Articles | Volume 19, issue 3
https://doi.org/10.5194/amt-19-813-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-813-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Feb 2026
Research article |
| 05 Feb 2026
| 05 Feb 2026
Uncertainty and retrieval sensitivity in TROPOMI-based methane inversions over the North Slope of Alaska
School of Geographical Sciences, University of Bristol, Bristol, United Kingdom
Climate System Research, Finnish Meteorological Institute, 00560 Helsinki, Finland
Luke M. Western
School of Chemistry, University of Bristol, Bristol, United Kingdom
Center for Sustainability Science and Strategy, Massachusetts Institute of Technology, Cambridge, MA, USA
Rachel L. Tunnicliffe
School of Chemistry, University of Bristol, Bristol, United Kingdom
Elena Fillola
Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
Aki Tsuruta
Climate System Research, Finnish Meteorological Institute, 00560 Helsinki, Finland
Tuula Aalto
Climate System Research, Finnish Meteorological Institute, 00560 Helsinki, Finland
Anita L. Ganesan
School of Geographical Sciences, University of Bristol, Bristol, United Kingdom
Related authors
No articles found.
Elinor C. Tuffnell, Emma Leedham-Elvidge, William T. Sturges, Harald Bönisch, Karina E. Adcock, Paul J. Fraser, Paul B. Krummel, David E. Oram, Ray L. Langenfelds, Thomas Röckmann, Luke M. Western, Jens Mühle, and Johannes C. Laube
Atmos. Chem. Phys., 26, 4583–4599, https://doi.org/10.5194/acp-26-4583-2026, https://doi.org/10.5194/acp-26-4583-2026, 2026
Short summary
Short summary
The greater the stratospheric lifetime of chlorofluorocarbons (CFCs), the longer they will deplete ozone. This paper investigates four longer-lived CFCs, and discovers two of them have much shorter lifetimes than previously believed. Demonstrating emissions of these compounds are higher than assumed, to account for their abundance. Unusually this paper uses stratospheric whole-air samples, rather than models or lab-based experiments, to derive policy-relevant metrics for these compounds.
Joseph R. Pitt, Dominique Rust, Anita Ganesan, Luke M. Western, Martin K. Vollmer, Jens Mühle, Tobias Bühlmann, Christina M. Harth, Stephen A. Montzka, Brad D. Hall, Isaac J. Vimont, Alistair J. Manning, Alison L. Redington, Stephan Henne, Daniela B. Melo, Saurabh Annadate, Lionel Constantin, Brendan M. Murphy, Matthew Rigby, Dickon Young, Simon O'Doherty, Angelina Wenger, Chris R. Lunder, Ove Hermansen, Thomas Wagenhäuser, Andreas Engel, Jgor Arduini, Michela Maione, Jaegeun Yun, Blagoj Mitrevski, Paul B. Krummel, Paul J. Fraser, Jooil Kim, Ray H. J. Wang, Tae Siek Rhee, Peter K. Salameh, T. Gerard Spain, Stefan Reimann, Ronald G. Prinn, Ray F. Weiss, and Kieran M. Stanley
EGUsphere, https://doi.org/10.5194/egusphere-2026-1589, https://doi.org/10.5194/egusphere-2026-1589, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
1,2‑Dichloroethane (DCE) can contribute to the depletion of the stratospheric ozone layer. Whether DCE reaches the stratosphere depends on where and when it is emitted. We present the ongoing measurements of DCE from the AGAGE and NOAA global monitoring networks. For the period 2017–2023, our modelled average global emissions based on these observations align with another study, while regional estimates differ. This suggests remaining uncertainty in the geographical distribution of emissions.
Alice Emily Ramsden, Anita Ganesan, Matthew Rigby, Chris Rennick, Tim Arnold, Emmal Safi, Edward Chung, Dafina Kikaj, Cameron Yeo, Dave Lowry, Pete Levy, Simon O'Doherty, Kieran Stanley, Dickon Young, Joe Pitt, Damien Martin, Morgan Lopez, Michel Ramonet, Grant Forster, Arnoud Frumau, and Alistair Manning
EGUsphere, https://doi.org/10.5194/egusphere-2026-779, https://doi.org/10.5194/egusphere-2026-779, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Methane is emitted from a range of anthropogenic and natural sources, each with a characteristic isotope ratio signature. We present a method for estimating emissions from multiple sources using isotopic signatures and observations of atmospheric methane isotope ratios. We show the importance of considering isotopic signature uncertainty and demonstrate how our current understanding of these signatures may be limiting this method’s ability to reduce uncertainties in emissions estimates.
Mengze Li, Robert B. Jackson, Marielle Saunois, Philippe Ciais, Ben Poulter, Josep G. Canadell, Prabir K. Patra, Hanqin Tian, Zhen Zhang, Etienne Fluet-Chouinard, Zutao Ouyang, Ting Zhang, David Beerling, Dmitry Belikov, Philippe Bousquet, Danilo Custodio, Naveen Chandra, Xinyu Dou, Nicola Gedney, Peter O. Hopcroft, Alison Hoyt, Kazuhito Ichii, Akihito Ito, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Masayuki Kondo, Fa Li, Tingting Li, Xiangyu Liu, Shamil Maksyutov, Avni Malhotra, Adrien Martinez, Kyle McDonald, Joe R. Melton, Paul Miller, Jurek Müller, Yosuke Niwa, Shufen Pan, Shushi Peng, Changhui Peng, Zhangcai Qin, Peter Raymond, William Riley, Arjo Segers, Rona L. Thompson, Aki Tsuruta, Xi Yi, Kunxiaojia Yuan, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-176, https://doi.org/10.5194/essd-2026-176, 2026
Preprint under review for ESSD
Short summary
Short summary
We proposed a framework that combines artificial intelligence and climate data to predict natural wetland methane emissions for 2000–2025. We found that although total global emissions remained stable, Northern Hemisphere emissions surged whilst tropical emissions fell. This approach allows us to rapidly monitor emissions and provides early warnings for climate impacts.
Elena Fillola, Raul Santos-Rodriguez, Rachel Tunnicliffe, Jeffrey N. Clark, Nawid Keshtmand, Anita Ganesan, and Matthew Rigby
Geosci. Model Dev., 19, 1893–1915, https://doi.org/10.5194/gmd-19-1893-2026, https://doi.org/10.5194/gmd-19-1893-2026, 2026
Short summary
Short summary
Satellite-based greenhouse gas measurements can be used in “inverse models” to improve emissions reporting, but one of the key components, the simulations of atmospheric transport, struggle to scale to large datasets. We introduce the model GATES, an AI-driven emulator that outputs transport plumes 1000× faster than traditional models. Applied to Brazil’s methane emissions, GATES produces estimates consistent with physics-based methods, offering a scalable path for timely emissions monitoring.
Hélène De Longueville, Daniela B. Melo, Alison Redington, Alice Ramsden, Alexandre Danjou, Peter Andrews, Joseph Pitt, Brendan Murphy, Lionel Constantin, Kieran M. Stanley, Simon O'Doherty, Angelina Wenger, Dickon Young, Andreas Engel, Tanja Schuck, Katharina Meixner, Thomas Wagenhaeuser, Fides Gad, Martin K. Vollmer, Stefan Reimann, Michela Maoine, Jgor Arduini, Chris Lunder, Norbert Schmidtbauer, László Haszpra, Mihály Molnár, Arnoud Frumau, Cedric Couret, Matthew Rigby, Stephan Henne, Alistair Manning, and Anita Ganesan
EGUsphere, https://doi.org/10.5194/egusphere-2026-194, https://doi.org/10.5194/egusphere-2026-194, 2026
Short summary
Short summary
This study estimates emissions of hydrofluorocarbons, important greenhouse gases, in north-western Europe using atmospheric observations and atmospheric modelling. The estimates are compared with nationally reported emissions submitted to the United Nations. Overall, our results are consistent with reported values, although differences are found for some gases and countries. The findings indicate that emissions in north-western Europe are declining, reflecting the effects of climate regulations.
