Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1505-2025
© Author(s) 2025. 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-18-1505-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Mar 2025
Development and technical paper |
| 10 Mar 2025
| 10 Mar 2025
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
Antoine Berchet
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
Lionel Constantin
Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
Aki Tsuruta
Finnish Meteorological Institute (FMI), Helsinki, Finland
Michael Steiner
Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
Friedemann Reum
Deutsches Zentrum für Luft- und Raumfahrt e.V., Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Stephan Henne
Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
Dominik Brunner
Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
Related authors
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.
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.
Joël Thanwerdas, Marielle Saunois, Antoine Berchet, Isabelle Pison, and Philippe Bousquet
Atmos. Chem. Phys., 24, 2129–2167, https://doi.org/10.5194/acp-24-2129-2024, https://doi.org/10.5194/acp-24-2129-2024, 2024
Short summary
Short summary
We investigate the causes of the renewed growth of atmospheric methane (CH4) after 2007 using inverse modeling. We use the additional information provided by observations of CH4 isotopic compositions to better differentiate between the emission categories. Accounting for the large uncertainties in source signatures, our results suggest that the post-2007 increase in atmospheric CH4 was caused by similar increases in emissions from (1) fossil fuels and (2) agriculture and waste.
Sophie Wittig, Antoine Berchet, Isabelle Pison, Marielle Saunois, Joël Thanwerdas, Adrien Martinez, Jean-Daniel Paris, Toshinobu Machida, Motoki Sasakawa, Douglas E. J. Worthy, Xin Lan, Rona L. Thompson, Espen Sollum, and Mikhail Arshinov
Atmos. Chem. Phys., 23, 6457–6485, https://doi.org/10.5194/acp-23-6457-2023, https://doi.org/10.5194/acp-23-6457-2023, 2023
Short summary
Short summary
Here, an inverse modelling approach is applied to estimate CH4 sources and sinks in the Arctic from 2008 to 2019. We study the magnitude, seasonal patterns and trends from different sources during recent years. We also assess how the current observation network helps to constrain fluxes. We find that constraints are only significant for North America and, to a lesser extent, West Siberia, where the observation network is relatively dense. We find no clear trend over the period of inversion.
Joël Thanwerdas, Marielle Saunois, Isabelle Pison, Didier Hauglustaine, Antoine Berchet, Bianca Baier, Colm Sweeney, and Philippe Bousquet
Atmos. Chem. Phys., 22, 15489–15508, https://doi.org/10.5194/acp-22-15489-2022, https://doi.org/10.5194/acp-22-15489-2022, 2022
Short summary
Short summary
Atmospheric methane (CH4) concentrations have been rising since 2007, resulting from an imbalance between CH4 sources and sinks. The CH4 budget is generally estimated through top-down approaches using CH4 and δ13C(CH4) observations as constraints. The oxidation by chlorine (Cl) contributes little to the total oxidation of CH4 but strongly influences δ13C(CH4). Here, we compare multiple recent Cl fields and quantify the influence of Cl concentrations on CH4, δ13C(CH4), and CH4 budget estimates.
Joël Thanwerdas, Marielle Saunois, Antoine Berchet, Isabelle Pison, Bruce H. Vaughn, Sylvia Englund Michel, and Philippe Bousquet
Geosci. Model Dev., 15, 4831–4851, https://doi.org/10.5194/gmd-15-4831-2022, https://doi.org/10.5194/gmd-15-4831-2022, 2022
Short summary
Short summary
Estimating CH4 sources by exploiting observations within an inverse modeling framework is a powerful approach. Here, a new system designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. By optimizing both the emissions and associated source signatures of multiple emission categories, this new system can efficiently differentiate the co-located emission categories and provide estimates of CH4 sources that are consistent with isotopic data.
Antoine Berchet, Espen Sollum, Rona L. Thompson, Isabelle Pison, Joël Thanwerdas, Grégoire Broquet, Frédéric Chevallier, Tuula Aalto, Adrien Berchet, Peter Bergamaschi, Dominik Brunner, Richard Engelen, Audrey Fortems-Cheiney, Christoph Gerbig, Christine D. Groot Zwaaftink, Jean-Matthieu Haussaire, Stephan Henne, Sander Houweling, Ute Karstens, Werner L. Kutsch, Ingrid T. Luijkx, Guillaume Monteil, Paul I. Palmer, Jacob C. A. van Peet, Wouter Peters, Philippe Peylin, Elise Potier, Christian Rödenbeck, Marielle Saunois, Marko Scholze, Aki Tsuruta, and Yuanhong Zhao
Geosci. Model Dev., 14, 5331–5354, https://doi.org/10.5194/gmd-14-5331-2021, https://doi.org/10.5194/gmd-14-5331-2021, 2021
Short summary
Short summary
We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is a programming protocol to allow various inversion bricks to be exchanged among researchers.
The ensemble of bricks makes a flexible, transparent and open-source Python-based tool. We describe the main structure and functionalities and demonstrate it in a simple academic case.
Sara Hyvärinen, Maria Katariina Tenkanen, Aki Tsuruta, Anttoni Erkkilä, Kimmo Rautiainen, Hermanni Aaltonen, Motoki Sasakawa, and Tuula Aalto
EGUsphere, https://doi.org/10.5194/egusphere-2025-2794, https://doi.org/10.5194/egusphere-2025-2794, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We analyzed spring methane emissions from northern high-latitude wetlands using satellite thaw data and inverse modeling (2011–2021). Comparing region-based and grid-based approaches, we found that emissions varied with the length of the melting season, which depended on air temperature. We found spring melting season emissions ranged from 0.45 Tg to 1.83 Tg depending on the approach, with no clear trend over the period. Our methods allow for seasonal methane monitoring across different scales.
Aki Tsuruta, Akihiko Kuze, Kei Shiomi, Fumie Kataoka, Nobuhiro Kikuchi, Tuula Aalto, Leif Backman, Ella Kivimäki, Maria K. Tenkanen, Kathryn McKain, Omaira E. García, Frank Hase, Rigel Kivi, Isamu Morino, Hirofumi Ohyama, David F. Pollard, Mahesh K. Sha, Kimberly Strong, Ralf Sussmann, Yao Te, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, Minqiang Zhou, and Hiroshi Suto
Atmos. Chem. Phys., 25, 7829–7862, https://doi.org/10.5194/acp-25-7829-2025, https://doi.org/10.5194/acp-25-7829-2025, 2025
Short summary
Short summary
Satellite data bring invaluable information about greenhouse gas emissions globally. We found that a new type of data from the Greenhouse Gas Observing Satellite (GOSAT), which contains information about methane in the lowest layer of Earth's atmosphere, could provide reliable estimates of recent methane emissions when combined with atmospheric modelling. Therefore, the use of such data is encouraged to improve emission quantification methods and advance our understanding of methane cycles.
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
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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.
Antoine Berchet, Isabelle Pison, Camille Huselstein, Clément Narbaud, Marine Remaud, Sauveur Belviso, Camille Abadie, and Fabienne Maignan
Atmos. Chem. Phys., 25, 7499–7525, https://doi.org/10.5194/acp-25-7499-2025, https://doi.org/10.5194/acp-25-7499-2025, 2025
Short summary
Short summary
We use the measurements of atmospheric carbonyl sulfide (COS) concentrations at the monitoring site of Gif-sur-Yvette (in the Paris region) from August 2014 to December 2019, combined with existing knowledge on COS fluxes in the atmosphere and a transport model, to gain insight into COS fluxes, either natural, such as oceanic emissions or vegetation and soil fluxes, or anthropogenic, from industrial activities and power generation.
Antti Laitinen, Hermanni Aaltonen, Christoph Zellweger, Aki Tsuruta, Tuula Aalto, and Juha Hatakka
Atmos. Meas. Tech., 18, 3109–3133, https://doi.org/10.5194/amt-18-3109-2025, https://doi.org/10.5194/amt-18-3109-2025, 2025
Short summary
Short summary
This paper presents long-term observations of atmospheric CO2 and CH4 mole fractions and a comparison of two permanent and two mobile measurement systems located in Northern Finland. Furthermore, the observed mole fractions are compared against the mean marine boundary layer product for the Northern Hemisphere. The comparisons of all the systems show good agreement in relation to the World Meteorological Organization/Global Atmosphere Watch network compatibility goal limits for CO2 and CH4.
Aurélien Sicsik-Paré, Audrey Fortems-Cheiney, Isabelle Pison, Grégoire Broquet, Alvin Opler, Elise Potier, Adrien Martinez, Oliver Schneising, Michael Buchwitz, Joannes D. Maasakkers, Tobias Borsdorff, and Antoine Berchet
EGUsphere, https://doi.org/10.5194/egusphere-2025-2622, https://doi.org/10.5194/egusphere-2025-2622, 2025
Short summary
Short summary
Assimilating satellite observations from TROPOMI provides top-down quantification of regional methane emissions. This study compares European emissions in 2019 estimated from the inversion of three TROPOMI datasets. We find inconsistencies in national budgets and spatial patterns, with no product clearly superior. We disentangle drivers of the differences, highlighting the impact of differences in coverage, observations and associated errors on the consistency of methane emission estimates.
Jakob Boyd Pernov, William H. Aeberhard, Michele Volpi, Eliza Harris, Benjamin Hohermuth, Sakiko Ishino, Ragnhild B. Skeie, Stephan Henne, Ulas Im, Patricia K. Quinn, Lucia M. Upchurch, and Julia Schmale
Atmos. Chem. Phys., 25, 6497–6537, https://doi.org/10.5194/acp-25-6497-2025, https://doi.org/10.5194/acp-25-6497-2025, 2025
Short summary
Short summary
Particulate methanesulfonic acid (MSAp) is vital for the Arctic climate system. Numerical models struggle to reproduce the MSAp seasonal cycle. We evaluate three numerical models and one reanalysis product’s ability to simulate MSAp. We develop data-driven models for MSAp at four Arctic stations. The data-driven models outperform the numerical models and reanalysis product and identified precursor source-, chemical-processing-, and removal-related features as being important for modeling MSAp.
Stephan Henne, Florian R. Storck, Henry Wöhrnschimmel, Markus Leuenberger, Martin K. Vollmer, and Stefan Reimann
EGUsphere, https://doi.org/10.5194/egusphere-2025-2861, https://doi.org/10.5194/egusphere-2025-2861, 2025
Short summary
Short summary
We observed the persistent, man-made trifluoroacetate (TFA) in precipitation and surface waters. Atmospheric simulations attributed TFA to precursor gases in the atmosphere. Although recently increasing concentrations could be followed, gaps in the budget indicate limited understanding of the atmospheric degradation of widely used fluorocarbons. Without additional regulation, environmental TFA concentrations are expected to rise strongly, necessitating continued monitoring and risk assessment.
Audrey Fortems-Cheiney, Grégoire Broquet, Elise Potier, Antoine Berchet, Isabelle Pison, Adrien Martinez, Robin Plauchu, Rimal Abeed, Aurélien Sicsik-Paré, Gaelle Dufour, Adriana Coman, Dilek Savas, Guillaume Siour, Henk Eskes, Hugo A. C. Denier van der Gon, and Stijn N. C. Dellaert
Atmos. Chem. Phys., 25, 6047–6068, https://doi.org/10.5194/acp-25-6047-2025, https://doi.org/10.5194/acp-25-6047-2025, 2025
Short summary
Short summary
This study assesses the potential of the Ozone Monitoring Instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI) satellite observations to inform about the decrease in anthropogenic emissions of nitrogen oxides (NOx) in 2019 compared with 2005 at regional to national scales in Europe. Both the OMI and TROPOMI inversions show decreases in European NOx anthropogenic emission budgets in 2019 compared to 2005 but with different magnitudes.
Eleftherios Ioannidis, Antoon Meesters, Michael Steiner, Dominik Brunner, Friedemann Reum, Isabelle Pison, Antoine Berchet, Rona Thompson, Espen Sollum, Frank-Thomas Koch, Christoph Gerbig, Fenjuan Wang, Shamil Maksyutov, Aki Tsuruta, Maria Tenkanen, Tuula Aalto, Guillaume Monteil, Hong Lin, Ge Ren, Marko Scholze, and Sander Houweling
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-235, https://doi.org/10.5194/essd-2025-235, 2025
Preprint under review for ESSD
Short summary
Short summary
This paper describes a detailed study on CH4 European emissions, using different methodologies (9 total inverse models). The study spans over 15 years and provides detailed information on European CH4 emission trends and seasonality, using in-situ data, including ICOS network. Our results highlight the importance of improving details in the inversion setup, such as the treatment of lateral boundary conditions to narrow the uncertainty ranges further.
Gerrit Kuhlmann, Foteini Stavropoulou, Stefan Schwietzke, Daniel Zavala-Araiza, Andrew Thorpe, Andreas Hueni, Lukas Emmenegger, Andreea Calcan, Thomas Röckmann, and Dominik Brunner
Atmos. Chem. Phys., 25, 5371–5385, https://doi.org/10.5194/acp-25-5371-2025, https://doi.org/10.5194/acp-25-5371-2025, 2025
Short summary
Short summary
A measurement campaign in 2019 found that methane emissions from oil and gas in Romania were significantly higher than reported. In 2021, our follow-up campaign using airborne remote sensing showed a marked decreases in emissions by 20 %–60 % due to improved infrastructure. The study highlights the importance of measurement-based emission monitoring and illustrates the value of a multi-scale assessment integrating ground-based observations with large-scale airborne remote sensing campaigns.
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.
