Articles | Volume 15, issue 14
https://doi.org/10.5194/gmd-15-5511-2022
© 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/gmd-15-5511-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
19 Jul 2022
Development and technical paper |
| 19 Jul 2022
| 19 Jul 2022
Improving the joint estimation of CO2 and surface carbon fluxes using a constrained ensemble Kalman filter in COLA (v1.0)
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
Earth System Science Interdisciplinary Center, College Park, Maryland, USA
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Yun Liu
International Laboratory for High-Resolution Earth System Model and
Prediction (iHESP), Texas A&M University, College Station, Texas, USA
Department of Oceanography, Texas A&M University, College Station, TX, USA
Eugenia Kalnay
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
Ghassem Asrar
Universities Space Research Association, Columbia, Maryland, USA
Bo Wu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Qixiang Cai
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Di Liu
Research Center for Eco-Environmental Sciences, Chinese Academy of
Sciences, Beijing, China
Pengfei Han
Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 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.
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.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
Short summary
Short summary
Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Qixiang Cai, and Pengfei Han
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-15, https://doi.org/10.5194/gmd-2023-15, 2023
Revised manuscript not accepted
Short summary
Short summary
We introduced a novel algorithm that assimilates a better a priori knowledge to improve the estimation of global surface carbon flux. The algorithm aims at separating the first-order systematic biases in the a priori "bottom-up" flux estimations out of the inversion framework from a comprehensive data assimilation perspective.
Di Liu, Wanqi Sun, Ning Zeng, Pengfei Han, Bo Yao, Zhiqiang Liu, Pucai Wang, Ke Zheng, Han Mei, and Qixiang Cai
Atmos. Chem. Phys., 21, 4599–4614, https://doi.org/10.5194/acp-21-4599-2021, https://doi.org/10.5194/acp-21-4599-2021, 2021
Short summary
Short summary
It is difficult to directly observe the COVID-19 signals in CO2 due to the strong weather induced variations. Here, we determined the on-road CO2 concentration declines in Beijing using mobile observatory data before (BC), during (DC) and after COVID-19 (AC). We chose trips with the most similar weather and calculated the enhancement, the difference between on-road and the city “background”. We showed a clear on-road CO2 decrease in DC, which is consistent with the emissions reductions in DC.
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.
Qixiang Cai, Ning Zeng, Xiaoyu Yang, Chi Xu, Zhaojun Wang, and Pengfei Han
Atmos. Meas. Tech., 18, 4871–4884, https://doi.org/10.5194/amt-18-4871-2025, https://doi.org/10.5194/amt-18-4871-2025, 2025
Short summary
Short summary
Mid- and low-cost CO2 sensors are attractive in carbon monitoring and atmospheric inversions. They are useful in both fixed stations and mobile monitoring. Yet the performance faces great challenges due to environmental impacts and long-term drifts. Here, we conducted 30 months of co-located observations using such sensors with a reference instrument. After corrections of environmental impacts and drifts, the accuracy reached 1–3 ppm. We recommend standard gas calibration within 3–6 months.
Pengfei Han, Ning Zeng, Bo Yao, Wen Zhang, Weijun Quan, Pucai Wang, Ting Wang, Minqiang Zhou, Qixiang Cai, Yuzhong Zhang, Ruosi Liang, Wanqi Sun, and Shengxiang Liu
Atmos. Chem. Phys., 25, 4965–4988, https://doi.org/10.5194/acp-25-4965-2025, https://doi.org/10.5194/acp-25-4965-2025, 2025
Short summary
Short summary
Methane (CH4) is a potent greenhouse gas. Northern China contributes a large proportion of CH4 emissions, yet large observation gaps exist. Here we compiled a comprehensive dataset, which is publicly available, that includes ground-based, satellite-based, inventory, and modeling results to show the CH4 concentrations, enhancements, and spatial–temporal variations. The data can benefit the research community and policy-makers for future observations, atmospheric inversions, and policy-making.
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.
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.