Ahmed Hasan Shahriyer, David Kraus, Tiina Markkanen, Mika Korkiakoski, Helena Rautakoski, Suvi Orttenvuori, Yao Gao, Henri Kajasilta, Rüdiger Grote, Annalea Lohila, and Tuula Aalto
EGUsphere, https://doi.org/10.5194/egusphere-2025-5197, https://doi.org/10.5194/egusphere-2025-5197, 2026
Short summary
Short summary
We successfully represented hydrology and carbon cycle associated with different forestry managements (Rotational and continuous cover forestry) for a drained peatland ecosystem using the processed based model LDNDC. This provides a robust framework for investigating future management scenarios and develop forest management strategies that supports climate neutrality in peatland ecosystems.
Helen Walter-Terrinoni, John S. Daniel, Chelsea R. Thompson, and Luke M. Western
Atmos. Chem. Phys., 26, 1193–1210, https://doi.org/10.5194/acp-26-1193-2026, https://doi.org/10.5194/acp-26-1193-2026, 2026
Short summary
Short summary
This study presents a new bottom-up model to estimate emissions and banks of long-lived ozone-depleting substances. It is applied here to HCFC-141b. Calculated global emission trends are qualitatively consistent with atmospheric observations from 1990–2017. However, they diverge after 2017, suggesting either a growing additional source or a model deficiency. The easily recoverable portion of the bank is projected to be smaller than previously estimated, impacting future recovery feasibility.
Kane Stone, Candice Chen, Susan Solomon, Luke M. Western, Paul B. Krummel, Gabrielle Pétron, Jens Mühle, and Simon O’Doherty
EGUsphere, https://doi.org/10.5194/egusphere-2025-6461, https://doi.org/10.5194/egusphere-2025-6461, 2026
Short summary
Short summary
There is a growing interest in hydrogens impact on atmospheric chemistry and climate. Here, the seasonality of hydrogen oxidation and loss to microbial activity in soils are investigated using information from the hydrofluorocarbon, HFC-152a. A large seasonal range of the soil sink, over twice that of hydroxyl loss, is seen in the Northern latitudes peaking in the late summer, while the South shows a much lower soil sink range. This will be useful for chemistry climate model hydrogen cycles.
Anteneh Getachew Mengistu, Aki Tsuruta, Antoine Berchet, Rona Thompson, Maria Tenkanen, Hannakaisa Lindqvist, Tiina Markkanen, Antti Leppänen, Antti Laitinen, Adrien Martinez, Audrey Fortems-Cheiney, Lena Höglund-Isaksson, and Tuula Aalto
EGUsphere, https://doi.org/10.5194/egusphere-2025-5877, https://doi.org/10.5194/egusphere-2025-5877, 2026
Short summary
Short summary
Our manuscript presents a six-year, high-resolution inversion of European methane emissions using the Community Inversion Framework and FLEXPART. Leveraging an expanded in situ network, we reconcile inventories with observations, reveal regional biases, refine European methane budgets, and highlight the dominance of agricultural emissions. Results provide policy-relevant insights for mitigation and inventory verification.
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, 18, 167–198, https://doi.org/10.5194/essd-18-167-2026, https://doi.org/10.5194/essd-18-167-2026, 2026
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 Integrated Carbon Observation System (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.
Luke M. Western, Stephen Bourguet, Molly Crotwell, Lei Hu, Paul B. Krummel, Hélène De Longueville, Alistair J. Manning, Jens Mühle, Dominique Rust, Isaac Vimont, Martin K. Vollmer, Minde An, Jgor Arduini, Andreas Engel, Paul J. Fraser, Anita L. Ganesan, Christina M. Harth, Chris Lunder, Michela Maione, Stephen A. Montzka, David Nance, Simon O'Doherty, Sunyoung Park, Stefan Reimann, Peter K. Salameh, Roland Schmidt, Kieran M. Stanley, Thomas Wagenhäuser, Dickon Young, Matt Rigby, Ronald G. Prinn, and Ray F. Weiss
Atmos. Chem. Phys., 25, 17761–17778, https://doi.org/10.5194/acp-25-17761-2025, https://doi.org/10.5194/acp-25-17761-2025, 2025
Short summary
Short summary
We used atmospheric measurements to estimate emissions of two hydrochlorofluorocarbon (HCFC) gases, called HCFC-123 and HCFC-124, that harm the ozone layer. Despite international regulation to stop their production, their emissions have not fallen. This may be linked to how they are used to make other chemicals. Our findings show that some banned substances are still reaching the atmosphere, likely through leaks during chemical production, which could slow the recovery of the ozone layer.
Luke M. Western, Matthew Rigby, Jens Mühle, Paul B. Krummel, Chris R. Lunder, Simon O'Doherty, Stefan Reimann, Martin K. Vollmer, Dickon Young, Ben Adam, Paul J. Fraser, Anita L. Ganesan, Christina M. Harth, Ove Hermansen, Jooil Kim, Ray L. Langenfelds, Zoë M. Loh, Blagoj Mitrevski, Joseph R. Pitt, Peter K. Salameh, Roland Schmidt, Kieran Stanley, Ann R. Stavert, Hsiang-Jui Wang, Ray F. Weiss, and Ronald G. Prinn
Earth Syst. Sci. Data, 17, 6557–6582, https://doi.org/10.5194/essd-17-6557-2025, https://doi.org/10.5194/essd-17-6557-2025, 2025
Short summary
Short summary
We used global measurements and an atmospheric model to estimate how emissions and abundances of 42 chemically and radiatively important trace gases have changed over time. These gases affect the Earth's radiative balance and the ozone layer. Our data sets help track progress in reducing emissions of these gases to the atmosphere. This work supports international efforts to protect the environment by providing clear, long-term, consistent data on how these gases are changing in the atmosphere.
Juha Leskinen, Leif Backman, Tiina Markkanen, Jussi Lintunen, and Tuula Aalto
EGUsphere, https://doi.org/10.5194/egusphere-2025-4882, https://doi.org/10.5194/egusphere-2025-4882, 2025
Preprint archived
Short summary
Short summary
In forest management, carbon sequestration is often the main focus when it comes to climate change mitigation. However, forests impact the climate in other ways too. We examined albedo and heat fluxes alongside carbon sequestration in a boreal Finnish pine forest. For the studied management scenarios we found that the differences in carbon sequestration were the main drivers for total climate impact, albedo mitigated it by a few percentage points, the offset from the heat fluxes were even less.
Martin K. Vollmer, Joseph R. Pitt, Dickon Young, Stephan Henne, Blagoj Mitrevski, Jens Mühle, Anita Ganesan, Jgor Arduini, Alistair J. Manning, Thomas Wagenhäuser, Alison L. Redington, Brendan Murphy, Ray Gluckmann, Kieran M. Stanley, Paul B. Krummel, Chris R. Lunder, Jaegeun Yun, Dominique Rust, Angelina Wenger, Myriam Guillevic, Jooil Kim, Ray H. J. Wang, Tae Siek Rhee, Lionel Constantin, Arnoud Frumau, Christina M. Harth, Peter K. Salameh, Ove Hermansen, Andreas Engel, Simon O'Doherty, Sunyoung Park, Michela Maione, Paul J. Fraser, Ronald G. Prinn, Ray F. Weiss, and Stefan Reimann
EGUsphere, https://doi.org/10.5194/egusphere-2025-4824, https://doi.org/10.5194/egusphere-2025-4824, 2025
Short summary
Short summary
We provide atmospheric measurements of halogenated olefins from the Advanced Global Atmospheric Gases Experiments and we calculate NorthWest European Emissions.