Stavros Stagakis, Dominik Brunner, Junwei Li, Leif Backman, Anni Karvonen, Lionel Constantin, Leena Järvi, Minttu Havu, Jia Chen, Sophie Emberger, and Liisa Kulmala
Biogeosciences, 22, 2133–2161, https://doi.org/10.5194/bg-22-2133-2025, https://doi.org/10.5194/bg-22-2133-2025, 2025
Short summary
Short summary
The balance between CO2 uptake and emissions from urban green areas is still not well understood. This study evaluated for the first time the urban park CO2 exchange simulations with four different types of biosphere model by comparing them with observations. Even though some advantages and disadvantages of the different model types were identified, there was no strong evidence that more complex models performed better than simple ones.
Dominik Brunner, Ivo Suter, Leonie Bernet, Lionel Constantin, Stuart K. Grange, Pascal Rubli, Junwei Li, Jia Chen, Alessandro Bigi, and Lukas Emmenegger
EGUsphere, https://doi.org/10.5194/egusphere-2025-640, https://doi.org/10.5194/egusphere-2025-640, 2025
Short summary
Short summary
In order to support the city of Zurich in tracking its path to net-zero greenhouse gas emissions planned to be reached by 2040, a CO2 emission monitoring system was established. The system combines a dense network of CO2 sensors with a high-resolution atmospheric transport model GRAMM/GRAL. This study presents the setup of the model together with its numerous inputs and evaluates its performance in comparison with the observations from the CO2 sensor network.
Rainer Hilland, Josh Hashemi, Stavros Stagakis, Dominik Brunner, Lionel Constantin, Natascha Kljun, Betty Molinier, Samuel Hammer, Lukas Emmenegger, and Andreas Christen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1088, https://doi.org/10.5194/egusphere-2025-1088, 2025
Short summary
Short summary
We present a study of simultaneously measured fluxes of carbon dioxide (CO2) and co-emitted species in the city of Zurich. Flux measurements of CO2 alone can’t be attributed to specific emission sectors, such as road transport or residential heating. We present a model which uses the measured ratios of CO2 to carbon monoxide (CO) and nitrogen oxides (NOx) as well as sector-specific reference ratios, to attribute measured fluxes to their emission sectors.
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.
Stuart K. Grange, Pascal Rubli, Andrea Fischer, Dominik Brunner, Christoph Hueglin, and Lukas Emmenegger
Atmos. Chem. Phys., 25, 2781–2806, https://doi.org/10.5194/acp-25-2781-2025, https://doi.org/10.5194/acp-25-2781-2025, 2025
Short summary
Short summary
Carbon dioxide (CO2) is a very important atmospheric pollutant, and to better understand the gas's source and sink dynamics, a mid-cost sensor network hosting 26 sites was deployed in and around Zurich, Switzerland. The sensor measurement performance was quantified, and natural and anthropogenic CO2 emission sources were explored with a focus on what drives high CO2 levels. The observations will be used further by others to validate what is thought to be known about CO2 emissions in the region.
Fabian Maier, Eva Falge, Maksym Gachkivskyi, Stephan Henne, Ute Karstens, Dafina Kikaj, Ingeborg Levin, Alistair Manning, Christian Rödenbeck, and Christoph Gerbig
EGUsphere, https://doi.org/10.5194/egusphere-2025-477, https://doi.org/10.5194/egusphere-2025-477, 2025
Short summary
Short summary
The radioactive noble gas radon (222Rn) is a suitable natural tracer for atmospheric transport and mixing processes that can be used to validate and calibrate atmospheric transport models. However, this requires accurate estimates of the 222Rn flux from the soil into the atmosphere. In our study, we evaluate the reliability of process-based 222Rn flux maps for Europe using a 222Rn inversion. Our inversion results can give some indications on how to improve the process-based 222Rn flux maps.
Maria K. Tenkanen, Aki Tsuruta, Hugo Denier van der Gon, Lena Höglund-Isaksson, Antti Leppänen, Tiina Markkanen, Ana Maria Roxana Petrescu, Maarit Raivonen, Hermanni Aaltonen, and Tuula Aalto
Atmos. Chem. Phys., 25, 2181–2206, https://doi.org/10.5194/acp-25-2181-2025, https://doi.org/10.5194/acp-25-2181-2025, 2025
Short summary
Short summary
Accurate national methane (CH4) emission estimates are essential for tracking progress towards climate goals. This study compares estimates from Finland, which use different methods and scales, and shows how well a global model estimates emissions within a country. The bottom-up estimates vary a lot, but constraining them with atmospheric CH4 measurements brought the estimates closer together. We also highlight the importance of quantifying natural emissions alongside anthropogenic emissions.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
Short summary
Short summary
Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Hossein Maazallahi, Foteini Stavropoulou, Samuel Jonson Sutanto, Michael Steiner, Dominik Brunner, Mariano Mertens, Patrick Jöckel, Antoon Visschedijk, Hugo Denier van der Gon, Stijn Dellaert, Nataly Velandia Salinas, Stefan Schwietzke, Daniel Zavala-Araiza, Sorin Ghemulet, Alexandru Pana, Magdalena Ardelean, Marius Corbu, Andreea Calcan, Stephen A. Conley, Mackenzie L. Smith, and Thomas Röckmann
Atmos. Chem. Phys., 25, 1497–1511, https://doi.org/10.5194/acp-25-1497-2025, https://doi.org/10.5194/acp-25-1497-2025, 2025
Short summary
Short summary
This article presents insights from airborne in situ measurements collected during the ROmanian Methane Emissions from Oil and gas (ROMEO) campaign supported by two models. Results reveal Romania's oil and gas methane emissions were significantly under-reported to the United Nations Framework Convention on Climate Change (UNFCCC) in 2019. A large underestimation was also found in the Emissions Database for Global Atmospheric Research (EDGAR) v7.0 for the study domain in the same year.
Ella Kivimäki, Tuula Aalto, Michael Buchwitz, Kari Luojus, Jouni Pulliainen, Kimmo Rautiainen, Oliver Schneising, Anu-Maija Sundström, Johanna Tamminen, Aki Tsuruta, and Hannakaisa Lindqvist
EGUsphere, https://doi.org/10.5194/egusphere-2025-249, https://doi.org/10.5194/egusphere-2025-249, 2025
Short summary
Short summary
We investigate how environmental variables influencing natural methane fluxes explain the large-scale seasonal variability of satellite-observed methane at Northern high latitudes. Our findings show that soil moisture, snow cover, and soil temperature have the strongest influence, with snowmelt playing a surprisingly significant role, likely through soil isolation and wetting. This study highlights the value of multi-satellite observations for understanding large-scale wetland emissions.
Tuula Aalto, Aki Tsuruta, Jarmo Mäkelä, Jurek Müller, Maria Tenkanen, Eleanor Burke, Sarah Chadburn, Yao Gao, Vilma Mannisenaho, Thomas Kleinen, Hanna Lee, Antti Leppänen, Tiina Markkanen, Stefano Materia, Paul A. Miller, Daniele Peano, Olli Peltola, Benjamin Poulter, Maarit Raivonen, Marielle Saunois, David Wårlind, and Sönke Zaehle
Biogeosciences, 22, 323–340, https://doi.org/10.5194/bg-22-323-2025, https://doi.org/10.5194/bg-22-323-2025, 2025
Short summary
Short summary
Wetland methane responses to temperature and precipitation were studied in a boreal wetland-rich region in northern Europe using ecosystem models, atmospheric inversions, and upscaled flux observations. The ecosystem models differed in their responses to temperature and precipitation and in their seasonality. However, multi-model means, inversions, and upscaled fluxes had similar seasonality, and they suggested co-limitation by temperature and precipitation.
Diego Santaren, Janne Hakkarainen, Gerrit Kuhlmann, Erik Koene, Frédéric Chevallier, Iolanda Ialongo, Hannakaisa Lindqvist, Janne Nurmela, Johanna Tamminen, Laia Amorós, Dominik Brunner, and Grégoire Broquet
Atmos. Meas. Tech., 18, 211–239, https://doi.org/10.5194/amt-18-211-2025, https://doi.org/10.5194/amt-18-211-2025, 2025
Short summary
Short summary
This study evaluates data-driven inversion methods for estimating CO2 emissions from local sources, such as power plants and cities, using meteorological data and XCO2 and NO2 satellite images rather than atmospheric transport modeling. We assess and compare the performance of five different methods using simulations of 1 year of satellite images, taken from the upcoming Copernicus CO2 Monitoring Mission, covering 15 power plants and the city of Berlin, Germany.
Michael Steiner, Luca Cantarello, Stephan Henne, and Dominik Brunner
Atmos. Chem. Phys., 24, 12447–12463, https://doi.org/10.5194/acp-24-12447-2024, https://doi.org/10.5194/acp-24-12447-2024, 2024
Short summary
Short summary
Atmospheric greenhouse gas inversions have great potential to independently check reported bottom-up emissions; however they are subject to large uncertainties. It is paramount to address and reduce the largest source of uncertainty, which stems from the representation of atmospheric transport in the models. In this study, we show that the use of a temporally varying flow-dependent atmospheric transport uncertainty can enhance the accuracy of emission estimation in an idealized experiment.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
Short summary
Short summary
Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
Short summary
Short summary
Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Ana Maria Roxana Petrescu, Glen P. Peters, Richard Engelen, Sander Houweling, Dominik Brunner, Aki Tsuruta, Bradley Matthews, Prabir K. Patra, Dmitry Belikov, Rona L. Thompson, Lena Höglund-Isaksson, Wenxin Zhang, Arjo J. Segers, Giuseppe Etiope, Giancarlo Ciotoli, Philippe Peylin, Frédéric Chevallier, Tuula Aalto, Robbie M. Andrew, David Bastviken, Antoine Berchet, Grégoire Broquet, Giulia Conchedda, Stijn N. C. Dellaert, Hugo Denier van der Gon, Johannes Gütschow, Jean-Matthieu Haussaire, Ronny Lauerwald, Tiina Markkanen, Jacob C. A. van Peet, Isabelle Pison, Pierre Regnier, Espen Solum, Marko Scholze, Maria Tenkanen, Francesco N. Tubiello, Guido R. van der Werf, and John R. Worden
Earth Syst. Sci. Data, 16, 4325–4350, https://doi.org/10.5194/essd-16-4325-2024, https://doi.org/10.5194/essd-16-4325-2024, 2024
Short summary
Short summary
This study provides an overview of data availability from observation- and inventory-based CH4 emission estimates. It systematically compares them and provides recommendations for robust comparisons, aiming to steadily engage more parties in using observational methods to complement their UNFCCC submissions. Anticipating improvements in atmospheric modelling and observations, future developments need to resolve knowledge gaps in both approaches and to better quantify remaining uncertainty.
Robin Plauchu, Audrey Fortems-Cheiney, Grégoire Broquet, Isabelle Pison, Antoine Berchet, Elise Potier, Gaëlle Dufour, Adriana Coman, Dilek Savas, Guillaume Siour, and Henk Eskes
Atmos. Chem. Phys., 24, 8139–8163, https://doi.org/10.5194/acp-24-8139-2024, https://doi.org/10.5194/acp-24-8139-2024, 2024
Short summary
Short summary
This study uses the Community Inversion Framework and CHIMERE model to assess the potential of TROPOMI-S5P PAL NO2 tropospheric column data to estimate NOx emissions in France (2019–2021). Results show a 3 % decrease in average emissions compared to the 2016 CAMS-REG/INS, lower than the 14 % decrease from CITEPA. The study highlights challenges in capturing emission anomalies due to limited data coverage and error levels but shows promise for local inventory improvements.
Sandro Meier, Erik F. M. Koene, Maarten Krol, Dominik Brunner, Alexander Damm, and Gerrit Kuhlmann
Atmos. Chem. Phys., 24, 7667–7686, https://doi.org/10.5194/acp-24-7667-2024, https://doi.org/10.5194/acp-24-7667-2024, 2024
Short summary
Short summary
Nitrogen oxides (NOx = NO + NO2) are important air pollutants. This study addresses the challenge of accurately estimating NOx emissions from NO2 satellite observations. We develop a realistic model to convert NO2 to NOx by using simulated plumes from various power plants. We apply the model to satellite NO2 observations, significantly reducing biases in estimated NOx emissions. The study highlights the potential for a consistent, high-resolution estimation of NOx emissions using satellite data.
Gerrit Kuhlmann, Erik Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
Geosci. Model Dev., 17, 4773–4789, https://doi.org/10.5194/gmd-17-4773-2024, https://doi.org/10.5194/gmd-17-4773-2024, 2024
Short summary
Short summary
We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter notebooks included in the library.
Sophie Wittig, Antoine Berchet, Isabelle Pison, Marielle Saunois, and Jean-Daniel Paris
Atmos. Chem. Phys., 24, 6359–6373, https://doi.org/10.5194/acp-24-6359-2024, https://doi.org/10.5194/acp-24-6359-2024, 2024
Short summary
Short summary
The aim of this work is to analyse how accurately a methane bomb event could be detected with the current and a hypothetically extended stationary observation network in the Arctic. For this, we incorporate synthetically modelled possible future CH4 concentrations based on plausible emission scenarios into an inverse modelling framework. We analyse how well the increase is detected in different Arctic regions and evaluate the impact of additional observation sites in this respect.