Eugenia Kalnay, Travis Sluka, Takuma Yoshida, Cheng Da, and Safa Mote
Nonlin. Processes Geophys., 30, 217–236, https://doi.org/10.5194/npg-30-217-2023, https://doi.org/10.5194/npg-30-217-2023, 2023
Short summary
Short summary
Strongly coupled data assimilation (SCDA) generates coherent integrated Earth system analyses by assimilating the full Earth observation set into all Earth components. We describe SCDA based on the ensemble Kalman filter with a hierarchy of coupled models, from a coupled Lorenz to the Climate Forecast System v2. SCDA with a sufficiently large ensemble can provide more accurate coupled analyses compared to weakly coupled DA. The correlation-cutoff method can compensate for a small ensemble size.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
Short summary
Short summary
Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Axel Kleidon, Gabriele Messori, Somnath Baidya Roy, Ira Didenkulova, and Ning Zeng
Earth Syst. Dynam., 14, 241–242, https://doi.org/10.5194/esd-14-241-2023, https://doi.org/10.5194/esd-14-241-2023, 2023
Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Qixiang Cai, and Pengfei Han
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-15, https://doi.org/10.5194/gmd-2023-15, 2023
Revised manuscript not accepted
Short summary
Short summary
We introduced a novel algorithm that assimilates a better a priori knowledge to improve the estimation of global surface carbon flux. The algorithm aims at separating the first-order systematic biases in the a priori "bottom-up" flux estimations out of the inversion framework from a comprehensive data assimilation perspective.
Wenxiu Zhang, Di Liu, Hanqin Tian, Naiqin Pan, Ruqi Yang, Wenhan Tang, Jia Yang, Fei Lu, Buddhi Dayananda, Han Mei, Siyuan Wang, and Hao Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-428, https://doi.org/10.5194/essd-2022-428, 2022
Manuscript not accepted for further review
Short summary
Short summary
High temporal resolution surface ozone concentration data is still lacking in China, so we used deep learning to generate hourly surface ozone data (HrSOD) during 2005–2020 across China. HrSOD showed that surface O3 in China tended to increase from 2016 to 2019, despite a decrease in 2020. HrSOD had high spatial and temporal accuracies, long time ranges and high temporal resolution, enabling it to be easily converted to various evaluation indicators for ecosystem and human health assessments.
Chu-Chun Chang and Eugenia Kalnay
Nonlin. Processes Geophys., 29, 317–327, https://doi.org/10.5194/npg-29-317-2022, https://doi.org/10.5194/npg-29-317-2022, 2022
Short summary
Short summary
This study introduces a new approach for enhancing the ensemble data assimilation (DA), a technique that combines observations and forecasts to improve numerical weather predictions. Our method uses the prescribed correlations to suppress spurious errors, improving the accuracy of DA. The experiments on the simplified atmosphere model show that our method has comparable performance to the traditional method but is superior in the early stage and is more computationally efficient.
Ruqi Yang, Jun Wang, Ning Zeng, Stephen Sitch, Wenhan Tang, Matthew Joseph McGrath, Qixiang Cai, Di Liu, Danica Lombardozzi, Hanqin Tian, Atul K. Jain, and Pengfei Han
Earth Syst. Dynam., 13, 833–849, https://doi.org/10.5194/esd-13-833-2022, https://doi.org/10.5194/esd-13-833-2022, 2022
Short summary
Short summary
We comprehensively investigate historical GPP trends based on five kinds of GPP datasets and analyze the causes for any discrepancies among them. Results show contrasting behaviors between modeled and satellite-based GPP trends, and their inconsistencies are likely caused by the contrasting performance between satellite-derived and modeled leaf area index (LAI). Thus, the uncertainty in satellite-based GPP induced by LAI undermines its role in assessing the performance of DGVM simulations.