Ella Kivimäki, Maria Tenkanen, Tuula Aalto, Michael Buchwitz, Kari Luojus, Jouni Pulliainen, Kimmo Rautiainen, Oliver Schneising, Anu-Maija Sundström, Johanna Tamminen, Aki Tsuruta, and Hannakaisa Lindqvist
Biogeosciences, 22, 5193–5230, https://doi.org/10.5194/bg-22-5193-2025, https://doi.org/10.5194/bg-22-5193-2025, 2025
Short summary
Short summary
We study how environmental variables influencing natural methane fluxes explain the seasonal variability in satellite-observed methane in Northern Hemisphere high-latitude wetland areas. Using two atmospheric model set-ups, we assess consistency with satellite data. Methane loss through reaction with hydroxyl radicals and links with snow cover, temperature, and snowmelt had the strongest influence. Our study highlights the value of satellite observations for understanding large-scale wetland emissions.
Kirstin Gerrand, Elena Fillola, Alistair J. Manning, Jgor Arduini, Paul B. Krummel, Chris R. Lunder, Jens Mühle, Simon O'Doherty, Sunyoung Park, Ronald G. Prinn, Stefan Reimann, Dickon Young, and Matthew Rigby
EGUsphere, https://doi.org/10.5194/egusphere-2025-4137, https://doi.org/10.5194/egusphere-2025-4137, 2025
Short summary
Short summary
To analyse long-term trends in atmospheric trace gas concentrations, it is important to identify data points minimally affected by local pollution sources or air masses carried from other latitudes or altitudes. Traditional methods for detecting these “baselines” are computationally expensive or lack a basis in physical principles. This paper introduces a machine-learning method that uses meteorological data and offers significantly lower computational costs compared to physics-based techniques.
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
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.
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.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Christophe Cassou, Mathias Hauser, Zeke Hausfather, June-Yi Lee, Matthew D. Palmer, Karina von Schuckmann, Aimée B. A. Slangen, Sophie Szopa, Blair Trewin, Jeongeun Yun, Nathan P. Gillett, Stuart Jenkins, H. Damon Matthews, Krishnan Raghavan, Aurélien Ribes, Joeri Rogelj, Debbie Rosen, Xuebin Zhang, Myles Allen, Lara Aleluia Reis, Robbie M. Andrew, Richard A. Betts, Alex Borger, Jiddu A. Broersma, Samantha N. Burgess, Lijing Cheng, Pierre Friedlingstein, Catia M. Domingues, Marco Gambarini, Thomas Gasser, Johannes Gütschow, Masayoshi Ishii, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Aurélien Liné, Didier P. Monselesan, Colin Morice, Jens Mühle, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Jan C. Minx, Matthew Rigby, Robert Rohde, Abhishek Savita, Sonia I. Seneviratne, Peter Thorne, Christopher Wells, Luke M. Western, Guido R. van der Werf, Susan E. Wijffels, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 17, 2641–2680, https://doi.org/10.5194/essd-17-2641-2025, https://doi.org/10.5194/essd-17-2641-2025, 2025
Short summary
Short summary
In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. Here we compile monitoring datasets to track real-world changes over time. To make our work relevant to policymakers, we follow methods from the Intergovernmental Panel on Climate Change (IPCC). Human activities are increasing the Earth's energy imbalance and driving faster sea-level rise compared to the IPCC assessment.
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, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
Short summary
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.
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.
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.
Vilna Tyystjärvi, Tiina Markkanen, Leif Backman, Maarit Raivonen, Antti Leppänen, Xuefei Li, Paavo Ojanen, Kari Minkkinen, Roosa Hautala, Mikko Peltoniemi, Jani Anttila, Raija Laiho, Annalea Lohila, Raisa Mäkipää, and Tuula Aalto
Biogeosciences, 21, 5745–5771, https://doi.org/10.5194/bg-21-5745-2024, https://doi.org/10.5194/bg-21-5745-2024, 2024
Short summary
Short summary
Drainage of boreal peatlands strongly influences soil methane fluxes, with important implications for climatic impacts. Here we simulate methane fluxes in forestry-drained and restored peatlands during the 21st century. We found that restoration turned peatlands into a source of methane, but the magnitude varied regionally. In forests, changes in the water table level influenced methane fluxes, and in general, the sink was weaker under rotational forestry compared to continuous cover forestry.
Outi Kinnunen, Leif Backman, Juha Aalto, Tuula Aalto, and Tiina Markkanen
Biogeosciences, 21, 4739–4763, https://doi.org/10.5194/bg-21-4739-2024, https://doi.org/10.5194/bg-21-4739-2024, 2024
Short summary
Short summary
Climate change is expected to increase the risk of forest fires. Ecosystem process model simulations are used to project changes in fire occurrence in Fennoscandia under six climate projections. The findings suggest a longer fire season, more fires, and an increase in burnt area towards the end of the century.
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.
Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
Earth Syst. Sci. Data, 16, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024, https://doi.org/10.5194/essd-16-2543-2024, 2024
Short summary
Short summary
Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
Hannah Chawner, Eric Saboya, Karina E. Adcock, Tim Arnold, Yuri Artioli, Caroline Dylag, Grant L. Forster, Anita Ganesan, Heather Graven, Gennadi Lessin, Peter Levy, Ingrid T. Luijkx, Alistair Manning, Penelope A. Pickers, Chris Rennick, Christian Rödenbeck, and Matthew Rigby
Atmos. Chem. Phys., 24, 4231–4252, https://doi.org/10.5194/acp-24-4231-2024, https://doi.org/10.5194/acp-24-4231-2024, 2024
Short summary
Short summary
The quantity of atmospheric potential oxygen (APO), derived from coincident measurements of carbon dioxide (CO2) and oxygen (O2), has been proposed as a tracer for fossil fuel CO2 emissions. In this model sensitivity study, we examine the use of APO for this purpose in the UK and compare our model to observations. We find that our model simulations are most sensitive to uncertainties relating to ocean fluxes and boundary conditions.
Tanja J. Schuck, Johannes Degen, Eric Hintsa, Peter Hoor, Markus Jesswein, Timo Keber, Daniel Kunkel, Fred Moore, Florian Obersteiner, Matt Rigby, Thomas Wagenhäuser, Luke M. Western, Andreas Zahn, and Andreas Engel
Atmos. Chem. Phys., 24, 689–705, https://doi.org/10.5194/acp-24-689-2024, https://doi.org/10.5194/acp-24-689-2024, 2024
Short summary
Short summary
We study the interhemispheric gradient of sulfur hexafluoride (SF6), a strong long-lived greenhouse gas. Its emissions are stronger in the Northern Hemisphere; therefore, mixing ratios in the Southern Hemisphere lag behind. Comparing the observations to a box model, the model predicts air in the Southern Hemisphere to be older. For a better agreement, the emissions used as model input need to be increased (and their spatial pattern changed), and we need to modify north–south transport.
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.
Elena Fillola, Raul Santos-Rodriguez, Alistair Manning, Simon O'Doherty, and Matt Rigby
Geosci. Model Dev., 16, 1997–2009, https://doi.org/10.5194/gmd-16-1997-2023, https://doi.org/10.5194/gmd-16-1997-2023, 2023
Short summary
Short summary
Lagrangian particle dispersion models are used extensively for the estimation of greenhouse gas (GHG) fluxes using atmospheric observations. However, these models do not scale well as data volumes increase. Here, we develop a proof-of-concept machine learning emulator that can produce outputs similar to those of the dispersion model, but 50 000 times faster, using only meteorological inputs. This works demonstrates the potential of machine learning to accelerate GHG estimations across the globe.
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.
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.
Angharad C. Stell, Michael Bertolacci, Andrew Zammit-Mangion, Matthew Rigby, Paul J. Fraser, Christina M. Harth, Paul B. Krummel, Xin Lan, Manfredi Manizza, Jens Mühle, Simon O'Doherty, Ronald G. Prinn, Ray F. Weiss, Dickon Young, and Anita L. Ganesan
Atmos. Chem. Phys., 22, 12945–12960, https://doi.org/10.5194/acp-22-12945-2022, https://doi.org/10.5194/acp-22-12945-2022, 2022
Short summary
Short summary
Nitrous oxide is a potent greenhouse gas and ozone-depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions. We derive increasing global nitrous oxide emissions over 2011–2020, which are mainly driven by emissions between 0° and 30°N, with the highest emissions recorded in 2020.