Audrey Fortems-Cheiney, Gregoire Broquet, Elise Potier, Robin Plauchu, Antoine Berchet, Isabelle Pison, Hugo Denier van der Gon, and Stijn Dellaert
Atmos. Chem. Phys., 24, 4635–4649, https://doi.org/10.5194/acp-24-4635-2024, https://doi.org/10.5194/acp-24-4635-2024, 2024
Short summary
Short summary
We have estimated the carbon monixide (CO) European emissions from satellite observations of the MOPITT instrument at the relatively high resolution of 0.5° for a period of over 10 years from 2011 to 2021. The analysis of the inversion results reveals the challenges associated with the inversion of CO emissions at the regional scale over Europe.
Michael Steiner, Wouter Peters, Ingrid Luijkx, Stephan Henne, Huilin Chen, Samuel Hammer, and Dominik Brunner
Atmos. Chem. Phys., 24, 2759–2782, https://doi.org/10.5194/acp-24-2759-2024, https://doi.org/10.5194/acp-24-2759-2024, 2024
Short summary
Short summary
The Paris Agreement increased interest in estimating greenhouse gas (GHG) emissions of individual countries, but top-down emission estimation is not yet considered policy-relevant. It is therefore paramount to reduce large errors and to build systems that are based on the newest atmospheric transport models. In this study, we present the first application of ICON-ART in the inverse modeling of GHG fluxes with an ensemble Kalman filter and present our results for European CH4 emissions.
Robert Hanfland, Dominik Brunner, Christiane Voigt, Alina Fiehn, Anke Roiger, and Margit Pattantyús-Ábrahám
Atmos. Chem. Phys., 24, 2511–2534, https://doi.org/10.5194/acp-24-2511-2024, https://doi.org/10.5194/acp-24-2511-2024, 2024
Short summary
Short summary
To show that the three-dimensional dispersion of plumes simulated by the Atmospheric Radionuclide Transport Model within the planetary boundary layer agrees with real plumes, we identify the most important input parameters and analyse the turbulence properties of five different turbulence models in very unstable stratification conditions using their deviation from the well-mixed state. Simulations show that one model agrees slightly better in unstable stratification conditions.
Joël Thanwerdas, Marielle Saunois, Antoine Berchet, Isabelle Pison, and Philippe Bousquet
Atmos. Chem. Phys., 24, 2129–2167, https://doi.org/10.5194/acp-24-2129-2024, https://doi.org/10.5194/acp-24-2129-2024, 2024
Short summary
Short summary
We investigate the causes of the renewed growth of atmospheric methane (CH4) after 2007 using inverse modeling. We use the additional information provided by observations of CH4 isotopic compositions to better differentiate between the emission categories. Accounting for the large uncertainties in source signatures, our results suggest that the post-2007 increase in atmospheric CH4 was caused by similar increases in emissions from (1) fossil fuels and (2) agriculture and waste.
Ioannis Katharopoulos, Dominique Rust, Martin K. Vollmer, Dominik Brunner, Stefan Reimann, Simon J. O'Doherty, Dickon Young, Kieran M. Stanley, Tanja Schuck, Jgor Arduini, Lukas Emmenegger, and Stephan Henne
Atmos. Chem. Phys., 23, 14159–14186, https://doi.org/10.5194/acp-23-14159-2023, https://doi.org/10.5194/acp-23-14159-2023, 2023
Short summary
Short summary
The effectiveness of climate change mitigation needs to be scrutinized by monitoring greenhouse gas (GHG) emissions. Countries report their emissions to the UN in a bottom-up manner. By combining atmospheric observations and transport models someone can independently validate emission estimates in a top-down fashion. We report Swiss emissions of synthetic GHGs based on kilometer-scale transport and inverse modeling, highlighting the role of appropriate resolution in complex terrain.
Matthew J. McGrath, Ana Maria Roxana Petrescu, Philippe Peylin, Robbie M. Andrew, Bradley Matthews, Frank Dentener, Juraj Balkovič, Vladislav Bastrikov, Meike Becker, Gregoire Broquet, Philippe Ciais, Audrey Fortems-Cheiney, Raphael Ganzenmüller, Giacomo Grassi, Ian Harris, Matthew Jones, Jürgen Knauer, Matthias Kuhnert, Guillaume Monteil, Saqr Munassar, Paul I. Palmer, Glen P. Peters, Chunjing Qiu, Mart-Jan Schelhaas, Oksana Tarasova, Matteo Vizzarri, Karina Winkler, Gianpaolo Balsamo, Antoine Berchet, Peter Briggs, Patrick Brockmann, Frédéric Chevallier, Giulia Conchedda, Monica Crippa, Stijn N. C. Dellaert, Hugo A. C. Denier van der Gon, Sara Filipek, Pierre Friedlingstein, Richard Fuchs, Michael Gauss, Christoph Gerbig, Diego Guizzardi, Dirk Günther, Richard A. Houghton, Greet Janssens-Maenhout, Ronny Lauerwald, Bas Lerink, Ingrid T. Luijkx, Géraud Moulas, Marilena Muntean, Gert-Jan Nabuurs, Aurélie Paquirissamy, Lucia Perugini, Wouter Peters, Roberto Pilli, Julia Pongratz, Pierre Regnier, Marko Scholze, Yusuf Serengil, Pete Smith, Efisio Solazzo, Rona L. Thompson, Francesco N. Tubiello, Timo Vesala, and Sophia Walther
Earth Syst. Sci. Data, 15, 4295–4370, https://doi.org/10.5194/essd-15-4295-2023, https://doi.org/10.5194/essd-15-4295-2023, 2023
Short summary
Short summary
Accurate estimation of fluxes of carbon dioxide from the land surface is essential for understanding future impacts of greenhouse gas emissions on the climate system. A wide variety of methods currently exist to estimate these sources and sinks. We are continuing work to develop annual comparisons of these diverse methods in order to clarify what they all actually calculate and to resolve apparent disagreement, in addition to highlighting opportunities for increased understanding.
Foteini Stavropoulou, Katarina Vinković, Bert Kers, Marcel de Vries, Steven van Heuven, Piotr Korbeń, Martina Schmidt, Julia Wietzel, Pawel Jagoda, Jaroslav M. Necki, Jakub Bartyzel, Hossein Maazallahi, Malika Menoud, Carina van der Veen, Sylvia Walter, Béla Tuzson, Jonas Ravelid, Randulph Paulo Morales, Lukas Emmenegger, Dominik Brunner, Michael Steiner, Arjan Hensen, Ilona Velzeboer, Pim van den Bulk, Hugo Denier van der Gon, Antonio Delre, Maklawe Essonanawe Edjabou, Charlotte Scheutz, Marius Corbu, Sebastian Iancu, Denisa Moaca, Alin Scarlat, Alexandru Tudor, Ioana Vizireanu, Andreea Calcan, Magdalena Ardelean, Sorin Ghemulet, Alexandru Pana, Aurel Constantinescu, Lucian Cusa, Alexandru Nica, Calin Baciu, Cristian Pop, Andrei Radovici, Alexandru Mereuta, Horatiu Stefanie, Alexandru Dandocsi, Bas Hermans, Stefan Schwietzke, Daniel Zavala-Araiza, Huilin Chen, and Thomas Röckmann
Atmos. Chem. Phys., 23, 10399–10412, https://doi.org/10.5194/acp-23-10399-2023, https://doi.org/10.5194/acp-23-10399-2023, 2023
Short summary
Short summary
In this study, we quantify CH4 emissions from onshore oil production sites in Romania at source and facility level using a combination of ground- and drone-based measurement techniques. We show that the total CH4 emissions in our studied areas are much higher than the emissions reported to UNFCCC, and up to three-quarters of the detected emissions are related to operational venting. Our results suggest that oil and gas production infrastructure in Romania holds a massive mitigation potential.
Alison L. Redington, Alistair J. Manning, Stephan Henne, Francesco Graziosi, Luke M. Western, Jgor Arduini, Anita L. Ganesan, Christina M. Harth, Michela Maione, Jens Mühle, Simon O'Doherty, Joseph Pitt, Stefan Reimann, Matthew Rigby, Peter K. Salameh, Peter G. Simmonds, T. Gerard Spain, Kieran Stanley, Martin K. Vollmer, Ray F. Weiss, and Dickon Young
Atmos. Chem. Phys., 23, 7383–7398, https://doi.org/10.5194/acp-23-7383-2023, https://doi.org/10.5194/acp-23-7383-2023, 2023
Short summary
Short summary
Chlorofluorocarbons (CFCs) were used in Europe pre-1990, damaging the stratospheric ozone layer. Legislation has controlled production and use, and global emissions have decreased sharply. The global rate of decline in CFC-11 recently slowed and was partly attributed to illegal emission in eastern China. This study concludes that emissions of CFC-11 in western Europe have not contributed to the unexplained part of the global increase in CFC-11 observed in the last decade.
Sophie Wittig, Antoine Berchet, Isabelle Pison, Marielle Saunois, Joël Thanwerdas, Adrien Martinez, Jean-Daniel Paris, Toshinobu Machida, Motoki Sasakawa, Douglas E. J. Worthy, Xin Lan, Rona L. Thompson, Espen Sollum, and Mikhail Arshinov
Atmos. Chem. Phys., 23, 6457–6485, https://doi.org/10.5194/acp-23-6457-2023, https://doi.org/10.5194/acp-23-6457-2023, 2023
Short summary
Short summary
Here, an inverse modelling approach is applied to estimate CH4 sources and sinks in the Arctic from 2008 to 2019. We study the magnitude, seasonal patterns and trends from different sources during recent years. We also assess how the current observation network helps to constrain fluxes. We find that constraints are only significant for North America and, to a lesser extent, West Siberia, where the observation network is relatively dense. We find no clear trend over the period of inversion.
Ana Maria Roxana Petrescu, Chunjing Qiu, Matthew J. McGrath, Philippe Peylin, Glen P. Peters, Philippe Ciais, Rona L. Thompson, Aki Tsuruta, Dominik Brunner, Matthias Kuhnert, Bradley Matthews, Paul I. Palmer, Oksana Tarasova, Pierre Regnier, Ronny Lauerwald, David Bastviken, Lena Höglund-Isaksson, Wilfried Winiwarter, Giuseppe Etiope, Tuula Aalto, Gianpaolo Balsamo, Vladislav Bastrikov, Antoine Berchet, Patrick Brockmann, Giancarlo Ciotoli, Giulia Conchedda, Monica Crippa, Frank Dentener, Christine D. Groot Zwaaftink, Diego Guizzardi, Dirk Günther, Jean-Matthieu Haussaire, Sander Houweling, Greet Janssens-Maenhout, Massaer Kouyate, Adrian Leip, Antti Leppänen, Emanuele Lugato, Manon Maisonnier, Alistair J. Manning, Tiina Markkanen, Joe McNorton, Marilena Muntean, Gabriel D. Oreggioni, Prabir K. Patra, Lucia Perugini, Isabelle Pison, Maarit T. Raivonen, Marielle Saunois, Arjo J. Segers, Pete Smith, Efisio Solazzo, Hanqin Tian, Francesco N. Tubiello, Timo Vesala, Guido R. van der Werf, Chris Wilson, and Sönke Zaehle
Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, https://doi.org/10.5194/essd-15-1197-2023, 2023
Short summary
Short summary
This study updates the state-of-the-art scientific overview of CH4 and N2O emissions in the EU27 and UK in Petrescu et al. (2021a). Yearly updates are needed to improve the different respective approaches and to inform on the development of formal verification systems. It integrates the most recent emission inventories, process-based model and regional/global inversions, comparing them with UNFCCC national GHG inventories, in support to policy to facilitate real-time verification procedures.
Dominik Brunner, Gerrit Kuhlmann, Stephan Henne, Erik Koene, Bastian Kern, Sebastian Wolff, Christiane Voigt, Patrick Jöckel, Christoph Kiemle, Anke Roiger, Alina Fiehn, Sven Krautwurst, Konstantin Gerilowski, Heinrich Bovensmann, Jakob Borchardt, Michal Galkowski, Christoph Gerbig, Julia Marshall, Andrzej Klonecki, Pascal Prunet, Robert Hanfland, Margit Pattantyús-Ábrahám, Andrzej Wyszogrodzki, and Andreas Fix
Atmos. Chem. Phys., 23, 2699–2728, https://doi.org/10.5194/acp-23-2699-2023, https://doi.org/10.5194/acp-23-2699-2023, 2023
Short summary
Short summary
We evaluated six atmospheric transport models for their capability to simulate the CO2 plumes from two of the largest power plants in Europe by comparing the models against aircraft observations collected during the CoMet (Carbon Dioxide and Methane Mission) campaign in 2018. The study analyzed how realistically such plumes can be simulated at different model resolutions and how well the planned European satellite mission CO2M will be able to quantify emissions from power plants.
Clément Narbaud, Jean-Daniel Paris, Sophie Wittig, Antoine Berchet, Marielle Saunois, Philippe Nédélec, Boris D. Belan, Mikhail Y. Arshinov, Sergei B. Belan, Denis Davydov, Alexander Fofonov, and Artem Kozlov
Atmos. Chem. Phys., 23, 2293–2314, https://doi.org/10.5194/acp-23-2293-2023, https://doi.org/10.5194/acp-23-2293-2023, 2023
Short summary
Short summary
We measured CH4 and CO2 from aircraft over the Russian Arctic. Analyzing our data with the Lagrangian model FLEXPART, we find a sharp east–west gradient in atmospheric composition. Western Siberia is influenced by strong wetland CH4 emissions, deep CO2 gradient from biospheric uptake, and long-range transport from Europe and North America. Eastern flights document less variability. Over the Arctic Ocean, we find a small influence from marine CH4 emissions compatible with reasonable inventories.