Tao Zhang, Yuyu Zhou, Zhengyuan Zhu, Xiaoma Li, and Ghassem R. Asrar
Earth Syst. Sci. Data, 14, 651–664, https://doi.org/10.5194/essd-14-651-2022, https://doi.org/10.5194/essd-14-651-2022, 2022
Short summary
Short summary
We generated a global seamless 1 km daily (mid-daytime and mid-nighttime) land surface temperature (LST) dataset (2003–2020) using MODIS LST products by proposing a spatiotemporal gap-filling framework. The average root mean squared errors of the gap-filled LST are 1.88°C and 1.33°C, respectively, in mid-daytime and mid-nighttime. The global seamless LST dataset is unique and of great use in studies on urban systems, climate research and modeling, and terrestrial ecosystem studies.
Nirasindhu Desinayak, Anup K. Prasad, Hesham El-Askary, Menas Kafatos, and Ghassem R. Asrar
Ann. Geophys., 40, 67–82, https://doi.org/10.5194/angeo-40-67-2022, https://doi.org/10.5194/angeo-40-67-2022, 2022
Short summary
Short summary
The study presents long-term altitudinal changes and variability (spatial and temporal; during 2000–2017) in the coverage of snow and glaciers in one of the world’s largest mountainous regions, i.e., the Hindu Kush Himalayan (HKH) region. The western zone and high-altitude regions (above 6000 m) show no significant decline in snow cover, whereas the lower-altitude regions (< 6000 m) show a variable but statistically significant decline in snow cover in the central and eastern zones (5 %–15 %).
Zhaohui Chen, Parvadha Suntharalingam, Andrew J. Watson, Ute Schuster, Jiang Zhu, and Ning Zeng
Biogeosciences, 18, 4549–4570, https://doi.org/10.5194/bg-18-4549-2021, https://doi.org/10.5194/bg-18-4549-2021, 2021
Short summary
Short summary
As the global temperature continues to increase, carbon dioxide (CO2) is a major driver of this global warming. The increased CO2 is mainly caused by emissions from fossil fuel use and land use. At the same time, the ocean is a significant sink in the carbon cycle. The North Atlantic is a critical ocean region in reducing CO2 concentration. We estimate the CO2 uptake in this region based on a carbon inverse system and atmospheric CO2 observations.
Yang Yang, Minqiang Zhou, Ting Wang, Bo Yao, Pengfei Han, Denghui Ji, Wei Zhou, Yele Sun, Gengchen Wang, and Pucai Wang
Atmos. Chem. Phys., 21, 11741–11757, https://doi.org/10.5194/acp-21-11741-2021, https://doi.org/10.5194/acp-21-11741-2021, 2021
Short summary
Short summary
This study introduces the in situ CO2 measurement system installed in Beijing (urban), Xianghe (suburban), and Xinglong (rural) in North China for the first time. The spatial and temporal variations in CO2 mole fractions at the three sites between June 2018 and April 2020 are discussed on both seasonal and diurnal scales.
Di Liu, Wanqi Sun, Ning Zeng, Pengfei Han, Bo Yao, Zhiqiang Liu, Pucai Wang, Ke Zheng, Han Mei, and Qixiang Cai
Atmos. Chem. Phys., 21, 4599–4614, https://doi.org/10.5194/acp-21-4599-2021, https://doi.org/10.5194/acp-21-4599-2021, 2021
Short summary
Short summary
It is difficult to directly observe the COVID-19 signals in CO2 due to the strong weather induced variations. Here, we determined the on-road CO2 concentration declines in Beijing using mobile observatory data before (BC), during (DC) and after COVID-19 (AC). We chose trips with the most similar weather and calculated the enhancement, the difference between on-road and the city “background”. We showed a clear on-road CO2 decrease in DC, which is consistent with the emissions reductions in DC.
Xiaohui Lin, Wen Zhang, Monica Crippa, Shushi Peng, Pengfei Han, Ning Zeng, Lijun Yu, and Guocheng Wang
Earth Syst. Sci. Data, 13, 1073–1088, https://doi.org/10.5194/essd-13-1073-2021, https://doi.org/10.5194/essd-13-1073-2021, 2021
Short summary
Short summary
CH4 is a potent greenhouse gas, and China’s anthropogenic CH4 emissions account for a large proportion of global total emissions. However, the existing estimates either focus on a specific sector or lag behind real time by several years. We collected and analyzed 12 datasets and compared them to reveal the spatiotemporal changes and their uncertainties. We further estimated the emissions from 1990–2019, and the estimates showed a robust trend in recent years when compared to top-down results.