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.
Alice E. Ramsden, Anita L. Ganesan, Luke M. Western, Matthew Rigby, Alistair J. Manning, Amy Foulds, James L. France, Patrick Barker, Peter Levy, Daniel Say, Adam Wisher, Tim Arnold, Chris Rennick, Kieran M. Stanley, Dickon Young, and Simon O'Doherty
Atmos. Chem. Phys., 22, 3911–3929, https://doi.org/10.5194/acp-22-3911-2022, https://doi.org/10.5194/acp-22-3911-2022, 2022
Short summary
Short summary
Quantifying methane emissions from different sources is a key focus of current research. We present a method for estimating sectoral methane emissions that uses ethane as a tracer for fossil fuel methane. By incorporating variable ethane : methane emission ratios into this model, we produce emissions estimates with improved uncertainty characterisation. This method will be particularly useful for studying methane emissions in areas with complex distributions of sources.
Jens Mühle, Lambert J. M. Kuijpers, Kieran M. Stanley, Matthew Rigby, Luke M. Western, Jooil Kim, Sunyoung Park, Christina M. Harth, Paul B. Krummel, Paul J. Fraser, Simon O'Doherty, Peter K. Salameh, Roland Schmidt, Dickon Young, Ronald G. Prinn, Ray H. J. Wang, and Ray F. Weiss
Atmos. Chem. Phys., 22, 3371–3378, https://doi.org/10.5194/acp-22-3371-2022, https://doi.org/10.5194/acp-22-3371-2022, 2022
Short summary
Short summary
Emissions of the strong greenhouse gas perfluorocyclobutane (c-C4F8) into the atmosphere have been increasing sharply since the early 2000s. These c-C4F8 emissions are highly correlated with the amount of hydrochlorofluorocarbon-22 produced to synthesize polytetrafluoroethylene (known for its non-stick properties) and related chemicals. From this process, c-C4F8 by-product is vented to the atmosphere. Avoiding these unnecessary c-C4F8 emissions could reduce the climate impact of this industry.
Olli Nevalainen, Olli Niemitalo, Istem Fer, Antti Juntunen, Tuomas Mattila, Olli Koskela, Joni Kukkamäki, Layla Höckerstedt, Laura Mäkelä, Pieta Jarva, Laura Heimsch, Henriikka Vekuri, Liisa Kulmala, Åsa Stam, Otto Kuusela, Stephanie Gerin, Toni Viskari, Julius Vira, Jari Hyväluoma, Juha-Pekka Tuovinen, Annalea Lohila, Tuomas Laurila, Jussi Heinonsalo, Tuula Aalto, Iivari Kunttu, and Jari Liski
Geosci. Instrum. Method. Data Syst., 11, 93–109, https://doi.org/10.5194/gi-11-93-2022, https://doi.org/10.5194/gi-11-93-2022, 2022
Short summary
Short summary
Better monitoring of soil carbon sequestration is needed to understand the best carbon farming practices in different soils and climate conditions. We, the Field Observatory Network (FiON), have therefore established a methodology for monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, and modeling. To disseminate our work, we built a website called the Field Observatory (fieldobservatory.org).
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.
Mark F. Lunt, Alistair J. Manning, Grant Allen, Tim Arnold, Stéphane J.-B. Bauguitte, Hartmut Boesch, Anita L. Ganesan, Aoife Grant, Carole Helfter, Eiko Nemitz, Simon J. O'Doherty, Paul I. Palmer, Joseph R. Pitt, Chris Rennick, Daniel Say, Kieran M. Stanley, Ann R. Stavert, Dickon Young, and Matt Rigby
Atmos. Chem. Phys., 21, 16257–16276, https://doi.org/10.5194/acp-21-16257-2021, https://doi.org/10.5194/acp-21-16257-2021, 2021
Short summary
Short summary
We present an evaluation of the UK's methane emissions between 2013 and 2020 using a network of tall tower measurement sites. We find emissions that are consistent in both magnitude and trend with the UK's reported emissions, with a declining trend driven by a decrease in emissions from England. The impact of various components of the modelling set-up on these findings are explored through a number of sensitivity studies.
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.
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.
Cited articles
Alexe, M., Bergamaschi, P., Segers, A., Detmers, R., Butz, A., Hasekamp, O., Guerlet, S., Parker, R., Boesch, H., Frankenberg, C., Scheepmaker, R. A., Dlugokencky, E., Sweeney, C., Wofsy, S. C., and Kort, E. A.: Inverse modelling of CH4 emissions for 2010–2011 using different satellite retrieval products from GOSAT and SCIAMACHY, Atmos. Chem. Phys., 15, 113–133, https://doi.org/10.5194/acp-15-113-2015, 2015. a, b
Berchet, A., Pison, I., Chevallier, F., Paris, J.-D., Bousquet, P., Bonne, J.-L., Arshinov, M. Y., Belan, B. D., Cressot, C., Davydov, D. K., Dlugokencky, E. J., Fofonov, A. V., Galanin, A., Lavrič, J., Machida, T., Parker, R., Sasakawa, M., Spahni, R., Stocker, B. D., and Winderlich, J.: Natural and anthropogenic methane fluxes in Eurasia: a mesoscale quantification by generalized atmospheric inversion, Biogeosciences, 12, 5393–5414, https://doi.org/10.5194/bg-12-5393-2015, 2015. a
Bergamaschi, P., Frankenberg, C., Meirink, J. F., Krol, M., Villani, M. G., Houweling, S., Dentener, F., Dlugokencky, E. J., Miller, J. B., Gatti, L. V., Engel, A., and Levin, I.: Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals, Journal of Geophysical Research: Atmospheres, 114, https://doi.org/10.1029/2009JD012287, 2009. a, b
Box, J. E., Colgan, W. T., Christensen, T. R., Schmidt, N. M., Lund, M., Parmentier, F.-J. W., Brown, R., Bhatt, U. S., Euskirchen, E. S., Romanovsky, V. E., Walsh, J. E., Overland, J. E., Wang, M., Corell, R. W., Meier, W. N., Wouters, B., Mernild, S., Mård, J., Pawlak, J., and Olsen, M. S.: Key indicators of Arctic climate change: 1971–2017, Environ. Res. Lett., 14, https://doi.org/10.1088/1748-9326/aafc1b, 2019. a
Buchwitz, M., de Beek, R., Noël, S., Burrows, J. P., Bovensmann, H., Bremer, H., Bergamaschi, P., Körner, S., and Heimann, M.: Carbon monoxide, methane and carbon dioxide columns retrieved from SCIAMACHY by WFM-DOAS: year 2003 initial data set, Atmos. Chem. Phys., 5, 3313–3329, https://doi.org/10.5194/acp-5-3313-2005, 2005. a
Crippa, M., Solazzo, E., Huang, G., Guizzardi, D., Koffi, E., Muntean, M., Schieberle, C., Friedrich, R., and Janssens-Maenhout, G.: High resolution temporal profiles in the Emissions Database for Global Atmospheric Research, Scientific Data, 7, 121, https://doi.org/10.1038/s41597-020-0462-2, 2020. a