Yao Gao, Eleanor J. Burke, Sarah E. Chadburn, Maarit Raivonen, Mika Aurela, Lawrence B. Flanagan, Krzysztof Fortuniak, Elyn Humphreys, Annalea Lohila, Tingting Li, Tiina Markkanen, Olli Nevalainen, Mats B. Nilsson, Włodzimierz Pawlak, Aki Tsuruta, Huiyi Yang, and Tuula Aalto
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-229, https://doi.org/10.5194/bg-2022-229, 2022
Manuscript not accepted for further review
Short summary
Short summary
We coupled a process-based peatland CH4 emission model HIMMELI with a state-of-art land surface model JULES. The performance of the coupled model was evaluated at six northern wetland sites. The coupled model is considered to be more appropriate in simulating wetland CH4 emission. In order to improve the simulated CH4 emission, the model requires better representation of the peat soil carbon and hydrologic processes in JULES and the methane production and transportation processes in HIMMELI.
Joël Thanwerdas, Marielle Saunois, Isabelle Pison, Didier Hauglustaine, Antoine Berchet, Bianca Baier, Colm Sweeney, and Philippe Bousquet
Atmos. Chem. Phys., 22, 15489–15508, https://doi.org/10.5194/acp-22-15489-2022, https://doi.org/10.5194/acp-22-15489-2022, 2022
Short summary
Short summary
Atmospheric methane (CH4) concentrations have been rising since 2007, resulting from an imbalance between CH4 sources and sinks. The CH4 budget is generally estimated through top-down approaches using CH4 and δ13C(CH4) observations as constraints. The oxidation by chlorine (Cl) contributes little to the total oxidation of CH4 but strongly influences δ13C(CH4). Here, we compare multiple recent Cl fields and quantify the influence of Cl concentrations on CH4, δ13C(CH4), and CH4 budget estimates.
Prabhakar Shrestha, Jana Mendrok, and Dominik Brunner
Atmos. Chem. Phys., 22, 14095–14117, https://doi.org/10.5194/acp-22-14095-2022, https://doi.org/10.5194/acp-22-14095-2022, 2022
Short summary
Short summary
The study extends the Terrestrial Systems Modeling Platform with gas-phase chemistry aerosol dynamics and a radar forward operator to enable detailed studies of aerosol–cloud–precipitation interactions. This is demonstrated using a case study of a deep convective storm, which showed that the strong updraft in the convective core of the storm produced aerosol-tower-like features, which affected the size of the hydrometeors and the simulated polarimetric features (e.g., ZDR and KDP columns).
Peter Bergamaschi, Arjo Segers, Dominik Brunner, Jean-Matthieu Haussaire, Stephan Henne, Michel Ramonet, Tim Arnold, Tobias Biermann, Huilin Chen, Sebastien Conil, Marc Delmotte, Grant Forster, Arnoud Frumau, Dagmar Kubistin, Xin Lan, Markus Leuenberger, Matthias Lindauer, Morgan Lopez, Giovanni Manca, Jennifer Müller-Williams, Simon O'Doherty, Bert Scheeren, Martin Steinbacher, Pamela Trisolino, Gabriela Vítková, and Camille Yver Kwok
Atmos. Chem. Phys., 22, 13243–13268, https://doi.org/10.5194/acp-22-13243-2022, https://doi.org/10.5194/acp-22-13243-2022, 2022
Short summary
Short summary
We present a novel high-resolution inverse modelling system, "FLEXVAR", and its application for the inverse modelling of European CH4 emissions in 2018. The new system combines a high spatial resolution of 7 km x 7 km with a variational data assimilation technique, which allows CH4 emissions to be optimized from individual model grid cells. The high resolution allows the observations to be better reproduced, while the derived emissions show overall good consistency with two existing models.
Elise Potier, Grégoire Broquet, Yilong Wang, Diego Santaren, Antoine Berchet, Isabelle Pison, Julia Marshall, Philippe Ciais, François-Marie Bréon, and Frédéric Chevallier
Atmos. Meas. Tech., 15, 5261–5288, https://doi.org/10.5194/amt-15-5261-2022, https://doi.org/10.5194/amt-15-5261-2022, 2022
Short summary
Short summary
Atmospheric inversion at local–regional scales over Europe and pseudo-data assimilation are used to evaluate how CO2 and 14CO2 ground-based measurement networks could complement satellite CO2 imagers to monitor fossil fuel (FF) CO2 emissions. This combination significantly improves precision in the FF emission estimates in areas with a dense network but does not strongly support the separation of the FF from the biogenic signals or the spatio-temporal extrapolation of the satellite information.
Simone M. Pieber, Béla Tuzson, Stephan Henne, Ute Karstens, Christoph Gerbig, Frank-Thomas Koch, Dominik Brunner, Martin Steinbacher, and Lukas Emmenegger
Atmos. Chem. Phys., 22, 10721–10749, https://doi.org/10.5194/acp-22-10721-2022, https://doi.org/10.5194/acp-22-10721-2022, 2022
Short summary
Short summary
Understanding regional greenhouse gas emissions into the atmosphere is a prerequisite to mitigate climate change. In this study, we investigated the regional contributions of carbon dioxide (CO2) at the location of the high Alpine observatory Jungfraujoch (JFJ, Switzerland, 3580 m a.s.l.). To this purpose, we combined receptor-oriented atmospheric transport simulations for CO2 concentration in the period 2009–2017 with stable carbon isotope (δ13C–CO2) information.
Luke M. Western, Alison L. Redington, Alistair J. Manning, Cathy M. Trudinger, Lei Hu, Stephan Henne, Xuekun Fang, Lambert J. M. Kuijpers, Christina Theodoridi, David S. Godwin, Jgor Arduini, Bronwyn Dunse, Andreas Engel, Paul J. Fraser, Christina M. Harth, Paul B. Krummel, Michela Maione, Jens Mühle, Simon O'Doherty, Hyeri Park, Sunyoung Park, Stefan Reimann, Peter K. Salameh, Daniel Say, Roland Schmidt, Tanja Schuck, Carolina Siso, Kieran M. Stanley, Isaac Vimont, Martin K. Vollmer, Dickon Young, Ronald G. Prinn, Ray F. Weiss, Stephen A. Montzka, and Matthew Rigby
Atmos. Chem. Phys., 22, 9601–9616, https://doi.org/10.5194/acp-22-9601-2022, https://doi.org/10.5194/acp-22-9601-2022, 2022
Short summary
Short summary
The production of ozone-destroying gases is being phased out. Even though production of one of the main ozone-depleting gases, called HCFC-141b, has been declining for many years, the amount that is being released to the atmosphere has been increasing since 2017. We do not know for sure why this is. A possible explanation is that HCFC-141b that was used to make insulating foams many years ago is only now escaping to the atmosphere, or a large part of its production is not being reported.
Joël Thanwerdas, Marielle Saunois, Antoine Berchet, Isabelle Pison, Bruce H. Vaughn, Sylvia Englund Michel, and Philippe Bousquet
Geosci. Model Dev., 15, 4831–4851, https://doi.org/10.5194/gmd-15-4831-2022, https://doi.org/10.5194/gmd-15-4831-2022, 2022
Short summary
Short summary
Estimating CH4 sources by exploiting observations within an inverse modeling framework is a powerful approach. Here, a new system designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. By optimizing both the emissions and associated source signatures of multiple emission categories, this new system can efficiently differentiate the co-located emission categories and provide estimates of CH4 sources that are consistent with isotopic data.
Horim Kim, Michael Müller, Stephan Henne, and Christoph Hüglin
Atmos. Meas. Tech., 15, 2979–2992, https://doi.org/10.5194/amt-15-2979-2022, https://doi.org/10.5194/amt-15-2979-2022, 2022
Short summary
Short summary
In this study, the performance of electrochemical sensors for NO and NO2 for measuring air quality was determined over a longer operating period. The performance of NO sensors remained reliable for more than 18 months. However, the NO2 sensors showed decreasing performance over time. During deployment, we found that the NO2 sensors can distinguish general pollution levels, but they proved unsuitable for accurate measurements due to significant biases.
Randulph Morales, Jonas Ravelid, Katarina Vinkovic, Piotr Korbeń, Béla Tuzson, Lukas Emmenegger, Huilin Chen, Martina Schmidt, Sebastian Humbel, and Dominik Brunner
Atmos. Meas. Tech., 15, 2177–2198, https://doi.org/10.5194/amt-15-2177-2022, https://doi.org/10.5194/amt-15-2177-2022, 2022
Short summary
Short summary
Mapping trace gas emission plumes using in situ measurements from unmanned aerial vehicles (UAVs) is an emerging and attractive possibility to quantify emissions from localized sources. We performed an extensive controlled-release experiment to develop an optimal quantification method and to determine the related uncertainties under various environmental and sampling conditions. Our approach was successful in quantifying local methane sources from drone-based measurements.
Gerrit Kuhlmann, Ka Lok Chan, Sebastian Donner, Ying Zhu, Marc Schwaerzel, Steffen Dörner, Jia Chen, Andreas Hueni, Duc Hai Nguyen, Alexander Damm, Annette Schütt, Florian Dietrich, Dominik Brunner, Cheng Liu, Brigitte Buchmann, Thomas Wagner, and Mark Wenig
Atmos. Meas. Tech., 15, 1609–1629, https://doi.org/10.5194/amt-15-1609-2022, https://doi.org/10.5194/amt-15-1609-2022, 2022
Short summary
Short summary
Nitrogen dioxide (NO2) is an air pollutant whose concentration often exceeds air quality guideline values, especially in urban areas. To map the spatial distribution of NO2 in Munich, we conducted the Munich NO2 Imaging Campaign (MuNIC), where NO2 was measured with stationary, mobile, and airborne in situ and remote sensing instruments. The campaign provides a unique dataset that has been used to compare the different instruments and to study the spatial variability of NO2 and its sources.
Marine Remaud, Frédéric Chevallier, Fabienne Maignan, Sauveur Belviso, Antoine Berchet, Alexandra Parouffe, Camille Abadie, Cédric Bacour, Sinikka Lennartz, and Philippe Peylin
Atmos. Chem. Phys., 22, 2525–2552, https://doi.org/10.5194/acp-22-2525-2022, https://doi.org/10.5194/acp-22-2525-2022, 2022
Short summary
Short summary
Carbonyl sulfide (COS) has been recognized as a promising indicator of the plant gross primary production (GPP). Here, we assimilate both COS and CO2 measurements into an atmospheric transport model to obtain information on GPP, plant respiration and COS budget. A possible scenario for the period 2008–2019 leads to a global COS biospheric sink of 800 GgS yr−1 and higher oceanic emissions between 400 and 600 GgS yr−1.
Dominique Rust, Ioannis Katharopoulos, Martin K. Vollmer, Stephan Henne, Simon O'Doherty, Daniel Say, Lukas Emmenegger, Renato Zenobi, and Stefan Reimann
Atmos. Chem. Phys., 22, 2447–2466, https://doi.org/10.5194/acp-22-2447-2022, https://doi.org/10.5194/acp-22-2447-2022, 2022
Short summary
Short summary
Artificial halocarbons contribute to ozone layer depletion and to global warming. We measured the atmospheric concentrations of halocarbons at the Beromünster tower, modelled the Swiss emissions, and compared the results to the internationally reported Swiss emissions inventory. For most of the halocarbons, we found good agreement, whereas one refrigerant might be overestimated in the inventory. In addition, we present first emission estimates of the newest types of halocarbons.
Cyril Brunner, Benjamin T. Brem, Martine Collaud Coen, Franz Conen, Maxime Hervo, Stephan Henne, Martin Steinbacher, Martin Gysel-Beer, and Zamin A. Kanji
Atmos. Chem. Phys., 21, 18029–18053, https://doi.org/10.5194/acp-21-18029-2021, https://doi.org/10.5194/acp-21-18029-2021, 2021
Short summary
Short summary
Special microscopic particles called ice-nucleating particles (INPs) are essential for ice crystals to form in the atmosphere. INPs are sparse and their atmospheric concentration and properties are not well understood. Mineral dust particles make up a significant fraction of INPs but how much remains unknown. Here, we address this knowledge gap by studying periods when mineral particles are present in large quantities at a mountaintop station in central Europe.
Larissa Lacher, Hans-Christian Clemen, Xiaoli Shen, Stephan Mertes, Martin Gysel-Beer, Alireza Moallemi, Martin Steinbacher, Stephan Henne, Harald Saathoff, Ottmar Möhler, Kristina Höhler, Thea Schiebel, Daniel Weber, Jann Schrod, Johannes Schneider, and Zamin A. Kanji
Atmos. Chem. Phys., 21, 16925–16953, https://doi.org/10.5194/acp-21-16925-2021, https://doi.org/10.5194/acp-21-16925-2021, 2021
Short summary
Short summary
We investigate ice-nucleating particle properties at Jungfraujoch during the 2017 joint INUIT/CLACE field campaign, to improve the knowledge about those rare particles in a cloud-relevant environment. By quantifying ice-nucleating particles in parallel to single-particle mass spectrometry measurements, we find that mineral dust and aged sea spray particles are potential candidates for ice-nucleating particles. Our findings are supported by ice residual analysis and source region modeling.
Vilma Kangasaho, Aki Tsuruta, Leif Backman, Pyry Mäkinen, Sander Houweling, Arjo Segers, Maarten Krol, Ed Dlugokencky, Sylvia Michel, James White, and Tuula Aalto
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-843, https://doi.org/10.5194/acp-2021-843, 2021
Revised manuscript not accepted
Short summary
Short summary
Understanding the composition of carbon isotopes can help to better understand the changes in methane budgets. This study investigates how methane sources affect the seasonal cycle of the methane carbon-13 isotope during 2000–2012 using an atmospheric transport model. We found that emissions from both anthropogenic and natural sources contribute. The findings raise a need to revise the magnitudes, proportion, and seasonal cycles of anthropogenic sources and northern wetland emissions.
