMDZ-OR-INCA model simulations show negative ozone trends in the Central East China region in the lower free (3–6 km column) and the upper free (6–9 km column) troposphere. Sensitivity studies from the model show that the Chinese anthropogenic emissions contribute to more than 50 % in the trend. The reduction in NOx emissions that has occurred since 2013 in China seems to lead to a decrease in ozone in the free troposphere, contrary to the increase at the surface.
Xiaotong Wang, Wen Yi, Zhaofeng Lv, Fanyuan Deng, Songxin Zheng, Hailian Xu, Junchao Zhao, Huan Liu, and Kebin He
Atmos. Chem. Phys., 21, 13835–13853, https://doi.org/10.5194/acp-21-13835-2021, https://doi.org/10.5194/acp-21-13835-2021, 2021
Short summary
Short summary
This study updates our previous Ship Emission Inventory Model to version 2.0 (SEIM v2.0) and develops high-spatiotemporal ship emission inventories of China’s inland rivers and a 200 nautical mile coastal zone in 2016–2019. The 4-year consecutive daily ship emissions and emission structure changes are analyzed from the national to port levels. The results of this study can provide high-quality datasets for air quality modeling and observation experiment verifications.
Yi Yin, Frederic Chevallier, Philippe Ciais, Philippe Bousquet, Marielle Saunois, Bo Zheng, John Worden, A. Anthony Bloom, Robert J. Parker, Daniel J. Jacob, Edward J. Dlugokencky, and Christian Frankenberg
Atmos. Chem. Phys., 21, 12631–12647, https://doi.org/10.5194/acp-21-12631-2021, https://doi.org/10.5194/acp-21-12631-2021, 2021
Short summary
Short summary
The growth of methane, the second-most important anthropogenic greenhouse gas after carbon dioxide, has been accelerating in recent years. Using an ensemble of multi-tracer atmospheric inversions constrained by surface or satellite observations, we show that global methane emissions increased by nearly 1 % per year from 2010–2017, with leading contributions from the tropics and East Asia.
Youwen Sun, Hao Yin, Cheng Liu, Emmanuel Mahieu, Justus Notholt, Yao Té, Xiao Lu, Mathias Palm, Wei Wang, Changgong Shan, Qihou Hu, Min Qin, Yuan Tian, and Bo Zheng
Atmos. Chem. Phys., 21, 11759–11779, https://doi.org/10.5194/acp-21-11759-2021, https://doi.org/10.5194/acp-21-11759-2021, 2021
Short summary
Short summary
The variability, sources, and transport of ethane (C2H6) over eastern China from 2015 to 2020 were studied using ground-based Fourier transform infrared (FTIR) spectroscopy and GEOS-Chem simulations. C2H6 variability is driven by both meteorological and emission factors. The reduction in C2H6 in recent years over eastern China points to air quality improvement in China.
Jinghui Lian, François-Marie Bréon, Grégoire Broquet, Thomas Lauvaux, Bo Zheng, Michel Ramonet, Irène Xueref-Remy, Simone Kotthaus, Martial Haeffelin, and Philippe Ciais
Atmos. Chem. Phys., 21, 10707–10726, https://doi.org/10.5194/acp-21-10707-2021, https://doi.org/10.5194/acp-21-10707-2021, 2021
Short summary
Short summary
Currently there is growing interest in monitoring city-scale CO2 emissions based on atmospheric CO2 measurements, atmospheric transport modeling, and inversion technique. We analyze the various sources of uncertainty that impact the atmospheric CO2 modeling and that may compromise the potential of this method for the monitoring of CO2 emission over Paris. Results suggest selection criteria for the assimilation of CO2 measurements into the inversion system that aims at retrieving city emissions.
Qingyang Xiao, Yixuan Zheng, Guannan Geng, Cuihong Chen, Xiaomeng Huang, Huizheng Che, Xiaoye Zhang, Kebin He, and Qiang Zhang
Atmos. Chem. Phys., 21, 9475–9496, https://doi.org/10.5194/acp-21-9475-2021, https://doi.org/10.5194/acp-21-9475-2021, 2021
Short summary
Short summary
We used both statistical methods and a chemical transport model to assess the contribution of meteorology and emissions to PM2.5 during 2000–2018. Both methods revealed that emissions dominated the long-term PM2.5 trend with notable meteorological effects ranged up to 37.9 % of regional annual average PM2.5. The meteorological contribution became more beneficial to PM2.5 control in southern China but more unfavorable in northern China during the studied period.