Crippa, M., Guizzardi, D., Pagani, F., Banja, M., Muntean, M., Schaaf, E., Monforti-Ferrario, F., Becker, W., Quadrelli, R., Risquez Martin, A., Taghavi-Moharamli, P., Köykkä, J., Grassi, G., Rossi, S., Melo, J., Oom, D., Branco, A., San-Miguel, J., Manca, G., Pisoni, E., Vignati, E., and Pekar, F.: GHG emissions of all world countries, Tech. rep., Luxembourg, Publications Office of the European Union, ISBN 978-92-68-20572-3, 2024. a
Degen, J., Baier, B., Blazaki, K., Chen, H., Engel, A., Heikkinen, P., Karhu, J., Kivi, R., Leuenberger, M., Meixner, K., Nyfeler, P., Sweeney, C., van Heuven, S., Zanchetta, A., and Laube, J.: Short-term variability and uncertainties of trace gases in the boreal summer UTLS from AirCore measurements during the OSTRICH campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15443, https://doi.org/10.5194/egusphere-egu24-15443, 2024. a
Dlugokencky, E. J., Steele, L. P., Lang, P. M., and Masarie, K. A.: Atmospheric methane at Mauna Loa and Barrow observatories: Presentation and analysis of in situ measurements, Journal of Geophysical Research: Atmospheres, 100, 23103–23113, https://doi.org/10.1029/95JD02460, 1995. a
Ehret, G., Bousquet, P., Pierangelo, C., Alpers, M., Millet, B., Abshire, J., Bovensmann, H., Burrows, J., Chevallier, F., Ciais, P., Crevoisier, C., Fix, A., Flamant, P., Frankenberg, C., Gibert, F., Heim, B., Heimann, M., Houweling, S., Hubberten, H., Jöckel, P., Law, K., Löw, A., Marshall, J., Agusti-Panareda, A., Payan, S., Prigent, C., Rairoux, P., Sachs, T., Scholze, M., and Wirth, M.: MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane, Remote Sensing, 9, 1052, https://doi.org/10.3390/rs9101052, 2017. a, b
Fillola, E., Santos-Rodriguez, R., Manning, A., O'Doherty, S., and Rigby, M.: A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME, Geosci. Model Dev., 16, 1997–2009, https://doi.org/10.5194/gmd-16-1997-2023, 2023. a
Frey, M., Sha, M. K., Hase, F., Kiel, M., Blumenstock, T., Harig, R., Surawicz, G., Deutscher, N. M., Shiomi, K., Franklin, J. E., Bösch, H., Chen, J., Grutter, M., Ohyama, H., Sun, Y., Butz, A., Mengistu Tsidu, G., Ene, D., Wunch, D., Cao, Z., Garcia, O., Ramonet, M., Vogel, F., and Orphal, J.: Building the COllaborative Carbon Column Observing Network (COCCON): long-term stability and ensemble performance of the EM27/SUN Fourier transform spectrometer, Atmos. Meas. Tech., 12, 1513–1530, https://doi.org/10.5194/amt-12-1513-2019, 2019. 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
Ganesan, A. L., Rigby, M., Lunt, M. F., Parker, R. J., Boesch, H., Goulding, N., Umezawa, T., Zahn, A., Chatterjee, A., Prinn, R. G., Tiwari, Y. K., van der Schoot, M., and Krummel, P. B.: Atmospheric observations show accurate reporting and little growth in India’s methane emissions, Nature Communications, 8, 836, https://doi.org/10.1038/s41467-017-00994-7, 2017. a
Gelman, A. and Rubin, D. B.: Inference from Iterative Simulation Using Multiple Sequences, Statistical Science, 7, 457–472, https://doi.org/10.1214/ss/1177011136, 1992. a
Houweling, S., Krol, M., Bergamaschi, P., Frankenberg, C., Dlugokencky, E. J., Morino, I., Notholt, J., Sherlock, V., Wunch, D., Beck, V., Gerbig, C., Chen, H., Kort, E. A., Röckmann, T., and Aben, I.: A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements, Atmos. Chem. Phys., 14, 3991–4012, https://doi.org/10.5194/acp-14-3991-2014, 2014. a
Houweling, S., Bergamaschi, P., Chevallier, F., Heimann, M., Kaminski, T., Krol, M., Michalak, A. M., and Patra, P.: Global inverse modeling of CH4 sources and sinks: an overview of methods, Atmos. Chem. Phys., 17, 235–256, https://doi.org/10.5194/acp-17-235-2017, 2017. a
Hoffman, M. D. and Gelman, A.: The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo, The Journal of Machine Learning Research, 15, 1593–1623, https://doi.org/10.48550/arXiv.1111.4246, 2014. a
Hu, H., Hasekamp, O., Butz, A., Galli, A., Landgraf, J., Aan de Brugh, J., Borsdorff, T., Scheepmaker, R., and Aben, I.: The operational methane retrieval algorithm for TROPOMI, Atmos. Meas. Tech., 9, 5423–5440, https://doi.org/10.5194/amt-9-5423-2016, 2016. a
Hu, L., Andrews, A. E., Montzka, S. A., Miller, S. M., Bruhwiler, L., Oh, Y., Sweeney, C., Miller, J. B., McKain, K., Ibarra Espinosa, S., Davis, K., Miles, N., Mountain, M., Lan, X., Crotwell, A., Madronich, M., Mefford, T., Michel, S., and Houwelling, S.: An Unexpected Seasonal Cycle in U.S. Oil and Gas Methane Emissions, Environmental Science & Technology, https://doi.org/10.1021/acs.est.4c14090, 2025. a
Hugelius, G., Ramage, J., Burke, E., Chatterjee, A., Smallman, T. L., Aalto, T., Bastos, A., Biasi, C., Canadell, J. G., Chandra, N., Chevallier, F., Ciais, P., Chang, J., Feng, L., Jones, M. W., Kleinen, T., Kuhn, M., Lauerwald, R., Liu, J., López-Blanco, E., Luijkx, I. T., Marushchak, M. E., Natali, S. M., Niwa, Y., Olefeldt, D., Palmer, P. I., Patra, P. K., Peters, W., Potter, S., Poulter, B., Rogers, B. M., Riley, W. J., Saunois, M., Schuur, E. a. G., Thompson, R. L., Treat, C., Tsuruta, A., Turetsky, M. R., Virkkala, A.-M., Voigt, C., Watts, J., Zhu, Q., and Zheng, B.: Permafrost Region Greenhouse Gas Budgets Suggest a Weak CO2 Sink and CH4 and N2O Sources, But Magnitudes Differ Between Top-Down and Bottom-Up Methods, Global Biogeochemical Cycles, 38, https://doi.org/10.1029/2023GB007969, 2024. a
Hutchison, K. D. and Cracknell, A. P.: Visible Infrared Imager Radiometer Suite: A New Operational Cloud Imager, CRC Press, Boca Raton, ISBN 978-0-429-11856-2, https://doi.org/10.1201/9781420023398, 2005. a
IPCC: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, p. 755, https://doi.org/10.1017/9781009157964, 2019. a
Ishizawa, M., Chan, D., Worthy, D., Chan, E., Vogel, F., and Maksyutov, S.: Analysis of atmospheric CH4 in Canadian Arctic and estimation of the regional CH4 fluxes, Atmos. Chem. Phys., 19, 4637–4658, https://doi.org/10.5194/acp-19-4637-2019, 2019. a
Jacob, D. J., Turner, A. J., Maasakkers, J. D., Sheng, J., Sun, K., Liu, X., Chance, K., Aben, I., McKeever, J., and Frankenberg, C.: Satellite observations of atmospheric methane and their value for quantifying methane emissions, Atmos. Chem. Phys., 16, 14371–14396, https://doi.org/10.5194/acp-16-14371-2016, 2016. a
Jensen, K. and Mcdonald, K.: Surface Water Microwave Product Series Version 3: A Near-Real Time and 25-Year Historical Global Inundated Area Fraction Time Series From Active and Passive Microwave Remote Sensing, IEEE Geoscience and Remote Sensing Letters, 16, 1402–1406, https://doi.org/10.1109/LGRS.2019.2898779, 2019. a