Marc Schwaerzel, Dominik Brunner, Fabian Jakub, Claudia Emde, Brigitte Buchmann, Alexis Berne, and Gerrit Kuhlmann
Atmos. Meas. Tech., 14, 6469–6482, https://doi.org/10.5194/amt-14-6469-2021, https://doi.org/10.5194/amt-14-6469-2021, 2021
Short summary
Short summary
NO2 maps from airborne imaging remote sensing often appear much smoother than one would expect from high-resolution model simulations of NO2 over cities, despite the small ground-pixel size of the sensors. Our case study over Zurich, using the newly implemented building module of the MYSTIC radiative transfer solver, shows that the 3D effect can explain part of the smearing and that building shadows cause a noticeable underestimation and noise in the measured NO2 columns.
Antoine Berchet, Espen Sollum, Rona L. Thompson, Isabelle Pison, Joël Thanwerdas, Grégoire Broquet, Frédéric Chevallier, Tuula Aalto, Adrien Berchet, Peter Bergamaschi, Dominik Brunner, Richard Engelen, Audrey Fortems-Cheiney, Christoph Gerbig, Christine D. Groot Zwaaftink, Jean-Matthieu Haussaire, Stephan Henne, Sander Houweling, Ute Karstens, Werner L. Kutsch, Ingrid T. Luijkx, Guillaume Monteil, Paul I. Palmer, Jacob C. A. van Peet, Wouter Peters, Philippe Peylin, Elise Potier, Christian Rödenbeck, Marielle Saunois, Marko Scholze, Aki Tsuruta, and Yuanhong Zhao
Geosci. Model Dev., 14, 5331–5354, https://doi.org/10.5194/gmd-14-5331-2021, https://doi.org/10.5194/gmd-14-5331-2021, 2021
Short summary
Short summary
We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is a programming protocol to allow various inversion bricks to be exchanged among researchers.
The ensemble of bricks makes a flexible, transparent and open-source Python-based tool. We describe the main structure and functionalities and demonstrate it in a simple academic case.
Jean-Daniel Paris, Aurélie Riandet, Efstratios Bourtsoukidis, Marc Delmotte, Antoine Berchet, Jonathan Williams, Lisa Ernle, Ivan Tadic, Hartwig Harder, and Jos Lelieveld
Atmos. Chem. Phys., 21, 12443–12462, https://doi.org/10.5194/acp-21-12443-2021, https://doi.org/10.5194/acp-21-12443-2021, 2021
Short summary
Short summary
We measured atmospheric methane and CO2 by ship in the Middle East. We probe the origin of methane with a combination of light alkane measurements and modeling. We find strong influence from nearby oil and gas production over the Arabian Gulf. Comparing our data to inventories indicates that inventories overestimate sources from the upstream gas industry but underestimate emissions from oil extraction and processing. The Red Sea was under a complex mixture of sources due to human activity.
Michael Weimer, Jennifer Buchmüller, Lars Hoffmann, Ole Kirner, Beiping Luo, Roland Ruhnke, Michael Steiner, Ines Tritscher, and Peter Braesicke
Atmos. Chem. Phys., 21, 9515–9543, https://doi.org/10.5194/acp-21-9515-2021, https://doi.org/10.5194/acp-21-9515-2021, 2021
Short summary
Short summary
We show that we are able to directly simulate polar stratospheric clouds formed locally in a mountain wave and represent their effect on the ozone chemistry with the global atmospheric chemistry model ICON-ART. Thus, we show the first simulations that close the gap between directly resolved mountain-wave-induced polar stratospheric clouds and their representation at coarse global resolutions.
Dac-Loc Nguyen, Hendryk Czech, Simone M. Pieber, Jürgen Schnelle-Kreis, Martin Steinbacher, Jürgen Orasche, Stephan Henne, Olga B. Popovicheva, Gülcin Abbaszade, Guenter Engling, Nicolas Bukowiecki, Nhat-Anh Nguyen, Xuan-Anh Nguyen, and Ralf Zimmermann
Atmos. Chem. Phys., 21, 8293–8312, https://doi.org/10.5194/acp-21-8293-2021, https://doi.org/10.5194/acp-21-8293-2021, 2021
Short summary
Short summary
Southeast Asia is well-known for emission-intense and recurring wildfires and after-harvest crop residue burning during the pre-monsoon season from February to April. We describe a biomass burning (BB) plume arriving at remote Pha Din meteorological station, outline its carbonaceous particulate matter (PM) constituents based on more than 50 target compounds and discuss possible BB sources. This study adds valuable information on chemical PM composition for a region with scarce data availability.
Ana Maria Roxana Petrescu, Chunjing Qiu, Philippe Ciais, Rona L. Thompson, Philippe Peylin, Matthew J. McGrath, Efisio Solazzo, Greet Janssens-Maenhout, Francesco N. Tubiello, Peter Bergamaschi, Dominik Brunner, Glen P. Peters, Lena Höglund-Isaksson, Pierre Regnier, Ronny Lauerwald, David Bastviken, Aki Tsuruta, Wilfried Winiwarter, Prabir K. Patra, Matthias Kuhnert, Gabriel D. Oreggioni, Monica Crippa, Marielle Saunois, Lucia Perugini, Tiina Markkanen, Tuula Aalto, Christine D. Groot Zwaaftink, Hanqin Tian, Yuanzhi Yao, Chris Wilson, Giulia Conchedda, Dirk Günther, Adrian Leip, Pete Smith, Jean-Matthieu Haussaire, Antti Leppänen, Alistair J. Manning, Joe McNorton, Patrick Brockmann, and Albertus Johannes Dolman
Earth Syst. Sci. Data, 13, 2307–2362, https://doi.org/10.5194/essd-13-2307-2021, https://doi.org/10.5194/essd-13-2307-2021, 2021
Short summary
Short summary
This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up and top-down CH4 and N2O emissions in the EU27 and UK. The data integrate recent emission inventories with process-based model data and regional/global inversions for the European domain, aiming at reconciling them with official country-level UNFCCC national GHG inventories in support to policy and to facilitate real-time verification procedures.
Audrey Fortems-Cheiney, Isabelle Pison, Grégoire Broquet, Gaëlle Dufour, Antoine Berchet, Elise Potier, Adriana Coman, Guillaume Siour, and Lorenzo Costantino
Geosci. Model Dev., 14, 2939–2957, https://doi.org/10.5194/gmd-14-2939-2021, https://doi.org/10.5194/gmd-14-2939-2021, 2021
Short summary
Short summary
Up-to-date and accurate emission inventories for air pollutants are essential for understanding their role in the formation of tropospheric ozone and particulate matter, for anticipating pollution peaks and for identifying the key drivers that could help mitigate their emissions. Complementarily with bottom-up inventories, the system described here aims at updating and improving the knowledge on the high spatiotemporal variability of emissions of air pollutants.
Michael Steiner, Beiping Luo, Thomas Peter, Michael C. Pitts, and Andrea Stenke
Geosci. Model Dev., 14, 935–959, https://doi.org/10.5194/gmd-14-935-2021, https://doi.org/10.5194/gmd-14-935-2021, 2021
Short summary
Short summary
We evaluate polar stratospheric clouds (PSCs) as simulated by the chemistry–climate model (CCM) SOCOLv3.1 in comparison with measurements by the CALIPSO satellite. A cold bias results in an overestimated PSC area and mountain-wave ice is underestimated, but we find overall good temporal and spatial agreement of PSC occurrence and composition. This work confirms previous studies indicating that simplified PSC schemes may also achieve good approximations of the fundamental properties of PSCs.
Gerrit Kuhlmann, Dominik Brunner, Grégoire Broquet, and Yasjka Meijer
Atmos. Meas. Tech., 13, 6733–6754, https://doi.org/10.5194/amt-13-6733-2020, https://doi.org/10.5194/amt-13-6733-2020, 2020
Short summary
Short summary
The European CO2M mission is a proposed constellation of CO2 imaging satellites expected to monitor CO2 emissions of large cities. Using synthetic observations, we show that a constellation of two or more satellites should be able to quantify Berlin's annual emissions with 10–20 % accuracy, even when considering atmospheric transport model errors. We therefore expect that CO2M will make an important contribution to the monitoring and verification of CO2 emissions from cities worldwide.
Tea Thum, Julia E. M. S. Nabel, Aki Tsuruta, Tuula Aalto, Edward J. Dlugokencky, Jari Liski, Ingrid T. Luijkx, Tiina Markkanen, Julia Pongratz, Yukio Yoshida, and Sönke Zaehle
Biogeosciences, 17, 5721–5743, https://doi.org/10.5194/bg-17-5721-2020, https://doi.org/10.5194/bg-17-5721-2020, 2020
Short summary
Short summary
Global vegetation models are important tools in estimating the impacts of global climate change. The fate of soil carbon is of the upmost importance as its emissions will enhance the atmospheric carbon dioxide concentration. To evaluate the skill of global vegetation models to model the soil carbon and its responses to environmental factors, it is important to use different data sources. We evaluated two different soil carbon models by using atmospheric carbon dioxide concentrations.
Ying Zhu, Jia Chen, Xiao Bi, Gerrit Kuhlmann, Ka Lok Chan, Florian Dietrich, Dominik Brunner, Sheng Ye, and Mark Wenig
Atmos. Chem. Phys., 20, 13241–13251, https://doi.org/10.5194/acp-20-13241-2020, https://doi.org/10.5194/acp-20-13241-2020, 2020
Short summary
Short summary
Average NO2 concentration of on-street mobile measurements (MMs) near the monitoring stations (MSs) was found to be considerably higher than the MSs data. The common measurement height (H) and distance (D) of the MSs result in 27 % lower average concentrations in total than the concentration of our MMs. Another 21 % difference remained after correcting the influence of the measuring H and D. This result makes our city-wide measurements for capturing the full range of concentrations necessary.
Rachel L. Tunnicliffe, Anita L. Ganesan, Robert J. Parker, Hartmut Boesch, Nicola Gedney, Benjamin Poulter, Zhen Zhang, Jošt V. Lavrič, David Walter, Matthew Rigby, Stephan Henne, Dickon Young, and Simon O'Doherty
Atmos. Chem. Phys., 20, 13041–13067, https://doi.org/10.5194/acp-20-13041-2020, https://doi.org/10.5194/acp-20-13041-2020, 2020
Short summary
Short summary
This study quantifies Brazil’s emissions of a potent atmospheric greenhouse gas, methane. This is in the field of atmospheric modelling and uses remotely sensed data and surface measurements of methane concentrations as well as an atmospheric transport model to interpret the data. Because of Brazil’s large emissions from wetlands, agriculture and biomass burning, these emissions affect global methane concentrations and thus are of global significance.
Yuanhong Zhao, Marielle Saunois, Philippe Bousquet, Xin Lin, Antoine Berchet, Michaela I. Hegglin, Josep G. Canadell, Robert B. Jackson, Makoto Deushi, Patrick Jöckel, Douglas Kinnison, Ole Kirner, Sarah Strode, Simone Tilmes, Edward J. Dlugokencky, and Bo Zheng
Atmos. Chem. Phys., 20, 13011–13022, https://doi.org/10.5194/acp-20-13011-2020, https://doi.org/10.5194/acp-20-13011-2020, 2020
Short summary
Short summary
Decadal trends and variations in OH are critical for understanding atmospheric CH4 evolution. We quantify the impacts of OH trends and variations on the CH4 budget by conducting CH4 inversions on a decadal scale with an ensemble of OH fields. We find the negative OH anomalies due to enhanced fires can reduce the optimized CH4 emissions by up to 10 Tg yr−1 during El Niño years and the positive OH trend from 1986 to 2010 results in a ∼ 23 Tg yr−1 additional increase in optimized CH4 emissions.