Bo Zheng, Qiang Zhang, Guannan Geng, Cuihong Chen, Qinren Shi, Mengshi Cui, Yu Lei, and Kebin He
Earth Syst. Sci. Data, 13, 2895–2907, https://doi.org/10.5194/essd-13-2895-2021, https://doi.org/10.5194/essd-13-2895-2021, 2021
Short summary
Short summary
Here we report the monthly anthropogenic pollutant emissions in China during the COVID-19 pandemic by using a bottom-up approach based on near-real-time data. The COVID lockdowns were estimated to have reduced China's emissions substantially between January and March in 2020, with the largest reduction in February. With the spread of coronavirus controlled, China's anthropogenic emissions rebounded in April and since then returned to levels comparable to those of 2019 through December 2020.
Youwen Sun, Hao Yin, Yuan Cheng, Qianggong Zhang, Bo Zheng, Justus Notholt, Xiao Lu, Cheng Liu, Yuan Tian, and Jianguo Liu
Atmos. Chem. Phys., 21, 9201–9222, https://doi.org/10.5194/acp-21-9201-2021, https://doi.org/10.5194/acp-21-9201-2021, 2021
Short summary
Short summary
We quantified the variability, source, and transport of urban CO over the Himalayas and Tibetan Plateau (HTP) by using measurement, model simulation, and the analysis of meteorological fields. Urban CO over the HTP is dominated by anthropogenic and biomass burning emissions from local, South Asia and East Asia, and oxidation sources. The decreasing trends in surface CO since 2015 in most cities over the HTP are attributed to the reduction in local and transported CO emissions in recent years.
Jingsha Xu, Di Liu, Xuefang Wu, Tuan V. Vu, Yanli Zhang, Pingqing Fu, Yele Sun, Weiqi Xu, Bo Zheng, Roy M. Harrison, and Zongbo Shi
Atmos. Chem. Phys., 21, 7321–7341, https://doi.org/10.5194/acp-21-7321-2021, https://doi.org/10.5194/acp-21-7321-2021, 2021
Short summary
Short summary
Source apportionment of fine aerosols in an urban site of Beijing used a chemical mass balance (CMB) model. Seven primary sources (industrial/residential coal burning, biomass burning, gasoline/diesel vehicles, cooking and vegetative detritus) explained an average of 75.7 % and 56.1 % of fine OC in winter and summer, respectively. CMB was found to resolve more primary OA sources than AMS-PMF, but the latter apportioned more secondary OA sources.
Youwen Sun, Hao Yin, Cheng Liu, Lin Zhang, Yuan Cheng, Mathias Palm, Justus Notholt, Xiao Lu, Corinne Vigouroux, Bo Zheng, Wei Wang, Nicholas Jones, Changong Shan, Min Qin, Yuan Tian, Qihou Hu, Fanhao Meng, and Jianguo Liu
Atmos. Chem. Phys., 21, 6365–6387, https://doi.org/10.5194/acp-21-6365-2021, https://doi.org/10.5194/acp-21-6365-2021, 2021
Short summary
Short summary
This study mapped the drivers of HCHO variability from 2015 to 2019 over eastern China. Hydroxyl (OH) radical production rates from HCHO photolysis were evaluated. The relative contributions of emitted and photochemical sources to the observed HCHO abundance were analyzed. Contributions of various emission sources and geographical regions to the observed HCHO summertime enhancements were determined.
Diego Santaren, Grégoire Broquet, François-Marie Bréon, Frédéric Chevallier, Denis Siméoni, Bo Zheng, and Philippe Ciais
Atmos. Meas. Tech., 14, 403–433, https://doi.org/10.5194/amt-14-403-2021, https://doi.org/10.5194/amt-14-403-2021, 2021
Short summary
Short summary
Atmospheric transport inversions with synthetic data are used to assess the potential of new satellite observations of atmospheric CO2 to monitor anthropogenic emissions from regions, cities and large industrial plants. The analysis, applied to a large ensemble of sources in western Europe, shows a strong dependence of the results on different characteristics of the spaceborne instrument, on the source emission budgets and spreads, and on the wind conditions.