Cited articles
Anderson, J. L.: An adaptive covariance inflation error correction algorithm
for ensemble filters, Tellus Dyn. Meteorol. Oceanogr., 59, 210–224,
https://doi.org/10.1111/j.1600-0870.2006.00216.x, 2007.
Baker, D. F., Law, R. M., Gurney, K. R., Rayner, P., Peylin, P., Denning, A.
S., Bousquet, P., Bruhwiler, L., Chen, Y.-H., Ciais, P., Fung, I. Y.,
Heimann, M., John, J., Maki, T., Maksyutov, S., Masarie, K., Prather, M.,
Pak, B., Taguchi, S., and Zhu, Z.: TransCom 3 inversion intercomparison:
Impact of transport model errors on the interannual variability of regional
CO2 fluxes, 1988–2003, Global Biogeochem. Cy., 20, GB1002,
https://doi.org/10.1029/2004GB002439, 2006a.
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, 2006b.
Baker, D. F., Bell, E., Davis, K. J., Campbell, J. F., Lin, B., and Dobler, J.: A new exponentially decaying error correlation model for assimilating OCO-2 column-average CO2 data using a length scale computed from airborne lidar measurements, Geosci. Model Dev., 15, 649–668, https://doi.org/10.5194/gmd-15-649-2022, 2022.
Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I., Krummel, P., Steele, P., Langenfelds, R., Torn, M., Biraud, S., Stephens, B., Andrews, A., and Worthy, D.: Global CO2 fluxes estimated from GOSAT retrievals of total column CO2, Atmos. Chem. Phys., 13, 8695–8717, https://doi.org/10.5194/acp-13-8695-2013, 2013.
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.
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.
Chen, H. W., Zhang, F., Lauvaux, T., Davis, K. J., Feng, S., Butler, M. P.,
and Alley, R. B.: Characterization of Regional-Scale CO2 Transport
Uncertainties in an Ensemble with Flow-Dependent Transport Errors, Geophys.
Res. Lett., 46, 4049–4058, https://doi.org/10.1029/2018GL081341, 2019.
Chevallier, F., Ciais, P., Conway, T. J., Aalto, T., Anderson, B. E.,
Bousquet, P., Brunke, E. G., Ciattaglia, L., Esaki, Y., Fröhlich, M.,
Gomez, A., Gomez-Pelaez, A. J., Haszpra, L., Krummel, P. B., Langenfelds, R.
L., Leuenberger, M., Machida, T., Maignan, F., Matsueda, H., Morguí, J.
A., Mukai, H., Nakazawa, T., Peylin, P., Ramonet, M., Rivier, L., Sawa, Y.,
Schmidt, M., Steele, L. P., Vay, S. A., Vermeulen, A. T., Wofsy, S., and
Worthy, D.: CO2 surface fluxes at grid point scale estimated from a
global 21 year reanalysis of atmospheric measurements, J. Geophys. Res.,
115, D21307, https://doi.org/10.1029/2010JD013887, 2010a.
Chevallier, F., Feng, L., Bösch, H., Palmer, P. I., and Rayner, P. J.:
On the impact of transport model errors for the estimation of CO2 surface
fluxes from GOSAT observations, Geophys. Res. Lett., 37, L21803,
https://doi.org/10.1029/2010GL044652, 2010b.
Crevoisier, C., Heilliette, S., Chédin, A., Serrar, S., Armante, R., and
Scott, N. A.: Midtropospheric CO2 concentration retrieval from AIRS
observations in the tropics, Geophys. Res. Lett., 31, L17106,
https://doi.org/10.1029/2004GL020141, 2004.