Cited articles
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001. a
Berchet, A., Sollum, E., Thompson, R. L., Pison, I., Thanwerdas, J., Broquet, G., Chevallier, F., Aalto, T., Berchet, A., Bergamaschi, P., Brunner, D., Engelen, R., Fortems-Cheiney, A., Gerbig, C., Groot Zwaaftink, C. D., Haussaire, J.-M., Henne, S., Houweling, S., Karstens, U., Kutsch, W. L., Luijkx, I. T., Monteil, G., Palmer, P. I., van Peet, J. C. A., Peters, W., Peylin, P., Potier, E., Rödenbeck, C., Saunois, M., Scholze, M., Tsuruta, A., and Zhao, Y.: The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies, Geosci. Model Dev., 14, 5331–5354, https://doi.org/10.5194/gmd-14-5331-2021, 2021. a
Berchet, A., Sollum, E., Pison, I., Thompson, R. L., Thanwerdas, J., Fortems-Cheiney, A., Peet, J. C. A. V., Potier, E., Chevallier, F., Broquet, G., and Berchet, A.: The Community Inversion Framework: codes and documentation, Zenodo [code], https://doi.org/10.5281/zenodo.12742377, 2022. a
Berchet, A., Thanwerdas, J., Reum, F., Elias, E., and Broquet, G.: D5.3 Quantification of transport errors and database of optimized fluxes and simulations for an ensemble of models and inversion set-up|CoCO2: Prototype system for a Copernicus CO2 service, https://coco2-project.eu/sites/default/files/2023-11/CoCO2-D5-3-V0.1.pdf (last access: 15 January 2025), 2023. a, b, c, d
Berchet, A., Pison, I., Thanwerdas, J., Reum, F., Elias, E., Chevallier, F., Fortems-Cheiney, A., Peylin, P., Bastrikov, V., Saunois, M., Tsuruta, A., and Martinez, A.: Input data for running forward simulations of CO2 atmospheric concentrations over Europe for the year 2019, Zenodo [data set], https://doi.org/10.5281/zenodo.12609041, 2024. a
Bergamaschi, P., Krol, M., Meirink, J. F., Dentener, F., Segers, A., van Aardenne, J., Monni, S., Vermeulen, A. T., Schmidt, M., Ramonet, M., Yver, C., Meinhardt, F., Nisbet, E. G., Fisher, R. E., O'Doherty, S., and Dlugokencky, E. J.: Inverse modeling of European CH4 emissions 2001–2006, J. Geophys. Res., 115, D22309 https://doi.org/10.1029/2010JD014180, 2010. a
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects, Mon. Weather Rev., 129, 420–436, https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2, 2001. a
Bousquet, P., Ringeval, B., Pison, I., Dlugokencky, E. J., Brunke, E.-G., Carouge, C., Chevallier, F., Fortems-Cheiney, A., Frankenberg, C., Hauglustaine, D. A., Krummel, P. B., Langenfelds, R. L., Ramonet, M., Schmidt, M., Steele, L. P., Szopa, S., Yver, C., Viovy, N., and Ciais, P.: Source attribution of the changes in atmospheric methane for 2006–2008, Atmos. Chem. Phys., 11, 3689–3700, https://doi.org/10.5194/acp-11-3689-2011, 2011. a
Brasseur, G. P. and Jacob, D. J.: Modeling of Atmospheric Chemistry, Cambridge University Press, Cambridge, ISBN 978-1-107-14696-9, https://doi.org/10.1017/9781316544754, 2017. a
Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M., and Bladé, I.: The Effective Number of Spatial Degrees of Freedom of a Time-Varying Field, J. Climate, 12, 1990–2009, https://doi.org/10.1175/1520-0442(1999)012<1990:TENOSD>2.0.CO;2, 1999. a
Bruhwiler, L., Dlugokencky, E., Masarie, K., Ishizawa, M., Andrews, A., Miller, J., Sweeney, C., Tans, P., and Worthy, D.: CarbonTracker-CH4: an assimilation system for estimating emissions of atmospheric methane, Atmos. Chem. Phys., 14, 8269–8293, https://doi.org/10.5194/acp-14-8269-2014, 2014. a
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. a, b
Brunner, D., Henne, S., Keller, C. A., Reimann, S., Vollmer, M. K., O'Doherty, S., and Maione, M.: An extended Kalman-filter for regional scale inverse emission estimation, Atmos. Chem. Phys., 12, 3455–3478, https://doi.org/10.5194/acp-12-3455-2012, 2012. a, b
Burgers, G., Leeuwen, P. J. V., and Evensen, G.: Analysis Scheme in the Ensemble Kalman Filter, Mon. Weather Rev., 126, 1719–1724, https://doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2, 1998. a
Cardinali, C., Pezzulli, S., and Andersson, E.: Influence-matrix diagnostic of a data assimilation system, Q. J. Roy. Meteorol. Soc., 130, 2767–2786, https://doi.org/10.1256/qj.03.205, 2004. a
Chatterjee, A., Michalak, A. M., Anderson, J. L., Mueller, K. L., and Yadav, V.: Toward reliable ensemble Kalman filter estimates of CO2 fluxes, J. Geophys. Res., 117, D22306, https://doi.org/10.1029/2012JD018176, 2012. a
Chevallier, F.: Impact of correlated observation errors on inverted CO2 surface fluxes from OCO measurements, Geophys. Res. Lett., 34, L24804, https://doi.org/10.1029/2007GL030463, 2007. a
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., 110, D24309, https://doi.org/10.1029/2005JD006390, 2005. a
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, 2010. a, b
Cunnold, D. M., Prinn, R. G., Rasmussen, R. A., Simmonds, P. G., Alyea, F. N., Cardelino, C. A., Crawford, A. J., Fraser, P. J., and Rosen, R. D.: The Atmospheric Lifetime Experiment: 3. Lifetime methodology and application to three years of CFCl3 data, J. Geophys. Res.-Oceans, 88, 8379–8400, https://doi.org/10.1029/JC088iC13p08379, 1983. a
Desroziers, G. and Ivanov, S.: Diagnosis and adaptive tuning of observation-error parameters in a variational assimilation, Q. J. Roy. Meteorol. Soc., 127, 1433–1452, https://doi.org/10.1002/qj.49712757417, 2001. a, b
Dlugokencky, E. J., Steele, L. P., Lang, P., and Masarie, K. A.: The growth rate and distribution of atmospheric methane, J. Geophys. Res.-Atmos., 99, 17021–17043, https://doi.org/10.1029/94JD01245, 1994. a
Enting, I. G.: Inverse Problems in Atmospheric Constituent Transport, Cambridge Atmospheric and Space Science Series, Cambridge University Press, Cambridge, ISBN 978-0-521-81210-8, https://doi.org/10.1017/CBO9780511535741, 2002. a
Enting, I. G. and Newsam, G. N.: Atmospheric constituent inversion problems: Implications for baseline monitoring, J. Atmos. Chem., 11, 69–87, https://doi.org/10.1007/BF00053668, 1990a. a
Enting, I. G. and Newsam, G. N.: Inverse problems in atmospheric constituent studies: II. Sources in the free atmosphere, Invers. Probl., 6, 349, https://doi.org/10.1088/0266-5611/6/3/005, 1990b. a
Enting, I. G., Trudinger, C., Franceyand, R., and Granek, H.: Synthesis inversion of atmospheric CO2 using the GISS tracer transport model, no. 29 in CSIRO Division of Atmospheric Research technical paper, CSIRO Division of Atmospheric Research, Melbourne, ISBN 978-0-643-05251-2, 1993. a
Enting, I. G., Trudinger, C. M., and Francey, R. J.: A synthesis inversion of the concentration and δ13C of atmospheric CO2, Tellus B, 47, 35–52, https://doi.org/10.1034/j.1600-0889.47.issue1.5.x, 1995. a
Evensen, G.: Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model, J. Geophys. Res.-Oceans, 97, 17905–17924, https://doi.org/10.1029/92JC01972, 1992. a
Evensen, G.: Open boundary conditions for the extended Kalman filter with a quasi-geostrophic ocean model, J. Geophys. Res.-Oceans, 98, 16529–16546, https://doi.org/10.1029/93JC01365, 1993. a
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res.-Oceans, 99, 10143–10162, https://doi.org/10.1029/94JC00572, 1994. a
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. a
Fortems-Cheiney, A., Pison, I., Broquet, G., Dufour, G., Berchet, A., Potier, E., Coman, A., Siour, G., and Costantino, L.: Variational regional inverse modeling of reactive species emissions with PYVAR-CHIMERE-v2019, Geosci. Model Dev., 14, 2939–2957, https://doi.org/10.5194/gmd-14-2939-2021, 2021. a
Fortems-Cheiney, A., Broquet, G., Potier, E., Plauchu, R., Berchet, A., Pison, I., Denier van der Gon, H., and Dellaert, S.: CO anthropogenic emissions in Europe from 2011 to 2021: insights from Measurement of Pollution in the Troposphere (MOPITT) satellite data, Atmos. Chem. Phys., 24, 4635–4649, https://doi.org/10.5194/acp-24-4635-2024, 2024. a
Fraedrich, K., Ziehmann, C., and Sielmann, F.: Estimates of Spatial Degrees of Freedom, J. Climate, 8, 361–369, https://doi.org/10.1175/1520-0442(1995)008<0361:EOSDOF>2.0.CO;2, 1995. a
Frankenberg, C., Meirink, J. F., Bergamaschi, P., Goede, A. P. H., Heimann, M., Körner, S., Platt, U., van Weele, M., and Wagner, T.: Satellite chartography of atmospheric methane from SCIAMACHY on board ENVISAT: Analysis of the years 2003 and 2004, J. Geophys. Res., 111, D07303, https://doi.org/10.1029/2005JD006235, 2006. a
Ganesan, A. L., Rigby, M., Zammit-Mangion, A., Manning, A. J., Prinn, R. G., Fraser, P. J., Harth, C. M., Kim, K.-R., Krummel, P. B., Li, S., Mühle, J., O'Doherty, S. J., Park, S., Salameh, P. K., Steele, L. P., and Weiss, R. F.: Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods, Atmos. Chem. Phys., 14, 3855–3864, https://doi.org/10.5194/acp-14-3855-2014, 2014. a, b
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteorol. Soc., 125, 723–757, https://doi.org/10.1002/qj.49712555417, 1999. a
Gelb, A.: Applied Optimal Estimation, MIT Press, https://mitpress.mit.edu/9780262570480/applied-optimal-estimation/ (last access: 15 January 2025), 1974. a
Ghil, M. and Malanotte-Rizzoli, P.: Data Assimilation in Meteorology and Oceanography, in: Advances in Geophysics, vol. 33, edited by: Dmowska, R. and Saltzman, B., Elsevier, 41–266, https://doi.org/10.1016/S0065-2687(08)60442-2, 1991. a
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005. a
Gulev, S. K., Thorne, P. W., Ahn, J., Dentener, F. J., Domingues, C. M., Gerland, S., Gong, D., Kaufman, D. S., Nnamchi, H. C., Quaas, J., Rivera, J. A., Sathyendranath, S., Smith, S. L., Trewin, B., von Shuckmann, K., and Vose, R. S.: Changing State of the Climate System, 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, UK and New York, NY, USA, 287–422, https://doi.org/10.1017/9781009157896.004, 2021. a
Haas-Laursen, D. E., Hartley, D. E., and Prinn, R. G.: Optimizing an inverse method to deduce time-varying emissions of trace gases, J. Geophys. Res.-Atmos., 101, 22823–22831, https://doi.org/10.1029/96JD02018, 1996. a
Hamill, T. M., Whitaker, J. S., and Snyder, C.: Distance-Dependent Filtering of Background Error Covariance Estimates in an Ensemble Kalman Filter, Mon. Weather Rev., 129, 2776–2790, https://doi.org/10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2, 2001. a
Hartley, D. and Prinn, R.: Feasibility of determining surface emissions of trace gases using an inverse method in a three-dimensional chemical transport model, J. Geophys. Res.-Atmos., 98, 5183–5197, https://doi.org/10.1029/92JD02594, 1993. a
He, W., van der Velde, I. R., Andrews, A. E., Sweeney, C., Miller, J., Tans, P., van der Laan-Luijkx, I. T., Nehrkorn, T., Mountain, M., Ju, W., Peters, W., and Chen, H.: CTDAS-Lagrange v1.0: a high-resolution data assimilation system for regional carbon dioxide observations, Geosci. Model Dev., 11, 3515–3536, https://doi.org/10.5194/gmd-11-3515-2018, 2018. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thëpaut, J.-N.: Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service (C3S) Data Store (CDS) [data set], https://doi.org/10.24381/cds.143582cf, 2017. a, b
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a, b
Hotta, D. and Ota, Y.: What limits the number of observations that can be effectively assimilated by EnKF?, Q. J. Roy. Meteorol. Soc., 147, 1258–1277, https://doi.org/10.1002/qj.3970, 2021. a, b, c
Houtekamer, P. L. and Mitchell, H. L.: Data Assimilation Using an Ensemble Kalman Filter Technique, Mon. Weather Rev., 126, 796–811, https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2, 1998. a, b, c