Shaojie Song, Tao Ma, Yuzhong Zhang, Lu Shen, Pengfei Liu, Ke Li, Shixian Zhai, Haotian Zheng, Meng Gao, Jonathan M. Moch, Fengkui Duan, Kebin He, and Michael B. McElroy
Atmos. Chem. Phys., 21, 457–481, https://doi.org/10.5194/acp-21-457-2021, https://doi.org/10.5194/acp-21-457-2021, 2021
Short summary
Short summary
We simulate the atmospheric chemical processes of an important sulfur-containing organic aerosol species, which is produced by the reaction between sulfur dioxide and formaldehyde. We can predict its distribution on a global scale. We find it is particularly rich in East Asia. This aerosol species is more abundant in the colder season partly because of weaker sunlight.
Erin E. McDuffie, Steven J. Smith, Patrick O'Rourke, Kushal Tibrewal, Chandra Venkataraman, Eloise A. Marais, Bo Zheng, Monica Crippa, Michael Brauer, and Randall V. Martin
Earth Syst. Sci. Data, 12, 3413–3442, https://doi.org/10.5194/essd-12-3413-2020, https://doi.org/10.5194/essd-12-3413-2020, 2020
Short summary
Short summary
Global emission inventories are vital to understanding the impacts of air pollution on the environment, human health, and society. We update the open-source Community Emissions Data System (CEDS) to provide global gridded emissions of seven key air pollutants from 1970–2017 for 11 source sectors and multiple fuel types, including coal, solid biofuel, and liquid oil and natural gas. This dataset includes both monthly global gridded emissions and annual national totals.
Yilong Wang, Grégoire Broquet, François-Marie Bréon, Franck Lespinas, Michael Buchwitz, Maximilian Reuter, Yasjka Meijer, Armin Loescher, Greet Janssens-Maenhout, Bo Zheng, and Philippe Ciais
Geosci. Model Dev., 13, 5813–5831, https://doi.org/10.5194/gmd-13-5813-2020, https://doi.org/10.5194/gmd-13-5813-2020, 2020
Cited articles
Baker, D. F., Doney, S. C., and Schimel, D. S.: Variational data
assimilation for atmospheric CO2,
Tellus B, 58, 359–365, https://doi.org/10.1111/j.1600-0889.2006.00218.x, 2006.
Bastos, A., O'Sullivan, M., Ciais, P., Makowski, D., Sitch, S.,
Friedlingstein, P., Chevallier, F., Rödenbeck, C., Pongratz, J., Luijkx,
I. T., Patra, P. K., Peylin, P., Canadell, J. G., Lauerwald, R., Li, W.,
Smith, N. E., Peters, W., Goll, D. S., Jain, A. K., Kato, E., Lienert, S.,
Lombardozzi, D. L., Haverd, V., Nabel, J. E. M. S., Poulter, B., Tian, H.,
Walker, A. P., and Zaehle, S.: Sources of Uncertainty in Regional and Global
Terrestrial CO2 Exchange Estimates, Global Biogeochem. Cy., 34,
e2019GB006393, https://doi.org/10.1029/2019GB006393, 2020.
Basu, S., Baker, D. F., Chevallier, F., Patra, P. K., Liu, J., and Miller, J. B.: The impact of transport model differences on CO2 surface flux estimates from OCO-2 retrievals of column average CO2, Atmos. Chem. Phys., 18, 7189–7215, https://doi.org/10.5194/acp-18-7189-2018, 2018.
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore,
A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global
modeling of tropospheric chemistry with assimilated meteorology: Model
description and evaluation, J. Geophys. Res.-Atmos., 106, 23073–23095, https://doi.org/10.1029/2001JD000807, 2001.
Bruhwiler, L. M. P., Michalak, A. M., Peters, W., Baker, D. F., and Tans, P.: An improved Kalman Smoother for atmospheric inversions, Atmos. Chem. Phys., 5, 2691–2702, https://doi.org/10.5194/acp-5-2691-2005, 2005.