Jones, A., Thomson, D., Hort, M., and Devenish, B.: The U.K. Met Office's Next-Generation Atmospheric Dispersion Model, NAME III, in: Air Pollution Modeling and Its Application XVII, edited by: Borrego, C. and Norman, A.-L., Springer US, Boston, MA, 580–589, 2007. a
Kittler, F., Heimann, M., Kolle, O., Zimov, N., Zimov, S., and Göckede, M.: Long-Term Drainage Reduces CO2 Uptake and CH4 Emissions in a Siberian Permafrost Ecosystem: Drainage impact on Arctic carbon cycle, Global Biogeochemical Cycles, 31, 1704–1717, https://doi.org/10.1002/2017GB005774, 2017. a, b
Lan, X. and Dlugokencky, E. J.: Trends in globally-averaged CH4, N2O, and SF6 determined from NOAA Global Monitoring Laboratory measurements, Version 2024-07, NOAA Global Monitoring Laboratory (GML), https://doi.org/10.15138/P8XG-AA10, 2022. a
Lan, X., Nisbet, E. G., Dlugokencky, E. J., and Michel, S. E.: What do we know about the global methane budget? Results from four decades of atmospheric CH4 observations and the way forward, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379, https://doi.org/10.1098/rsta.2020.0440, 2021. a
Laurila, T.: Atmospheric CH4 product, Tiksi (10.0 m), 2010-09-01–2022-04-09, European ObsPack, https://hdl.handle.net/11676/pQuLCZ0dgFVBHp0ZiSHB4vzm (last access: 10 October 2024), 2024. a
Lindqvist, H., Kivimäki, E., Häkkilä, T., Tsuruta, A., Schneising, O., Buchwitz, M., Lorente, A., Martinez Velarte, M., Borsdorff, T., Alberti, C., Backman, L., Buschmann, M., Chen, H., Dubravica, D., Hase, F., Heikkinen, P., Karppinen, T., Kivi, R., McGee, E., Notholt, J., Rautiainen, K., Roche, S., Simpson, W., Strong, K., Tu, Q., Wunch, D., Aalto, T., and Tamminen, J.: Evaluation of Sentinel-5P TROPOMI Methane Observations at Northern High Latitudes, Remote Sensing, 16, 2979, https://doi.org/10.3390/rs16162979, 2024. a, b, c
Lorente, A., Borsdorff, T., Butz, A., Hasekamp, O., aan de Brugh, J., Schneider, A., Wu, L., Hase, F., Kivi, R., Wunch, D., Pollard, D. F., Shiomi, K., Deutscher, N. M., Velazco, V. A., Roehl, C. M., Wennberg, P. O., Warneke, T., and Landgraf, J.: Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements, Atmos. Meas. Tech., 14, 665–684, https://doi.org/10.5194/amt-14-665-2021, 2021. a, b, c, d
Lorente, A., Borsdorff, T., Martinez-Velarte, M. C., and Landgraf, J.: Accounting for surface reflectance spectral features in TROPOMI methane retrievals, Atmos. Meas. Tech., 16, 1597–1608, https://doi.org/10.5194/amt-16-1597-2023, 2023. a, b, c, d
Lu, X., Jacob, D. J., Wang, H., Maasakkers, J. D., Zhang, Y., Scarpelli, T. R., Shen, L., Qu, Z., Sulprizio, M. P., Nesser, H., Bloom, A. A., Ma, S., Worden, J. R., Fan, S., Parker, R. J., Boesch, H., Gautam, R., Gordon, D., Moran, M. D., Reuland, F., Villasana, C. A. O., and Andrews, A.: Methane emissions in the United States, Canada, and Mexico: evaluation of national methane emission inventories and 2010–2017 sectoral trends by inverse analysis of in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric observations, Atmos. Chem. Phys., 22, 395–418, https://doi.org/10.5194/acp-22-395-2022, 2022. a
Lunt, M. F., Palmer, P. I., Feng, L., Taylor, C. M., Boesch, H., and Parker, R. J.: An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data, Atmos. Chem. Phys., 19, 14721–14740, https://doi.org/10.5194/acp-19-14721-2019, 2019. a
Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Scarpelli, T. R., Nesser, H., Sheng, J.-X., Zhang, Y., Hersher, M., Bloom, A. A., Bowman, K. W., Worden, J. R., Janssens-Maenhout, G., and Parker, R. J.: Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015, Atmos. Chem. Phys., 19, 7859–7881, https://doi.org/10.5194/acp-19-7859-2019, 2019. a, b
Mastepanov, M., Sigsgaard, C., Dlugokencky, E. J., Houweling, S., Ström, L., Tamstorf, M. P., and Christensen, T. R.: Large tundra methane burst during onset of freezing, Nature, 456, 628–630, https://doi.org/10.1038/nature07464, 2008. a, b
Narbaud, C., Paris, J.-D., Wittig, S., Berchet, A., Saunois, M., Nédélec, P., Belan, B. D., Arshinov, M. Y., Belan, S. B., Davydov, D., Fofonov, A., and Kozlov, A.: Disentangling methane and carbon dioxide sources and transport across the Russian Arctic from aircraft measurements, Atmos. Chem. Phys., 23, 2293–2314, https://doi.org/10.5194/acp-23-2293-2023, 2023. a
Neal, R. M.: Slice sampling, The Annals of Statistics, 31, 705–767, https://doi.org/10.1214/aos/1056562461, 2003. a
Nesser, H., Jacob, D. J., Maasakkers, J. D., Lorente, A., Chen, Z., Lu, X., Shen, L., Qu, Z., Sulprizio, M. P., Winter, M., Ma, S., Bloom, A. A., Worden, J. R., Stavins, R. N., and Randles, C. A.: High-resolution US methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills, Atmos. Chem. Phys., 24, 5069–5091, https://doi.org/10.5194/acp-24-5069-2024, 2024. a
O'Dell, C. W., Connor, B., Bösch, H., O'Brien, D., Frankenberg, C., Castano, R., Christi, M., Eldering, D., Fisher, B., Gunson, M., McDuffie, J., Miller, C. E., Natraj, V., Oyafuso, F., Polonsky, I., Smyth, M., Taylor, T., Toon, G. C., Wennberg, P. O., and Wunch, D.: The ACOS CO2 retrieval algorithm – Part 1: Description and validation against synthetic observations, Atmos. Meas. Tech., 5, 99–121, https://doi.org/10.5194/amt-5-99-2012, 2012. a
Ostler, A., Sussmann, R., Rettinger, M., Deutscher, N. M., Dohe, S., Hase, F., Jones, N., Palm, M., and Sinnhuber, B.-M.: Multistation intercomparison of column-averaged methane from NDACC and TCCON: impact of dynamical variability, Atmos. Meas. Tech., 7, 4081–4101, https://doi.org/10.5194/amt-7-4081-2014, 2014. a
Ostler, A., Sussmann, R., Patra, P. K., Houweling, S., De Bruine, M., Stiller, G. P., Haenel, F. J., Plieninger, J., Bousquet, P., Yin, Y., Saunois, M., Walker, K. A., Deutscher, N. M., Griffith, D. W. T., Blumenstock, T., Hase, F., Warneke, T., Wang, Z., Kivi, R., and Robinson, J.: Evaluation of column-averaged methane in models and TCCON with a focus on the stratosphere, Atmos. Meas. Tech., 9, 4843–4859, https://doi.org/10.5194/amt-9-4843-2016, 2016. a
Outcalt, S. I., Nelson, F. E., and Hinkel, K. M.: The zero‐curtain effect: Heat and mass transfer across an isothermal region in freezing soil, Water Resources Research, 26, 1509–1516, https://doi.org/10.1029/WR026i007p01509, 1990. a
Pandey, S., Houweling, S., Krol, M., Aben, I., Chevallier, F., Dlugokencky, E. J., Gatti, L. V., Gloor, E., Miller, J. B., Detmers, R., Machida, T., and Röckmann, T.: Inverse modeling of GOSAT-retrieved ratios of total column CH4 and CO2 for 2009 and 2010, Atmos. Chem. Phys., 16, 5043–5062, https://doi.org/10.5194/acp-16-5043-2016, 2016. a