Houtekamer, P. L. and Mitchell, H. L.: A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation, Mon. Weather Rev., 129, 123–137, https://doi.org/10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2, 2001. a
Hu, H., Landgraf, J., Detmers, R., Borsdorff, T., Aan de Brugh, J., Aben, I., Butz, A., and Hasekamp, O.: Toward Global Mapping of Methane With TROPOMI: First Results and Intersatellite Comparison to GOSAT, Geophys. Res. Lett., 45, 3682–3689, https://doi.org/10.1002/2018GL077259, 2018. a
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. a
ICON partnership (DWD and MPI-M and DKRZ and KIT and C2SM): ICON release 2024.01, WDC for Climate [code], https://doi.org/10.35089/WDCC/IconRelease01, 2024. a
ICOS RI, Bergamaschi, P., Colomb, A., De Maziẽre, M., Emmenegger, L., Kubistin, D., Lehner, I., Lehtinen, K., Lund Myhre, C., Marek, M., Platt, S. M., Plaß-Dülmer, C., Schmidt, M., Apadula, F., Arnold, S., Blanc, P.-E., Brunner, D., Chen, H., Chmura, L., Conil, S., Couret, C., Cristofanelli, P., Delmotte, M., Forster, G., Frumau, A., Gheusi, F., Hammer, S., Haszpra, L., Heliasz, M., Henne, S., Hoheisel, A., Kneuer, T., Laurila, T., Leskinen, A., Leuenberger, M., Levin, I., Lindauer, M., Lopez, M., Lunder, C., Mammarella, I., Manca, G., Manning, A., Marklund, P., Martin, D., Meinhardt, F., Müller-Williams, J., Necki, J., O'Doherty, S., Ottosson-Löfvenius, M., Philippon, C., Piacentino, S., Pitt, J., Ramonet, M., Rivas-Soriano, P., Scheeren, B., Schumacher, M., Sha, M. K., Spain, G., Steinbacher, M., Sørensen, L. L., Vermeulen, A., Vítková, G., Xueref-Remy, I., di Sarra, A., Conen, F., Kazan, V., Roulet, Y.-A., Biermann, T., Heltai, D., Hensen, A., Hermansen, O., Komínková, K., Laurent, O., Levula, J., Pichon, J.-M., Smith, P., Stanley, K., Trisolino, P., ICOS Carbon Portal, ICOS Atmosphere Thematic Centre, ICOS Flask And Calibration Laboratory, and ICOS Central Radiocarbon Laboratory: European Obspack compilation of atmospheric carbon dioxide data from ICOS and non-ICOS European stations for the period 1972–2023; obspack_co2_466_GLOBALVIEWplus_v8.0_2023-04-26, ICOS, https://doi.org/10.18160/CEC4-CAGK, 2023. a
Jähn, M., Kuhlmann, G., Mu, Q., Haussaire, J.-M., Ochsner, D., Osterried, K., Clément, V., and Brunner, D.: An online emission module for atmospheric chemistry transport models: implementation in COSMO-GHG v5.6a and COSMO-ART v5.1-3.1, Geosci. Model Dev., 13, 2379–2392, https://doi.org/10.5194/gmd-13-2379-2020, 2020. a
Kalman, R. E.: A New Approach to Linear Filtering and Prediction Problems, J. Basic Eng., 82, 35–45, https://doi.org/10.1115/1.3662552, 1960. a
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. a
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., 117, D24101, https://doi.org/10.1029/2012JD018259, 2012. a
Kim, J., Kim, H. M., and Cho, C.-H.: The effect of optimization and the nesting domain on carbon flux analyses in Asia using a carbon tracking system based on the ensemble Kalman filter, Asia-Pacif. J. Atmos. Sci., 50, 327–344, https://doi.org/10.1007/s13143-014-0020-y, 2014a. a, b
Kim, J., Kim, H. M., and Cho, C.-H.: Influence of CO2 observations on the optimized CO2 flux in an ensemble Kalman filter, Atmos. Chem. Phys., 14, 13515–13530, https://doi.org/10.5194/acp-14-13515-2014, 2014b. a, b, c
Kopacz, M., Jacob, D. J., Henze, D. K., Heald, C. L., Streets, D. G., and Zhang, Q.: Comparison of adjoint and analytical Bayesian inversion methods for constraining Asian sources of carbon monoxide using satellite (MOPITT) measurements of CO columns, J. Geophys. Res., 114, D04305, https://doi.org/10.1029/2007JD009264, 2009. a
Kotsuki, S., Greybush, S. J., and Miyoshi, T.: Can We Optimize the Assimilation Order in the Serial Ensemble Kalman Filter? A Study with the Lorenz-96 Model, Mon. Weather Rev., 145, 4977–4995, https://doi.org/10.1175/MWR-D-17-0094.1, 2017. a, b
Krol, M., Houweling, S., Bregman, B., van den Broek, M., Segers, A., van Velthoven, P., Peters, W., Dentener, F., and Bergamaschi, P.: The two-way nested global chemistry-transport zoom model TM5: algorithm and applications, Atmos. Chem. Phys., 5, 417–432, https://doi.org/10.5194/acp-5-417-2005, 2005. a
Lauvaux, T., Giron, C., Mazzolini, M., d'Aspremont, A., Duren, R., Cusworth, D., Shindell, D., and Ciais, P.: Global assessment of oil and gas methane ultra-emitters, Science, 375, 557–561, https://doi.org/10.1126/science.abj4351, 2022. a
Leeuwen, P. J. V.: Comment on “Data Assimilation Using an Ensemble Kalman Filter Technique”, Mon. Weather Rev., 127, 1374–1377, https://doi.org/10.1175/1520-0493(1999)127<1374:CODAUA>2.0.CO;2, 1999. a
Lin, J. C., Gerbig, C., Wofsy, S. C., Andrews, A. E., Daube, B. C., Davis, K. J., and Grainger, C. A.: A near-field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time-Inverted Lagrangian Transport (STILT) model, J. Geophys. Res., 108, 4493, https://doi.org/10.1029/2002JD003161, 2003. a
Liu, Z., Zeng, N., Liu, Y., Kalnay, E., Asrar, G., Wu, B., Cai, Q., Liu, D., and Han, P.: Improving the joint estimation of CO2 and surface carbon fluxes using a constrained ensemble Kalman filter in COLA (v1.0), Geosci. Model Dev., 15, 5511–5528, https://doi.org/10.5194/gmd-15-5511-2022, 2022. a
Livings, D. M., Dance, S. L., and Nichols, N. K.: Unbiased ensemble square root filters, Physica D, 237, 1021–1028, https://doi.org/10.1016/j.physd.2008.01.005, 2008. a
Lunt, M. F., Rigby, M., Ganesan, A. L., and Manning, A. J.: Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo, Geosci. Model Dev., 9, 3213—3229, https://doi.org/10.5194/gmd-9-3213-2016, 2016. a
Mannisenaho, V., Tsuruta, A., Backman, L., Houweling, S., Segers, A., Krol, M., Saunois, M., Poulter, B., Zhang, Z., Lan, X., Dlugokencky, E. J., Michel, S., White, J. W. C., and Aalto, T.: Global Atmospheric δ13CH4 and CH4 Trends for 2000–2020 from the Atmospheric Transport Model TM5 Using CH4 from Carbon Tracker Europe – CH4 Inversions, Atmosphere, 14, 1121, https://doi.org/10.3390/atmos14071121, 2023. a
Michalak, A. M. and Kitanidis, P. K.: A method for the interpolation of nonnegative functions with an application to contaminant load estimation, Stoch. Environ. Res. Risk A., 19, 8–23, https://doi.org/10.1007/s00477-004-0189-1, 2005. a
Michalak, A. M., Bruhwiler, L., and Tans, P. P.: A geostatistical approach to surface flux estimation of atmospheric trace gases, J. Geophys. Res., 109, D14109, https://doi.org/10.1029/2003JD004422, 2004. a
Miller, S. M., Michalak, A. M., and Levi, P. J.: Atmospheric inverse modeling with known physical bounds: an example from trace gas emissions, Geosci. Model Dev., 7, 303–315, https://doi.org/10.5194/gmd-7-303-2014, 2014. a, b
Mukherjee, C., Kasibhatla, P. S., and West, M.: Bayesian statistical modeling of spatially correlated error structure in atmospheric tracer inverse analysis, Atmos. Chem. Phys., 11, 5365–5382, https://doi.org/10.5194/acp-11-5365-2011, 2011. a
Nerger, L.: On Serial Observation Processing in Localized Ensemble Kalman Filters, Mon. Weather Rev., 143, 1554–1567, https://doi.org/10.1175/MWR-D-14-00182.1, 2015. a, b
Newsam, G. N. and Enting, I. G.: Inverse problems in atmospheric constituent studies. I. Determination of surface sources under a diffusive transport approximation, Invers. Probl., 4, 1037, https://doi.org/10.1088/0266-5611/4/4/008, 1988. a
Parker, R. J., Webb, A., Boesch, H., Somkuti, P., Barrio Guillo, R., Di Noia, A., Kalaitzi, N., Anand, J. S., Bergamaschi, P., Chevallier, F., Palmer, P. I., Feng, L., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Kivi, R., Morino, I., Notholt, J., Oh, Y.-S., Ohyama, H., Petri, C., Pollard, D. F., Roehl, C., Sha, M. K., Shiomi, K., Strong, K., Sussmann, R., Té, Y., Velazco, V. A., Warneke, T., Wennberg, P. O., and Wunch, D.: A decade of GOSAT Proxy satellite CH4 observations, Earth Syst. Sci. Data, 12, 3383–3412, https://doi.org/10.5194/essd-12-3383-2020, 2020. a
Peng, Z., Zhang, M., Kou, X., Tian, X., and Ma, X.: A regional carbon data assimilation system and its preliminary evaluation in East Asia, Atmos. Chem. Phys., 15, 1087–1104, https://doi.org/10.5194/acp-15-1087-2015, 2015. a, b
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. a, b, c, d, e, f, g, h, i, j, k
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. a
Prinn, R. G., Weiss, R. F., Fraser, P. J., Simmonds, P. G., Cunnold, D. M., Alyea, F. N., O'Doherty, S., Salameh, P., Miller, B. R., Huang, J., Wang, R. H. J., Hartley, D. E., Harth, C., Steele, L. P., Sturrock, G., Midgley, P. M., and McCulloch, A.: A history of chemically and radiatively important gases in air deduced from ALE/GAGE/AGAGE, J. Geophys. Res.-Atmos., 105, 17751–17792, https://doi.org/10.1029/2000JD900141, 2000. a
Ramonet, M., Ciais, P., Apadula, F., Bartyzel, J., Bastos, A., Bergamaschi, P., Blanc, P. E., Brunner, D., Caracciolo di Torchiarolo, L., Calzolari, F., Chen, H., Chmura, L., Colomb, A., Conil, S., Cristofanelli, P., Cuevas, E., Curcoll, R., Delmotte, M., di Sarra, A., Emmenegger, L., Forster, G., Frumau, A., Gerbig, C., Gheusi, F., Hammer, S., Haszpra, L., Hatakka, J., Hazan, L., Heliasz, M., Henne, S., Hensen, A., Hermansen, O., Keronen, P., Kivi, R., Komínková, K., Kubistin, D., Laurent, O., Laurila, T., Lavric, J. V., Lehner, I., Lehtinen, K. E. J., Leskinen, A., Leuenberger, M., Levin, I., Lindauer, M., Lopez, M., Myhre, C. L., Mammarella, I., Manca, G., Manning, A., Marek, M. V., Marklund, P., Martin, D., Meinhardt, F., Mihalopoulos, N., Mölder, M., Morgui, J. A., Necki, J., O'Doherty, S., O'Dowd, C., Ottosson, M., Philippon, C., Piacentino, S., Pichon, J. M., Plass-Duelmer, C., Resovsky, A., Rivier, L., Rodó, X., Sha, M. K., Scheeren, H. A., Sferlazzo, D., Spain, T. G., Stanley, K. M., Steinbacher, M., Trisolino, P., Vermeulen, A., Vítková, G., Weyrauch, D., Xueref-Remy, I., Yala, K., and Yver Kwok, C.: The fingerprint of the summer 2018 drought in Europe on ground-based atmospheric CO2 measurements, Philos. T. Roy. Soc. B, 375, 20190513, https://doi.org/10.1098/rstb.2019.0513, 2020. a
Remaud, M., Chevallier, F., Maignan, F., Belviso, S., Berchet, A., Parouffe, A., Abadie, C., Bacour, C., Lennartz, S., and Peylin, P.: Plant gross primary production, plant respiration and carbonyl sulfide emissions over the globe inferred by atmospheric inverse modelling, Atmos. Chem. Phys., 22, 2525–2552, https://doi.org/10.5194/acp-22-2525-2022, 2022. a
Reum, F., Florentie, L., Peters, W., Dogniaux, M., Crevoisier, C., Sic, B., and Houweling, S.: Performance of upcoming CO2 monitoring satellites in the new high-resolution inverse model CTDAS-WRF, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19293, https://doi.org/10.5194/egusphere-egu2020-19293, 2020. a
Rieger, D., Bangert, M., Bischoff-Gauss, I., Förstner, J., Lundgren, K., Reinert, D., Schröter, J., Vogel, H., Zängl, G., Ruhnke, R., and Vogel, B.: ICON–ART 1.0 – a new online-coupled model system from the global to regional scale, Geosci. Model Dev., 8, 1659–1676, https://doi.org/10.5194/gmd-8-1659-2015, 2015. a, b
Rödenbeck, C., Bakker, D. C. E., Metzl, N., Olsen, A., Sabine, C., Cassar, N., Reum, F., Keeling, R. F., and Heimann, M.: Interannual sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme, Biogeosciences, 11, 4599–4613, https://doi.org/10.5194/bg-11-4599-2014, 2014. a
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding: Theory and Practice, in: vol. 2 of Series on Atmospheric, Oceanic and Planetary Physics, World Scientific, ISBN 978-981-02-2740-1, ISBN 978-981-281-371-8, https://doi.org/10.1142/3171, 2000. a, b