Canadell, J. G., Monteiro, P. M. S., Costa, M. H., Cotrim da Cunha, L., Cox, P. M.,
Eliseev, A. V., Henson, S., Ishii, M., Jaccard, S., Koven, C., Lohila, A., Patra, P. K., Piao, S., Rogelj, J., Syampungani, S., Zaehle, S., and Zickfeld, K.:
Global Carbon and other Biogeochemical Cycles and Feedbacks, 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, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 673–816, https://doi.org/10.1017/9781009157896.007, 2021.
Chatterjee, A. and Michalak, A. M.: Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO2 data assimilation, Atmos. Chem. Phys., 13, 11643–11660, https://doi.org/10.5194/acp-13-11643-2013, 2013.
Chevallier, F., Fisher, M., Peylin, P., Serrar, S., Bousquet, P., Bréon,
F. M., Chédin, A., and Ciais, P.: Inferring CO2 sources and sinks
from satellite observations: Method and application to TOVS data, J.
Geophys. Res.-Atmos., 110, D24309, https://doi.org/10.1029/2005JD006390, 2005.
Chevallier, F., Palmer, P. I., Feng, L., Boesch, H., O'Dell, C. W., and
Bousquet, P.: Toward robust and consistent regional CO2 flux estimates
from in situ and spaceborne measurements of atmospheric CO2, Geophys.
Res. Lett., 41, 1065–1070, https://doi.org/10.1002/2013GL058772, 2014.
Chevallier, F., Remaud, M., O'Dell, C. W., Baker, D., Peylin, P., and Cozic, A.: Objective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions, Atmos. Chem. Phys., 19, 14233–14251, https://doi.org/10.5194/acp-19-14233-2019, 2019.
Ciais, P., Rayner, P., Chevallier, F., Bousquet, P., Logan, M., Peylin, P.,
and Ramonet, M.: Atmospheric inversions for estimating CO2 fluxes:
methods and perspectives, Clim. Chang., 103, 69–92, https://doi.org/10.1007/s10584-010-9909-3, 2010.
Ciais, P., Tan, J., Wang, X., Roedenbeck, C., Chevallier, F., Piao, S. L.,
Moriarty, R., Broquet, G., Le Quéré, C., Canadell, J. G., Peng, S.,
Poulter, B., Liu, Z., and Tans, P.: Five decades of northern land carbon
uptake revealed by the interhemispheric CO2 gradient, Nature, 568,
221–225, https://doi.org/10.1038/s41586-019-1078-6, 2019.
Ciais, P., Bastos, A., Chevallier, F., Lauerwald, R., Poulter, B., Canadell, J. G., Hugelius, G., Jackson, R. B., Jain, A., Jones, M., Kondo, M., Luijkx, I. T., Patra, P. K., Peters, W., Pongratz, J., Petrescu, A. M. R., Piao, S., Qiu, C., Von Randow, C., Regnier, P., Saunois, M., Scholes, R., Shvidenko, A., Tian, H., Yang, H., Wang, X., and Zheng, B.: Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2), Geosci. Model Dev., 15, 1289–1316, https://doi.org/10.5194/gmd-15-1289-2022, 2022.
Cooperative Global Atmospheric Data Integration Project: Multi-laboratory compilation of atmospheric carbon dioxide data for the period 1957–2020; obspack_co2_1_GLOBALVIEWplus_v7.0_2021-08-18, NOAA
Earth System Research Laboratory, Global Monitoring Laboratory, https://doi.org/10.25925/20210801, 2021.
Crowell, S., Baker, D., Schuh, A., Basu, S., Jacobson, A. R., Chevallier, F., Liu, J., Deng, F., Feng, L., McKain, K., Chatterjee, A., Miller, J. B., Stephens, B. B., Eldering, A., Crisp, D., Schimel, D., Nassar, R., O'Dell, C. W., Oda, T., Sweeney, C., Palmer, P. I., and Jones, D. B. A.: The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network, Atmos. Chem. Phys., 19, 9797–9831, https://doi.org/10.5194/acp-19-9797-2019, 2019.