Crisp, D., Pollock, H. R., Rosenberg, R., Chapsky, L., Lee, R. A. M., Oyafuso, F. A., Frankenberg, C., O'Dell, C. W., Bruegge, C. J., Doran, G. B., Eldering, A., Fisher, B. M., Fu, D., Gunson, M. R., Mandrake, L., Osterman, G. B., Schwandner, F. M., Sun, K., Taylor, T. E., Wennberg, P. O., and Wunch, D.: The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products, Atmos. Meas. Tech., 10, 59–81, https://doi.org/10.5194/amt-10-59-2017, 2017.
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.
Denning, A. S., Randall, D. A., Collatz, G. J., and Sellers, P. J.:
Simulations of terrestrial carbon metabolism and atmospheric CO2 in a
general circulation model. Part 2: Simulated CO2 concentrations, Tellus B,
48, 543–567, https://doi.org/10.1034/j.1600-0889.1996.t01-1-00010.x, 1996.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics, J. Geophys.
Res., 99, 10143, https://doi.org/10.1029/94JC00572, 1994.
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.
Friedlingstein, P., Cox, P., Betts, R., Bopp, L., von Bloh, W., Brovkin, V.,
Cadule, P., Doney, S., Eby, M., Fung, I., Bala, G., John, J., Jones, C.,
Joos, F., Kato, T., Kawamiya, M., Knorr, W., Lindsay, K., Matthews, H. D.,
Raddatz, T., Rayner, P., Reick, C., Roeckner, E., Schnitzler, K.-G., Schnur,
R., Strassmann, K., Weaver, A. J., Yoshikawa, C., and Zeng, N.:
Climate–Carbon Cycle Feedback Analysis: Results from the C4MIP Model
Intercomparison, J. Climate, 19, 3337–3353,
https://doi.org/10.1175/JCLI3800.1, 2006.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan,
K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
GEOS-Chem: GEOS-Chem Wiki, GEOS-Chem [code], http://wiki.seas.harvard.edu/geos-chem, last access: 30 June 2022.
Greybush, S. J., Kalnay, E., Miyoshi, T., Ide, K., and Hunt, B. R.: Balance
and Ensemble Kalman Filter Localization Techniques, Mon. Weather Rev., 139,
511–522, https://doi.org/10.1175/2010MWR3328.1, 2011.
Gruber, N., Clement, D., Carter, B. R., Feely, R. A., van Heuven, S.,
Hoppema, M., Ishii, M., Key, R. M., Kozyr, A., Lauvset, S. K., Lo Monaco,
C., Mathis, J. T., Murata, A., Olsen, A., Perez, F. F., Sabine, C. L.,
Tanhua, T., and Wanninkhof, R.: The oceanic sink for anthropogenic CO2
from 1994 to 2007, Science, 363, 1193–1199,
https://doi.org/10.1126/science.aau5153, 2019.
Gurney, K. R., Law, R. M., Denning, A. S., Rayner, P. J., Pak, B. C., Baker,
D., Bousquet, P., Bruhwiler, L., Chen, Y.-H., Ciais, P., Fung, I. Y.,
Heimann, M., John, J., Maki, T., Maksyutov, S., Peylin, P., Prather, M., and
Taguchi, S.: Transcom 3 inversion intercomparison: Model mean results for the estimation of seasonal carbon sources and sinks,
Global Biogeochem. Cy., 18, GB1010, https://doi.org/10.1029/2003GB002111,
2004.
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient Data Assimilation
for Spatiotemporal Chaos: a Local Ensemble Transform Kalman Filter, arXiv [preprint], https://doi.org/10.48550/arXiv.physics/0511236, 29 December 2005.
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation
for spatiotemporal chaos: A local ensemble transform Kalman filter, Phys.
Nonlinear Phenom., 230, 112–126,
https://doi.org/10.1016/j.physd.2006.11.008, 2007.
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.
Kang, J.-S., Kalnay, E., Miyoshi, T., Liu, J., and Fung, I.: Estimation of
surface carbon fluxes with an advanced data assimilation methodology, J.