Savas, D., Dufour, G., Coman, A., Siour, G., Fortems-Cheiney, A., Broquet, G., Pison, I., Berchet, A., and Bessagnet, B.: Anthropogenic NOx Emission Estimations over East China for 2015 and 2019 Using OMI Satellite Observations and the New Inverse Modeling System CIF-CHIMERE, Atmosphere, 14, 154, https://doi.org/10.3390/atmos14010154, 2023. a
Schröter, J., Rieger, D., Stassen, C., Vogel, H., Weimer, M., Werchner, S. Förstner, J., Prill, F., Reinert, D., Zängl, G., Giorgetta, M., Ruhnke, R., Vogel, B., and Braesicke, P.: ICON-ART 2.1: a flexible tracer framework and its application for composition studies in numerical weather forecasting and climate simulations, Geosci. Model Dev., 11, 4043–4068, https://doi.org/10.5194/gmd-11-4043-2018, 2018. a, b
Schuldt, K. N., Mund, J., Aalto, T., Abshire, J. B., Aikin, K., Allen, G., Andrews, A., Apadula, F., Arnold, S., Baier, B., Bakwin, P., Báni, L., Bartyzel, J., Bentz, G., Bergamaschi, P., Beyersdorf, A., Biermann, T., Biraud, S. C., Blanc, P.-E., Boenisch, H., Bowling, D., Brailsford, G., Brand, W. A., Brunner, D., Bui, T. P. V., Van Den Bulk, P., Calzolari, F., Chang, C. S., Chen, G., Chen, H., Chmura, L., St. Clair, J. M., Clark, S., Climadat, S., Coletta, J. D., Colomb, A., Commane, R., Condori, L., Conen, F., Conil, S., Couret, C., Cristofanelli, P., Cuevas, E., Curcoll, R., Daube, B., Davis, K. J., Dean-Day, J. M., Delmotte, M., Dickerson, R., DiGangi, E., DiGangi, J. P., Van Dinther, D., Elkins, J. W., Elsasser, M., Emmenegger, L., Fang, S., Fischer, M. L., Forster, G., France, J., Frumau, A., Fuente-Lastra, M., Galkowski, M., Gatti, L. V., Gehrlein, T., Gerbig, C., Gheusi, F., Gloor, E., Goto, D., Griffis, T., Hammer, S., Hanisco, T. F., Hanson, C., Haszpra, L., Hatakka, J., Heimann, M., Heliasz, M., Heltai, D., Henne, S., Hensen, A., Hermans, C., Hermansen, O., Hintsa, E., Hoheisel, A., Holst, J., Di Iorio, T., Iraci, L. T., Ivakhov, V., Jaffe, D. A., Jordan, A., Joubert, W., Kang, H.-Y., Karion, A., Kawa, S. R., Kazan, V., Keeling, R. F., Keronen, P., Kim, J., Klausen, J., Kneuer, T., Ko, M.-Y., Kolari, P., Kominkova, K., Kort, E., Kozlova, E., Krummel, P. B., Kubistin, D., Kulawik, S. S., Kumps, N., Labuschagne, C., Lam, D. H., Lan, X., Langenfelds, R. L., Lanza, A., Laurent, O., Laurila, T., Lauvaux, T., Lavric, J., Law, B. E., Lee, C.-H., Lee, H., Lee, J., Lehner, I., Lehtinen, K., Leppert, R., Leskinen, A., Leuenberger, M., Leung, W. H., Levin, I., Levula, J., Lin, J., Lindauer, M., Lindroth, A., Lafvenius, M. O., Loh, Z. M., Lopez, M., Lunder, C. R., Machida, T., Mammarella, I., Manca, G., Manning, A., Manning, A., Marek, M. V., Marklund, P., Marrero, J. E., Martin, D., Martin, M. Y., Martins, G. A., Matsueda, H., De Mazièe, M., McKain, K., Meijer, H., Meinhardt, F., Merchant, L., Metzger, J.-M., Mihalopoulos, N., Miles, N. L., Miller, C. E., Miller, J. B., Mitchell, L., Mölder, M., Monteiro, V., Montzka, S., Moore, F., Moossen, H., Morgan, E., Morgui, J.-A., Morimoto, S., Múller-Williams, J., Munger, J. W., Munro, D., Mutuku, M., Myhre, C. L., Nakaoka, S.-I., Necki, J., Newman, S., Nichol, S., Nisbet, E., Niwa, Y., Njiru, D. M., Noe, S. M., Nojiri, Y., O'Doherty, S., Obersteiner, F., Paplawsky, B., Parworth, C. L., Peischl, J., Peltola, O., Peters, W., Philippon, C., Piacentino, S., Pichon, J. M., Pickers, P., Piper, S., Pitt, J., Plass-Dülmer, C., Platt, S. M., Prinzivalli, S., Ramonet, M., Ramos, R., Ren, X., Reyes-Sanchez, E., Richardson, S. J., Rigouleau, L.-J., Riris, H., Rivas, P. P., Rothe, M., Roulet, Y.-A., Ryerson, T., Ryoo, J.-M., Sargent, M., Di Sarra, A. G., Sasakawa, M., Scheeren, B., Schmidt, M., Schuck, T., Schumacher, M., Seibel, J., Seifert, T., Sha, M. K., Shepson, P., Shook, M., Sloop, C. D., Smith, P. D., Sørensen, L. L., De Souza, R. A. F., Spain, G., Steger, D., Steinbacher, M., Stephens, B., Sweeney, C., Taipale, R., Takatsuji, S., Tans, P., Thoning, K., Timas, H., Torn, M., Trisolino, P., Turnbull, J., Vermeulen, A., Viner, B., Vitkova, G., Walker, S., Watson, A., Weiss, R., De Wekker, S., Weyrauch, D., Wofsy, S. C., Worsey, J., Worthy, D., Xueref-Remy, I., Yates, E. L., Young, D., Yver-Kwok, C., Zaehle, S., Zahn, A., Zellweger, C., and Zimnoch, M.: Multi-laboratory compilation of atmospheric carbon dioxide data for the period 1957–2022; obspack_co2_1_GLOBALVIEWplus_v9.1_2023-12-08, NOAA, https://doi.org/10.25925/20231201, 2023. a
Shlyaeva, A., Whitaker, J. S., and Snyder, C.: Model-Space Localization in Serial Ensemble Filters, J. Adv. Model. Earth Syst., 11, 1627–1636, https://doi.org/10.1029/2018MS001514, 2019. a
Skamarock, C., Klemp, B., Dudhia, J., Gill, O., Liu, Z., Berner, J., Wang, W., Powers, G., Duda, G., Barker, D., and Huang, X.-Y.: A Description of the Advanced Research WRF Model Version 4.3, No. NCAR/TN-556+STR, NCAR, https://doi.org/10.5065/1dfh-6p97, 2021. a
Steinbach, J., Gerbig, C., Rödenbeck, C., Karstens, U., Minejima, C., and Mukai, H.: The CO2 release and Oxygen uptake from Fossil Fuel Emission Estimate (COFFEE) dataset: effects from varying oxidative ratios, Atmos. Chem. Phys., 11, 6855–6870, https://doi.org/10.5194/acp-11-6855-2011, 2011. a
Stohl, A., Seibert, P., Arduini, J., Eckhardt, S., Fraser, P., Greally, B. R., Lunder, C., Maione, M., Mühle, J., O'Doherty, S., Prinn, R. G., Reimann, S., Saito, T., Schmidbauer, N., Simmonds, P. G., Vollmer, M. K., Weiss, R. F., and Yokouchi, Y.: An analytical inversion method for determining regional and global emissions of greenhouse gases: Sensitivity studies and application to halocarbons, Atmos. Chem. Phys., 9, 1597–1620, https://doi.org/10.5194/acp-9-1597-2009, 2009. a
Suto, H., Kataoka, F., Kikuchi, N., Knuteson, R. O., Butz, A., Haun, M., Buijs, H., Shiomi, K., Imai, H., and Kuze, A.: Thermal and near-infrared sensor for carbon observation Fourier transform spectrometer-2 (TANSO-FTS-2) on the Greenhouse gases Observing SATellite-2 (GOSAT-2) during its first year in orbit, Atmos. Meas. Tech., 14, 2013–2039, https://doi.org/10.5194/amt-14-2013-2021, 2021. a
Talagrand, O.: A posteriori evaluation and verification of analysis and assimilation algorithms, ECMWF, https://www.ecmwf.int/en/elibrary/76590-posteriori-evaluation-and-verification-analysis-and (last access: 15 January 2025), 1999. a, b
Tarantola, A.: Inverse Problem Theory and Methods for Model Parameter Estimation, Elvesier, https://doi.org/10.1137/1.9780898717921, 1987. a, b, c
Taylor, T. E., O'Dell, C. W., Baker, D., Bruegge, C., Chang, A., Chapsky, L., Chatterjee, A., Cheng, C., Chevallier, F., Crisp, D., Dang, L., Drouin, B., Eldering, A., Feng, L., Fisher, B., Fu, D., Gunson, M., Haemmerle, V., Keller, G. R., Kiel, M., Kuai, L., Kurosu, T., Lambert, A., Laughner, J., Lee, R., Liu, J., Mandrake, L., Marchetti, Y., McGarragh, G., Merrelli, A., Nelson, R. R., Osterman, G., Oyafuso, F., Palmer, P. I., Payne, V. H., Rosenberg, R., Somkuti, P., Spiers, G., To, C., Weir, B., Wennberg, P. O., Yu, S., and Zong, J.: Evaluating the consistency between OCO-2 and OCO-3 XCO2 estimates derived from the NASA ACOS version 10 retrieval algorithm, Atmos. Meas. Tech., 16, 3173–3209, https://doi.org/10.5194/amt-16-3173-2023, 2023. a
Thanwerdas, J., Saunois, M., Berchet, A., Pison, I., Vaughn, B. H., Michel, S. E., and Bousquet, P.: Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate δ13C(CH4) and CH4: a case study with model LMDz-SACS, Geosci. Model Dev., 15, 4831–4851, https://doi.org/10.5194/gmd-15-4831-2022, 2022a. a
Thanwerdas, J., Saunois, M., Pison, I., Hauglustaine, D., Berchet, A., Baier, B., Sweeney, C., and Bousquet, P.: How do Cl concentrations matter for the simulation of CH4 and δ13C(CH4) and estimation of the CH4 budget through atmospheric inversions?, Atmos. Chem. Phys., 22, 15489–15508, https://doi.org/10.5194/acp-22-15489-2022, 2022b. a
Thanwerdas, J., Saunois, M., Berchet, A., Pison, I., and Bousquet, P.: Investigation of the renewed methane growth post-2007 with high-resolution 3-D variational inverse modeling and isotopic constraints, Atmos. Chem. Phys., 24, 2129–2167, https://doi.org/10.5194/acp-24-2129-2024, 2024. a, b
Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., and Whitaker, J. S.: Ensemble Square Root Filters, Mon. Weather Rev., 131, 1485–1490, https://doi.org/10.1175/1520-0493(2003)131<1485:ESRF>2.0.CO;2, 2003. a, b
Tsuruta, A., Aalto, T., Backman, L., Hakkarainen, J., van der Laan-Luijkx, I. T., Krol, M. C., Spahni, R., Houweling, S., Laine, M., Dlugokencky, E., Gomez-Pelaez, A. J., van der Schoot, M., Langenfelds, R., Ellul, R., Arduini, J., Apadula, F., Gerbig, C., Feist, D. G., Kivi, R., Yoshida, Y., and Peters, W.: Global methane emission estimates for 2000–2012 from CarbonTracker Europe-CH4 v1.0, Geosci. Model Dev., 10, 1261–1289, https://doi.org/10.5194/gmd-10-1261-2017, 2017. a, b
Tsuruta, A., Kivimäki, E., Lindqvist, H., Karppinen, T., Backman, L., Hakkarainen, J., Schneising, O., Buchwitz, M., Lan, X., Kivi, R., Chen, H., Buschmann, M., Herkommer, B., Notholt, J., Roehl, C., Té, Y., Wunch, D., Tamminen, J., and Aalto, T.: CH4 Fluxes Derived from Assimilation of TROPOMI XCH4 in CarbonTracker Europe-CH4: Evaluation of Seasonality and Spatial Distribution in the Northern High Latitudes, Remote Sens., 15, 1620, https://doi.org/10.3390/rs15061620, 2023. a, b
van der Laan-Luijkx, I. T., van der Velde, I. R., Krol, M. C., Gatti, L. V., Domingues, L. G., Correia, C. S. C., Miller, J. B., Gloor, M., van Leeuwen, T. T., Kaiser, J. W., Wiedinmyer, C., Basu, S., Clerbaux, C., and Peters, W.: Response of the Amazon carbon balance to the 2010 drought derived with CarbonTracker South America, Global Biogeochem. Cy. 29, 1092–1108, https://doi.org/10.1002/2014GB005082, 2015. a
van der Laan-Luijkx, I. T., van der Velde, I. R., van der Veen, E., Tsuruta, A., Stanislawska, K., Babenhauserheide, A., Zhang, H. F., Liu, Y., He, W., Chen, H., Masarie, K. A., Krol, M. C., and Peters, W.: The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: implementation and global carbon balance 2001–2015, Geosci. Model Dev., 10, 2785–2800, https://doi.org/10.5194/gmd-10-2785-2017, 2017. a, b, c, d
Wang, J. S., Kawa, S. R., Collatz, G. J., Sasakawa, M., Gatti, L. V., Machida, T., Liu, Y., and Manyin, M. E.: A global synthesis inversion analysis of recent variability in CO2 fluxes using GOSAT and in situ observations, Atmos. Chem. Phys., 18, 11097–11124, https://doi.org/10.5194/acp-18-11097-2018, 2018. a
Wang, X., Bishop, C. H., and Julier, S. J.: Which Is Better, an Ensemble of Positive–Negative Pairs or a Centered Spherical Simplex Ensemble?, Mon. Weather Rev., 132, 1590–1605, https://doi.org/10.1175/1520-0493(2004)132<1590:WIBAEO>2.0.CO;2, 2004. a
Wittig, S., Berchet, A., Pison, I., Saunois, M., Thanwerdas, J., Martinez, A., Paris, J.-D., Machida, T., Sasakawa, M., Worthy, D. E. J., Lan, X., Thompson, R. L., Sollum, E., and Arshinov, M.: Estimating methane emissions in the Arctic nations using surface observations from 2008 to 2019, Atmos. Chem. Phys., 23, 6457–6485, https://doi.org/10.5194/acp-23-6457-2023, 2023. a, b
Zammit-Mangion, A., Cressie, N., and Ganesan, A. L.: Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion, Spat. Stat., 18, 194–220, https://doi.org/10.1016/j.spasta.2016.06.005, 2016. a
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core, Q. J. Roy. Meteorol. Soc., 141, 563–579, https://doi.org/10.1002/qj.2378, 2015. a, b, c
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. a
Short summary
The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas...