Deng, F., Jones, D. B. A., Henze, D. K., Bousserez, N., Bowman, K. W., Fisher, J. B., Nassar, R., O'Dell, C., Wunch, D., Wennberg, P. O., Kort, E. A., Wofsy, S. C., Blumenstock, T., Deutscher, N. M., Griffith, D. W. T., Hase, F., Heikkinen, P., Sherlock, V., Strong, K., Sussmann, R., and Warneke, T.: Inferring regional sources and sinks of atmospheric CO2 from GOSAT XCO2 data, Atmos. Chem. Phys., 14, 3703–3727, https://doi.org/10.5194/acp-14-3703-2014, 2014.
Eldering, A., Wennberg, P. O., Crisp, D., Schimel, D. S., Gunson, M. R.,
Chatterjee, A., Liu, J., Schwandner, F. M., Sun, Y., O'Dell, C. W.,
Frankenberg, C., Taylor, T., Fisher, B., Osterman, G. B., Wunch, D.,
Hakkarainen, J., Tamminen, J., and Weir, B.: The Orbiting Carbon
Observatory-2 early science investigations of regional carbon dioxide
fluxes, Science, 358, eaam5745, https://doi.org/10.1126/science.aam5745, 2017.
Feng, L., Palmer, P. I., Bösch, H., and Dance, S.: Estimating surface CO2 fluxes from space-borne CO2 dry air mole fraction observations using an ensemble Kalman Filter, Atmos. Chem. Phys., 9, 2619–2633, https://doi.org/10.5194/acp-9-2619-2009, 2009.
Gaubert, B., Stephens, B. B., Basu, S., Chevallier, F., Deng, F., Kort, E. A., Patra, P. K., Peters, W., Rödenbeck, C., Saeki, T., Schimel, D., Van der Laan-Luijkx, I., Wofsy, S., and Yin, Y.: Global atmospheric CO2 inverse models converging on neutral tropical land exchange, but disagreeing on fossil fuel and atmospheric growth rate, Biogeosciences, 16, 117–134, https://doi.org/10.5194/bg-16-117-2019, 2019.
Gurney, K. R., Law, R. M., Denning, A. S., Rayner, P. J., Baker, D.,
Bousquet, P., Bruhwiler, L., Chen, Y.-H., Ciais, P., Fan, S., Fung, I. Y.,
Gloor, M., Heimann, M., Higuchi, K., John, J., Maki, T., Maksyutov, S.,
Masarie, K., Peylin, P., Prather, M., Pak, B. C., Randerson, J., Sarmiento,
J., Taguchi, S., Takahashi, T., and Yuen, C.-W.: Towards robust regional
estimates of CO2 sources and sinks using atmospheric transport models,
Nature, 415, 626–630, https://doi.org/10.1038/415626a, 2002.
Gurney, K. R., Law, R. M., Denning, A. S., Rayner, P. J., Baker, D.,
Bousquet, P., Bruhwiler, L., Chen, Y.-H., Ciais, P., Fan, S., Fung, I. Y.,
Gloor, M., Heimann, M., Higuchi, K., John, J., Kowalczyk, E., Maki, T.,
Maksyutov, S., Peylin, P., Prather, M., Pak, B. C., Sarmiento, J., Taguchi,
S., Takahashi, T., and Yuen, C.-W.: TransCom 3 CO2 inversion
intercomparison: 1. Annual mean control results and sensitivity to transport
and prior flux information, Tellus B, 55,
555–579, https://doi.org/10.3402/tellusb.v55i2.16728, 2003.
Haynes, K. D., Baker, I. T., and Denning, A. S.: SiB4 Modeled Global
0.5-Degree Hourly Carbon Fluxes and Productivity, 2000–2018, ORNL DAAC, Oak
Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1847, 2021.
Houweling, S., Baker, D., Basu, S., Boesch, H., Butz, A., Chevallier, F.,
Deng, F., Dlugokencky, E. J., Feng, L., Ganshin, A., Hasekamp, O., Jones,
D., Maksyutov, S., Marshall, J., Oda, T., O'Dell, C. W., Oshchepkov, S.,
Palmer, P. I., Peylin, P., Poussi, Z., Reum, F., Takagi, H., Yoshida, Y.,
and Zhuravlev, R.: An intercomparison of inverse models for estimating
sources and sinks of CO2 using GOSAT measurements, J. Geophys. Res.-Atmos., 120, 5253–5266, https://doi.org/10.1002/2014JD022962, 2015.