Geophys. Res. Atmospheres, 117, D24101,
https://doi.org/10.1029/2012JD018259, 2012.
Kondo, M., Patra, P. K., Sitch, S., Friedlingstein, P., Poulter, B.,
Chevallier, F., Ciais, P., Canadell, J. G., Bastos, A., Lauerwald, R.,
Calle, L., Ichii, K., Anthoni, P., Arneth, A., Haverd, V., Jain, A. K.,
Kato, E., Kautz, M., Law, R. M., Lienert, S., Lombardozzi, D., Maki, T.,
Nakamura, T., Peylin, P., Rödenbeck, C., Zhuravlev, R., Saeki, T., Tian,
H., Zhu, D., and Ziehn, T.: State of the science in reconciling top-down and
bottom-up approaches for terrestrial CO 2 budget, Glob. Change Biol.,
26, 1068–1084, https://doi.org/10.1111/gcb.14917, 2020.
Liu, J., Fung, I., Kalnay, E., and Kang, J.-S.: CO2 transport
uncertainties from the uncertainties in meteorological fields, Geophys. Res.
Lett., 38, L12808, https://doi.org/10.1029/2011GL047213, 2011.
Liu, J., Fung, I., Kalnay, E., Kang, J.-S., Olsen, E. T., and Chen, L.:
Simultaneous assimilation of AIRS Xco2 and meteorological observations in a carbon climate model with an ensemble Kalman filter, J. Geophys. Res.-Atmos., 117, D05309,
https://doi.org/10.1029/2011JD016642, 2012.
Liu, J., Bowman, K. W., Lee, M., Henze, D. K., Bousserez, N., Brix, H.,
James Collatz, G., Menemenlis, D., Ott, L., Pawson, S., Jones, D., and
Nassar, R.: Carbon monitoring system flux estimation and attribution: impact
of ACOS-GOSAT X
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. and Zeng, N.: The Constrained Ensemble Kalman Filter used in the COLA data assimilation system (v 1.0), Zenodo [code], https://doi.org/10.5281/zenodo.5746140, 2021.
Lokupitiya, R. S., Zupanski, D., Denning, A. S., Kawa, S. R., Gurney, K. R.,
and Zupanski, M.: Estimation of global CO2 fluxes at regional scale
using the maximum likelihood ensemble filter, J. Geophys. Res., 113, D20110,
https://doi.org/10.1029/2007JD009679, 2008.
Mitchell, H. L. and Houtekamer, P. L.: An Adaptive Ensemble Kalman Filter,
Mon. Weather Rev., 128, 416–433,
https://doi.org/10.1175/1520-0493(2000)128<0416:AAEKF>2.0.CO;2, 2000.
Miyoshi, T.: The Gaussian Approach to Adaptive Covariance Inflation and Its
Implementation with the Local Ensemble Transform Kalman Filter, Mon. Weather
Rev., 139, 1519–1535, https://doi.org/10.1175/2010MWR3570.1, 2011 (data available at: https://github.com/takemasa-miyoshi/letkf, last access: 25 June 2022).
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.
Oda, T. and Maksyutov, S.: A very high-resolution (1 km × 1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights, Atmos. Chem. Phys., 11, 543–556, https://doi.org/10.5194/acp-11-543-2011, 2011.
Pan, M. and Wood, E. F.: Data Assimilation for Estimating the Terrestrial
Water Budget Using a Constrained Ensemble Kalman Filter, J. Hydrometeorol.,
7, 534–547, https://doi.org/10.1175/JHM495.1, 2006.
Peters, W., Miller, J. B., Whitaker, J., Denning, A. S., Hirsch, A., Krol,
M. C., Zupanski, D., Bruhwiler, L., and Tans, P. 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., Petron, 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, Proc. Natl. Acad. Sci. USA, 104,
18925–18930, https://doi.org/10.1073/pnas.0708986104, 2007.