Paris, J.-D., Belan, B., Ancellet, G., Nédélec, P., Arshinov, M., Pruvost, A., Berchet, A., Arzoumanian, E., Pison, I., Ciais, P., and Law, K.: YAK-AEROSIB: continental scale aircraft measurements of trace gases gases above Siberia, 2014. a
Parker, R., Boesch, H., Cogan, A., Fraser, A., Feng, L., Palmer, P. I., Messerschmidt, J., Deutscher, N., Griffith, D. W. T., Notholt, J., Wennberg, P. O., and Wunch, D.: Methane observations from the Greenhouse Gases Observing SATellite: Comparison to ground-based TCCON data and model calculations, Geophysical Research Letters, 38, https://doi.org/10.1029/2011GL047871, 2011. a
Qu, Z., Jacob, D. J., Shen, L., Lu, X., Zhang, Y., Scarpelli, T. R., Nesser, H., Sulprizio, M. P., Maasakkers, J. D., Bloom, A. A., Worden, J. R., Parker, R. J., and Delgado, A. L.: Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments, Atmos. Chem. Phys., 21, 14159–14175, https://doi.org/10.5194/acp-21-14159-2021, 2021. a
Randerson, J., van der Werf, G., Giglio, L., Collatz, G., and Kasibhatla, P.: Global Fire Emissions Database, Version 4.1 (GFEDv4), ORNL Distributed Active Archive Center [data set], https://doi.org/10.3334/ORNLDAAC/1293, 2017. a
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: The Arctic has warmed nearly four times faster than the globe since 1979, Communications Earth & Environment, 3, 1–10, https://doi.org/10.1038/s43247-022-00498-3, 2022. a
Saunois, M., Stavert, A. R., Poulter, B., Bousquet, P., Canadell, J. G., Jackson, R. B., Raymond, P. A., Dlugokencky, E. J., Houweling, S., Patra, P. K., Ciais, P., Arora, V. K., Bastviken, D., Bergamaschi, P., Blake, D. R., Brailsford, G., Bruhwiler, L., Carlson, K. M., Carrol, M., Castaldi, S., Chandra, N., Crevoisier, C., Crill, P. M., Covey, K., Curry, C. L., Etiope, G., Frankenberg, C., Gedney, N., Hegglin, M. I., Höglund-Isaksson, L., Hugelius, G., Ishizawa, M., Ito, A., Janssens-Maenhout, G., Jensen, K. M., Joos, F., Kleinen, T., Krummel, P. B., Langenfelds, R. L., Laruelle, G. G., Liu, L., Machida, T., Maksyutov, S., McDonald, K. C., McNorton, J., Miller, P. A., Melton, J. R., Morino, I., Müller, J., Murguia-Flores, F., Naik, V., Niwa, Y., Noce, S., O'Doherty, S., Parker, R. J., Peng, C., Peng, S., Peters, G. P., Prigent, C., Prinn, R., Ramonet, M., Regnier, P., Riley, W. J., Rosentreter, J. A., Segers, A., Simpson, I. J., Shi, H., Smith, S. J., Steele, L. P., Thornton, B. F., Tian, H., Tohjima, Y., Tubiello, F. N., Tsuruta, A., Viovy, N., Voulgarakis, A., Weber, T. S., van Weele, M., van der Werf, G. R., Weiss, R. F., Worthy, D., Wunch, D., Yin, Y., Yoshida, Y., Zhang, W., Zhang, Z., Zhao, Y., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q.: The Global Methane Budget 2000–2017, Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, 2020. a
Schneising, O., Buchwitz, M., Reuter, M., Bovensmann, H., Burrows, J. P., Borsdorff, T., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Hermans, C., Iraci, L. T., Kivi, R., Landgraf, J., Morino, I., Notholt, J., Petri, C., Pollard, D. F., Roche, S., Shiomi, K., Strong, K., Sussmann, R., Velazco, V. A., Warneke, T., and Wunch, D.: A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor, Atmos. Meas. Tech., 12, 6771–6802, https://doi.org/10.5194/amt-12-6771-2019, 2019. a, b, c
Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm, Atmos. Meas. Tech., 16, 669–694, https://doi.org/10.5194/amt-16-669-2023, 2023. a, b, c
Schuldt, K. N., Aalto, T., Andrade, M., Arlyn Andrews, Apadula, F., Jgor Arduini, Arnold, S., Baier, B., Bäni, L., Bartyzel, J., Bergamaschi, P., Biermann, T., Biraud, S. C., Pierre-Eric Blanc, Boenisch, H., Brailsford, G., Brand, W. A., Brunner, D., Bui, T. P. V., Van Den Bulk, P., Benoit Burban, Francescopiero Calzolari, Chang, C. S., Huilin Chen, Lukasz Chmura, St. Clair, J. M., Sites Climadat, Coletta, J. D., Colomb, A., 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., Ankur Desai, DiGangi, E., DiGangi, J. P., Elsasser, M., Emmenegger, L., Forster, G., Frumau, A., Fuente-Lastra, M., Galkowski, M., Gatti, L. V., Gehrlein, T., Gerbig, C., Francois Gheusi, Gloor, E., Goto, D., Hammer, S., Hanisco, T. F., Haszpra, L., Hatakka, J., Heimann, M., Heliasz, M., Heltai, D., Henne, S., Hensen, A., Hermans, C., Hermansen, O., Hoheisel, A., Holst, J., Di Iorio, T., Iraci, L. T., Ivakhov, V., Jaffe, D. A., Jordan, A., Joubert, W., Kang, H.-Y., Karion, A., Kazan, V., Keeling, R. F., Keronen, P., Kers, B., Jooil Kim, 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., Lan, X., Langenfelds, R. L., Lanza, A., Laurent, O., Laurila, T., Lauvaux, T., Lavric, J., Choong-Hoon Lee, Lee, J., Lehner, I., Lehtinen, K., Leppert, R., Leskinen, A., Leuenberger, M., Levin, I., Levula, J., Lindauer, M., Lindroth, A., Mikaell Ottosson Löfvenius, Loh, Z. M., Lopez, M., Lowry, D., Lunder, C. R., Machida, T., Mammarella, I., Manca, G., Manning, A., Marek, M. V., Marklund, P., Marrero, J. E., Martin, D., Martin, M. Y., Giordane A. Martins, Matsueda, H., De Mazière, M., McKain, K., Meinhardt, F., Menoud, M., Jean-Marc Metzger, Miles, N. L., Miller, C. E., Miller, J. B., Mölder, M., Monteiro, V., Montzka, S., Moore, F., Moossen, H., Moreno, C., Morgan, E., Josep-Anton Morgui, Morimoto, S., Müller-Williams, J., Munro, D., Mutuku, M., Myhre, C. L., Jaroslaw Necki, Nichol, S., Nisbet, E., Niwa, Y., Njiru, D. M., Noe, S. M., O'Doherty, S., Obersteiner, F., Parworth, C. L., Peltola, O., Peters, W., Philippon, C., Piacentino, S., Pichon, J. M., Pickers, P., Pitt, J., Pittman, J., Plass-Dülmer, C., Platt, S. M., Popa, M. E., Prinzivalli, S., Ramonet, M., Richardson, S. J., Louis-Jeremy Rigouleau, Rivas, P. P., Röckmann, T., Rothe, M., Yves-Alain Roulet, Ju-Mee Ryoo, Santoni, G., Di Sarra, A. G., Sasakawa, M., Schaefer, H., Scheeren, B., Schmidt, M., Schuck, T., Schumacher, M., Seifert, T., Sha, M. K., Shepson, P., Daegeun Shin, Sloop, C. D., Smale, 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., Thoning, K., Timas, H., Torn, M., Trisolino, P., Turnbull, J., Van Der Veen, C., Vermeulen, A., Viner, B., Vitkova, G., De Vries, M., Watson, A., Weiss, R., Weyrauch, D., Wofsy, S. C., Worsey, J., Worthy, D., Xueref-Remy, I., Yates, E. L., Dickon Young, Yver-Kwok, C., Zaehle, S., Zahn, A., Zazzeri, G., Zellweger, C., and Zimnoch, M.: Multi-laboratory compilation of atmospheric carbon dioxide data for the period 1983–2023; obspack_ch4_1_GLOBALVIEWplus_v7.0_2024-10-29, NOAA Global Monitoring Laboratory (GML) [data set], https://doi.org/10.25925/20241001, 2024. a