Houtekamer, P. L. and Zhang, F.: Review of the Ensemble Kalman Filter for
Atmospheric Data Assimilation, Mon. Weather Rev., 144, 4489–4532, https://doi.org/10.1175/MWR-D-15-0440.1, 2016.
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation
for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica
D, 230, 112–126, https://doi.org/10.1016/j.physd.2006.11.008,
2007.
Jiang, F., Wang, H., Chen, J. M., Ju, W., Tian, X., Feng, S., Li, G., Chen, Z., Zhang, S., Lu, X., Liu, J., Wang, H., Wang, J., He, W., and Wu, M.: Regional CO2 fluxes from 2010 to 2015 inferred from GOSAT XCO2 retrievals using a new version of the Global Carbon Assimilation System, Atmos. Chem. Phys., 21, 1963–1985, https://doi.org/10.5194/acp-21-1963-2021, 2021.
Kang, J.-S., Kalnay, E., Liu, J., Fung, I., Miyoshi, T., and Ide, K.:
“Variable localization” in an ensemble Kalman filter: application to the
carbon cycle data assimilation, J. Geophys. Res., 116, D09110,
https://doi.org/10.1029/2010JD014673, 2011.
Liu, J., Bowman, K. W., and Lee, M.: Comparison between the Local Ensemble
Transform Kalman Filter (LETKF) and 4D-Var in atmospheric CO2 flux
inversion with the Goddard Earth Observing System-Chem model and the
observation impact diagnostics from the LETKF, J. Geophys. Res.-Atmos., 121,
13066–13087, https://doi.org/10.1002/2016JD025100, 2016.
Liu, J., Bowman, K. W., Schimel, D. S., Parazoo, N. C., Jiang, Z., Lee, M.,
Bloom, A. A., Wunch, D., Frankenberg, C., Sun, Y., O'Dell, C. W., Gurney, K.
R., Menemenlis, D., Gierach, M., Crisp, D., and Eldering, A.: Contrasting
carbon cycle responses of the tropical continents to the 2015–2016 El
Niño, Science, 358, eaam5690, https://doi.org/10.1126/science.aam5690, 2017.
Liu, Y., Kalnay, E., Zeng, N., Asrar, G., Chen, Z., and Jia, B.: Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1, Geosci. Model Dev., 12, 2899–2914, https://doi.org/10.5194/gmd-12-2899-2019, 2019.
Liu, Z., Ciais, P., Deng, Z., Lei, R., Davis, S. J., Feng, S., Zheng, B.,
Cui, D., Dou, X., Zhu, B., Guo, R., Ke, P., Sun, T., Lu, C., He, P., Wang,
Y., Yue, X., Wang, Y., Lei, Y., Zhou, H., Cai, Z., Wu, Y., Guo, R., Han, T.,
Xue, J., Boucher, O., Boucher, E., Chevallier, F., Tanaka, K., Wei, Y.,
Zhong, H., Kang, C., Zhang, N., Chen, B., Xi, F., Liu, M., Bréon, F.-M.,
Lu, Y., Zhang, Q., Guan, D., Gong, P., Kammen, D. M., He, K., and
Schellnhuber, H. J.: Near-real-time monitoring of global CO2 emissions
reveals the effects of the COVID-19 pandemic, Nat. Commun., 11, 5172, https://doi.org/10.1038/s41467-020-18922-7, 2020.
Liu, J., Baskaran, L., Bowman, K., Schimel, D., Bloom, A. A., Parazoo, N. C., Oda, T., Carroll, D., Menemenlis, D., Joiner, J., Commane, R., Daube, B., Gatti, L. V., McKain, K., Miller, J., Stephens, B. B., Sweeney, C., and Wofsy, S.: Carbon Monitoring System Flux Net Biosphere Exchange 2020 (CMS-Flux NBE 2020), Earth Syst. Sci. Data, 13, 299–330, https://doi.org/10.5194/essd-13-299-2021, 2021.