Rödenbeck, C., Houweling, S., Gloor, M., and Heimann, M.: CO2 flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport, Atmos. Chem. Phys., 3, 1919–1964, https://doi.org/10.5194/acp-3-1919-2003, 2003.
Ruiz, J. J., Pulido, M., and Miyoshi, T.: Estimating Model Parameters with
Ensemble-Based Data Assimilation: A Review, J. Meteorol. Soc. Jpn. Ser II,
91, 79–99, https://doi.org/10.2151/jmsj.2013-201, 2013.
Sasakawa, M., Shimoyama, K., Machida, T., Tsuda, N., Suto, H., Arshinov, M.,
Davydov, D., Fofonov, A., Krasnov, O., Saeki, T., Koyama, Y., and Maksyutov,
S.: Continuous measurements of methane from a tower network over Siberia,
Tellus B, 62, 403–416,
https://doi.org/10.1111/j.1600-0889.2010.00494.x, 2010.
Schuh, A. E., Jacobson, A. R., Basu, S., Weir, B., Baker, D., Bowman, K.,
Chevallier, F., Crowell, S., Davis, K. J., Deng, F., Denning, S., Feng, L.,
Jones, D., Liu, J., and Palmer, P. I.: Quantifying the Impact of Atmospheric
Transport Uncertainty on CO2 Surface Flux Estimates, Global Biogeochem.
Cy., 33, 484–500, https://doi.org/10.1029/2018GB006086, 2019.
Schuldt, K. N., Mund, J., Luijkx, I. T., Jacobson, A. R., Cox, A.,
Vermeulen, A., Manning, A., Beyersdorf, A., Manning, A., Karion, A., Hensen,
A., Arlyn Andrews, Frumau, A., Colomb, A., Scheeren, B., Law, B., Baier, B.,
Munger, B., Paplawsky, B., Viner, B., Stephens, B., Daube, B., Labuschagne,
C., Myhre, C. L., Hanson, C., Miller, C. E., Plass-Duelmer, C., Sloop, C.
D., Sweeney, C., Kubistin, D., Goto, D., Jaffe, D., Say, D., Dinther, D. V.,
Bowling, D., Dickon Young, Weyrauch, D., Worthy, D., Dlugokencky, E., Gloor,
E., Cuevas, E., Reyes-Sanchez, E., Hintsa, E., Kort, E., Morgan, E.,
Apadula, F., Francois Gheusi, Meinhardt, F., Moore, F., Vitkova, G., Chen,
G., Bentz, G., Manca, G., Brailsford, G., Forster, G., Riris, H., Meijer,
H., Matsueda, H., Huilin Chen, Levin, I., Lehner, I., Mammarella, I.,
Bartyzel, J., Abshire, J. B., Elkins, J. W., Levula, J., Jaroslaw Necki,
Pichon, J. M., Peischl, J., Müller-Williams, J., Turnbull, J., Miller,
J. B., Lee, J., Lin, J., Josep-Anton Morgui, DiGangi, J. P., Hatakka, J.,
Coletta, J. D., Holst, J., Kominkova, K., McKain, K., Saito, K., Aikin, K.,
Davis, K., Thoning, K., Tørseth, K., Haszpra, L., Mitchell, L., Gatti, L.
V., Emmenegger, L., Lukasz Chmura, Merchant, L., Sha, M. K., Delmotte, M.,
Fischer, M. L., Schumacher, M., Torn, M., Leuenberger, M., Steinbacher, M.,
et al.: Multi-laboratory compilation of atmospheric carbon dioxide data for
the period 1957–2019; obspack_co2_1_GLOBALVIEWplus_v6.0_2020-09-11, NOAA [data set], https://doi.org/10.25925/20200903, 2020.
Tans, P. P., Conway, T. J., and Nakazawa, T.: Latitudinal distribution of
the sources and sinks of atmospheric carbon dioxide derived from surface
observations and an atmospheric transport model, J. Geophys. Res., 94, 5151–5172,
https://doi.org/10.1029/JD094iD04p05151, 1989.