Masarie, K. A., Peters, W., Jacobson, A. R., and Tans, P. P.: ObsPack: a framework for the preparation, delivery, and attribution of atmospheric greenhouse gas measurements, Earth Syst. Sci. Data, 6, 375–384, https://doi.org/10.5194/essd-6-375-2014, 2014.
Miyazaki, K., Maki, T., Patra, P., and Nakazawa, T.: Assessing the impact of
satellite, aircraft, and surface observations on CO2 flux estimation
using an ensemblebased 4-D data assimilation system, J. Geophys. Res., 116,
D16306, https://doi.org/10.1029/2010JD015366, 2011.
Miyoshi, T. and Yamane, S.: Local Ensemble Transform Kalman Filtering with
an AGCM at a T159/L48 Resolution, Mon. Weather Rev., 135, 3841–3861,
https://doi.org/10.1175/2007MWR1873.1, 2007.
Nassar, R., Napier-Linton, L., Gurney, K. R., Andres, R. J., Oda, T., Vogel,
F. R., and Deng, F.: Improving the temporal and spatial distribution of
CO2 emissions from global fossil fuel emission data sets, J. Geophys. Res.-Atmos., 118, 917–933, https://doi.org/10.1029/2012JD018196, 2013.
OCO-2 Science Team/Michael Gunson and Annmarie Eldering: OCO-2 Level 2
bias-corrected XCO2 and other select fields from the full-physics
retrieval aggregated as daily files, Retrospective processing V10r,
Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services
Center (GES DISC), https://doi.org/10.5067/E4E140XDMPO2,
2020.
Oda, T., Maksyutov, S., and Andres, R. J.: The Open-source Data Inventory for Anthropogenic CO2, version 2016 (ODIAC2016): a global monthly fossil fuel CO2 gridded emissions data product for tracer transport simulations and surface flux inversions, Earth Syst. Sci. Data, 10, 87–107, https://doi.org/10.5194/essd-10-87-2018, 2018.
O'Dell, C. W., Eldering, A., Wennberg, P. O., Crisp, D., Gunson, M. R., Fisher, B., Frankenberg, C., Kiel, M., Lindqvist, H., Mandrake, L., Merrelli, A., Natraj, V., Nelson, R. R., Osterman, G. B., Payne, V. H., Taylor, T. E., Wunch, D., Drouin, B. J., Oyafuso, F., Chang, A., McDuffie, J., Smyth, M., Baker, D. F., Basu, S., Chevallier, F., Crowell, S. M. R., Feng, L., Palmer, P. I., Dubey, M., García, O. E., Griffith, D. W. T., Hase, F., Iraci, L. T., Kivi, R., Morino, I., Notholt, J., Ohyama, H., Petri, C., Roehl, C. M., Sha, M. K., Strong, K., Sussmann, R., Te, Y., Uchino, O., and Velazco, V. A.: Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm, Atmos. Meas. Tech., 11, 6539–6576, https://doi.org/10.5194/amt-11-6539-2018, 2018.
Peters, W., Miller, J., Whitaker, J., Denning, S., Hirsch, A., Krol, M.,
Zupanski, D., Bruhwiler, L., and Tans, P.: An ensemble data assimilation
system to estimate CO2 surface fluxes from atmospheric trace gas
observations, J. Geophys. Res., 110, D24304, https://doi.org/10.1029/2005JD006157, 2005.
Peters, W., Jacobson, A. R., Sweeney, C., Andrews, A. E., Conway, T. J.,
Masarie, K., Miller, J. B., Bruhwiler, L. M. P., Pétron, G., Hirsch, A.
I., Worthy, D. E. J., van der Werf, G. R., Randerson, J. T., Wennberg, P.
O., Krol, M. C., and Tans, P. P.: An atmospheric perspective on North
American carbon dioxide exchange: CarbonTracker, P. Natl. Acad. Sci.
USA., 104, 18925–18930, https://doi.org/10.1073/pnas.0708986104, 2007.
Peylin, P., Law, R. M., Gurney, K. R., Chevallier, F., Jacobson, A. R., Maki, T., Niwa, Y., Patra, P. K., Peters, W., Rayner, P. J., Rödenbeck, C., van der Laan-Luijkx, I. T., and Zhang, X.: Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions, Biogeosciences, 10, 6699–6720, https://doi.org/10.5194/bg-10-6699-2013, 2013.
Rödenbeck, C., Zaehle, S., Keeling, R., and Heimann, M.: History of El
Niño impacts on the global carbon cycle 1957–2017: a quantification
from atmospheric CO2 data, Philos. T. Roy. Soc. B, 373, 20170303, https://doi.org/10.1098/rstb.2017.0303,
2018.
Takahashi, T., Sutherland, S. C., Wanninkhof, R., Sweeney, C., Feely, R. A.,
Chipman, D. W., Hales, B., Friederich, G., Chavez, F., Sabine, C., Watson,
A., Bakker, D. C. E., Schuster, U., Metzl, N., Yoshikawa-Inoue, H., Ishii,
M., Midorikawa, T., Nojiri, Y., Körtzinger, A., Steinhoff, T., Hoppema,
M., Olafsson, J., Arnarson, T. S., Tilbrook, B., Johannessen, T., Olsen, A.,
Bellerby, R., Wong, C. S., Delille, B., Bates, N. R., and de Baar, H. J. W.:
Climatological mean and decadal change in surface ocean pCO2, and net
sea–air CO2 flux over the global oceans, Deep-Sea Res. Pt. II, 56, 554–577, https://doi.org/10.1016/j.dsr2.2008.12.009, 2009.
Thompson, R. L., Patra, P. K., Chevallier, F., Maksyutov, S., Law, R. M.,
Ziehn, T., van der Laan-Luijkx, I. T., Peters, W., Ganshin, A., Zhuravlev,
R., Maki, T., Nakamura, T., Shirai, T., Ishizawa, M., Saeki, T., Machida,
T., Poulter, B., Canadell, J. G., and Ciais, P.: Top-down assessment of the
Asian carbon budget since the mid 1990s, Nat. Commun., 7, 10724, https://doi.org/10.1038/ncomms10724, 2016.
Tian, S., Wang, S., Bai, X., Luo, G., Li, Q., Yang, Y., Hu, Z., Li, C., and
Deng, Y.: Global patterns and changes of carbon emissions from land use
during 1992–2015, Environmental Science and Ecotechnology, 7, 100108, https://doi.org/10.1016/j.ese.2021.100108, 2021.
van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates during 1997–2016, Earth Syst. Sci. Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017, 2017.
Zheng, B., Geng, G., Ciais, P., Davis, S. J., Martin, R. V., Meng, J., Wu,
N., Chevallier, F., Broquet, G., Boersma, F., van der A, R., Lin, J., Guan,
D., Lei, Y., He, K., and Zhang, Q.: Satellite-based estimates of decline and
rebound in China's CO2 emissions during COVID-19 pandemic, Sci.
Adv., 6, eabd4998, https://doi.org/10.1126/sciadv.abd4998, 2020.
Zheng, B., Ciais, P., Chevallier, F., Chuvieco, E., Chen, Y., and Yang, H.:
Increasing forest fire emissions despite the decline in global burned area,
Sci. Adv., 7, eabh2646, https://doi.org/10.1126/sciadv.abh2646, 2021.
Zhang, S., Zheng, X., Chen, J. M., Chen, Z., Dan, B., Yi, X., Wang, L., and Wu, G.: A global carbon assimilation system using a modified ensemble Kalman filter, Geosci. Model Dev., 8, 805–816, https://doi.org/10.5194/gmd-8-805-2015, 2015.
Short summary
We developed a Bayesian atmospheric inversion system based on the 4D local ensemble transform Kalman filter (4D-LETKF) algorithm coupled with GEOS-Chem from the latest Orbiting Carbon Observatory-2 (OCO-2) V10r XCO2 retrievals. This is the first adaptation of 4D-LETKF to an OCO-2-based global carbon inversion system. We inferred global gridded carbon fluxes and investigated their magnitudes, variations, and partitioning schemes to understand the global and regional carbon budgets for 2015–2020.
We developed a Bayesian atmospheric inversion system based on the 4D local ensemble transform...
Altmetrics
Final-revised paper
Preprint