Tans, P. P., Fung, I. Y., and Taikahashi, T.: Observational Constraints on
the Global Atmospheric CO2 Budget, Science, 9, 1431–1438, https://doi.org/10.1126/science.247.4949.1431, 1990.
Whitaker, J. S. and Hamill, T. M.: Evaluating Methods to Account for System
Errors in Ensemble Data Assimilation, Mon. Weather Rev., 140, 3078–3089,
https://doi.org/10.1175/MWR-D-11-00276.1, 2012.
Whitaker, J. S., Hamill, T. M., Wei, X., Song, Y., and Toth, Z.: Ensemble
Data Assimilation with the NCEP Global Forecast System, Mon. Weather Rev.,
136, 463–482, https://doi.org/10.1175/2007MWR2018.1, 2008.
Wu, L., Bocquet, M., Chevallier, F., Lauvaux, T., and Davis, K.:
Hyperparameter estimation for uncertainty quantification in mesoscale carbon
dioxide inversions, Tellus B, 65, 20894,
https://doi.org/10.3402/tellusb.v65i0.20894, 2013.
Yang, D., Liu, Y., Cai, Z., Chen, X., Yao, L., and Lu, D.: First Global
Carbon Dioxide Maps Produced from TanSat Measurements, Adv. Atmospheric
Sci., 35, 621–623, https://doi.org/10.1007/s00376-018-7312-6, 2018.
Yokota, T., Yoshida, Y., Eguchi, N., Ota, Y., Tanaka, T., Watanabe, H., and
Maksyutov, S.: Global Concentrations of CO2 and CH4 Retrieved from GOSAT:
First Preliminary Results, SOLA, 5, 160–163,
https://doi.org/10.2151/sola.2009-041, 2009.
Zeng, N., Mariotti, A., and Wetzel, P.: Terrestrial mechanisms of
interannual CO2 variability, Global Biogeochem. Cy., 19, GB1016,
https://doi.org/10.1029/2004GB002273, 2005.
Zeng, Y., Janjiæ, T., Ruckstuhl, Y., and Verlaan, M.: Ensemble-type
Kalman filter algorithm conserving mass, total energy and enstrophy: SQPEns
Conserving Mass, Total Energy and Enstrophy, Q. J. Roy. Meteor. Soc., 143,
2902–2914, https://doi.org/10.1002/qj.3142, 2017.
Zeng, Y., de Lozar, A., Janjic, T., and Seifert, A.: Applying a new integrated mass-flux adjustment filter in rapid update cycling of convective-scale data assimilation for the COSMO model (v5.07), Geosci. Model Dev., 14, 1295–1307, https://doi.org/10.5194/gmd-14-1295-2021, 2021a.
Zeng, Y., Janjiæ, T., de Lozar, A., Welzbacher, C. A., Blahak, U., and
Seifert, A.: Assimilating radar radial wind and reflectivity data in an
idealized setup of the COSMO-KENDA system, Atmospheric Res., 249, 105282,
https://doi.org/10.1016/j.atmosres.2020.105282, 2021b.
Zhang, F., Snyder, C., and Sun, J.: Impacts of Initial Estimate and
Observation Availability on Convective-Scale Data Assimilation with an
Ensemble Kalman Filter, Mon. Weather Rev., 132, 16, 1238–1253, https://doi.org/10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2, 2004.
Zupanski, D., Denning, A. S., Uliasz, M., Zupanski, M., Schuh, A. E.,
Rayner, P. J., Peters, W., and Corbin, K. D.: Carbon flux bias estimation
employing Maximum Likelihood Ensemble Filter (MLEF), J. Geophys. Res., 112,
D17107, https://doi.org/10.1029/2006JD008371, 2007.
Short summary
We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and surface carbon fluxes estimation study. By assimilating the pseudo-surface and OCO-2 observations, the annual global flux estimation is significantly biased without mass conservation. With the additional CEnKF process, the CO2 mass is strictly constrained, and the estimation of annual fluxes is significantly improved.
We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and...