Schuur, E. A. G., Pallandt, M., and Göckede, M.: Russian collaboration loss risks permafrost carbon emissions network, Nature Climate Change, 1–2, https://doi.org/10.1038/s41558-024-02001-6, 2024. a
Segers, A. and Houweling, S.: CAMS global inversion-optimised greenhouse gas fluxes and concentrations, version v19r1, Atmosphere Data Store (ADS), https://doi.org/10.24381/ed2851d2, 2020. a, b
Sweeney, C., Dlugokencky, E., Miller, C. E., Wofsy, S., Karion, A., Dinardo, S., Chang, R. Y.-W., Miller, J. B., Bruhwiler, L., Crotwell, A. M., Newberger, T., McKain, K., Stone, R. S., Wolter, S. E., Lang, P. E., and Tans, P.: No significant increase in long-term CH4 emissions on North Slope of Alaska despite significant increase in air temperature, Geophysical Research Letters, 43, 6604–6611, https://doi.org/10.1002/2016GL069292, 2016. a, b
Tenkanen, M., Tsuruta, A., Rautiainen, K., Kangasaho, V., Ellul, R., and Aalto, T.: Utilizing Earth Observations of Soil Freeze/Thaw Data and Atmospheric Concentrations to Estimate Cold Season Methane Emissions in the Northern High Latitudes, Remote Sensing, 13, https://doi.org/10.3390/rs13245059, 2021. a, b, c
Thompson, R. L., Sasakawa, M., Machida, T., Aalto, T., Worthy, D., Lavric, J. V., Lund Myhre, C., and Stohl, A.: Methane fluxes in the high northern latitudes for 2005–2013 estimated using a Bayesian atmospheric inversion, Atmos. Chem. Phys., 17, 3553–3572, https://doi.org/10.5194/acp-17-3553-2017, 2017. a
Thompson, R. L., Krishnankutty, N., Pisso, I., Schneider, P., Stebel, K., Sasakawa, M., Stohl, A., and Platt, S. M.: Efficient use of a Lagrangian particle dispersion model for atmospheric inversions using satellite observations of column mixing ratios, Atmos. Chem. Phys., 25, 12737–12751, https://doi.org/10.5194/acp-25-12737-2025, 2025. a, b, c, d, e, f, g
Thornton, B. F., Wik, M., and Crill, P. M.: Double-counting challenges the accuracy of high-latitude methane inventories, Geophysical Research Letters, 43, https://doi.org/10.1002/2016GL071772, 2016. a
Treat, C. C., Bloom, A. A., and Marushchak, M. E.: Nongrowing season methane emissions–a significant component of annual emissions across northern ecosystems, Global Change Biology, 24, 3331–3343, https://doi.org/10.1111/gcb.14137, 2018. a, b, c
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 Sensing, 15, 1620, https://doi.org/10.3390/rs15061620, 2023. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Tsuruta, A., Kuze, A., Shiomi, K., Kataoka, F., Kikuchi, N., Aalto, T., Backman, L., Kivimäki, E., Tenkanen, M. K., McKain, K., García, O. E., Hase, F., Kivi, R., Morino, I., Ohyama, H., Pollard, D. F., Sha, M. K., Strong, K., Sussmann, R., Te, Y., Velazco, V. A., Vrekoussis, M., Warneke, T., Zhou, M., and Suto, H.: Global CH4 fluxes derived from JAXA/GOSAT lower-tropospheric partial column data and the CarbonTracker Europe-CH4 atmospheric inverse model, Atmos. Chem. Phys., 25, 7829–7862, https://doi.org/10.5194/acp-25-7829-2025, 2025. a
Turner, A. J., Jacob, D. J., Wecht, K. J., Maasakkers, J. D., Lundgren, E., Andrews, A. E., Biraud, S. C., Boesch, H., Bowman, K. W., Deutscher, N. M., Dubey, M. K., Griffith, D. W. T., Hase, F., Kuze, A., Notholt, J., Ohyama, H., Parker, R., Payne, V. H., Sussmann, R., Sweeney, C., Velazco, V. A., Warneke, T., Wennberg, P. O., and Wunch, D.: Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data, Atmos. Chem. Phys., 15, 7049–7069, https://doi.org/10.5194/acp-15-7049-2015, 2015. a, b
Turner, A. J., Frankenberg, C., and Kort, E. A.: Interpreting contemporary trends in atmospheric methane, Proceedings of the National Academy of Sciences, 116, 2805–2813, https://doi.org/10.1073/pnas.1814297116, 2019. a
U.S. Energy Information Administration: Alaska Natural Gas Gross Withdrawals (Million Cubic Feet), https://www.eia.gov/dnav/ng/hist/n9010ak2M.htm (last access: 21 May 2025), 2025. a
Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K., and Zerroukat, M.: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, 2019. a
Wang, F., Maksyutov, S., Tsuruta, A., Janardanan, R., Ito, A., Sasakawa, M., Machida, T., Morino, I., Yoshida, Y., Kaiser, J. W., Janssens-Maenhout, G., Dlugokencky, E. J., Mammarella, I., Lavric, J. V., and Matsunaga, T.: Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories, Remote Sensing, 11, 2489, https://doi.org/10.3390/rs11212489, 2019. a
Ward, R. H., Sweeney, C., Miller, J. B., Goeckede, M., Laurila, T., Hatakka, J., Ivakov, V., Sasakawa, M., Machida, T., Morimoto, S., Goto, D., and Ganesan, A. L.: Increasing Methane Emissions and Widespread Cold-Season Release From High-Arctic Regions Detected Through Atmospheric Measurements, Journal of Geophysical Research: Atmospheres, 129, https://doi.org/10.1029/2024JD040766, 2024. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u
Ward, R. H.: CH4 emissions estimates from “Uncertainty and retrieval sensitivity in TROPOMI-based methane inversions over the North Slope of Alaska”, Zenodo [data set], https://doi.org/10.5281/zenodo.16630729, 2025. a
Weber, T., Wiseman, N. A., and Kock, A.: Global ocean methane emissions dominated by shallow coastal waters, Nature Communications, 10, 4584, https://doi.org/10.1038/s41467-019-12541-7, 2019. 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
Wittig, S., Berchet, A., Pison, I., Saunois, M., and Paris, J.-D.: Surface networks in the Arctic may miss a future methane bomb, Atmos. Chem. Phys., 24, 6359–6373, https://doi.org/10.5194/acp-24-6359-2024, 2024. a
Wunch, D., Toon, G. C., Blavier, J.-F. L., Washenfelder, R. A., Notholt, J., Connor, B. J., Griffith, D. W. T., Sherlock, V., and Wennberg, P. O.: The Total Carbon Column Observing Network, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369, 2087–2112, https://doi.org/10.1098/rsta.2010.0240, 2011. a, b
Zhang, Y., Jacob, D. J., Lu, X., Maasakkers, J. D., Scarpelli, T. R., Sheng, J.-X., Shen, L., Qu, Z., Sulprizio, M. P., Chang, J., Bloom, A. A., Ma, S., Worden, J., Parker, R. J., and Boesch, H.: Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations, Atmos. Chem. Phys., 21, 3643–3666, https://doi.org/10.5194/acp-21-3643-2021, 2021. a
Zhao, Y., Saunois, M., Bousquet, P., Lin, X., Berchet, A., Hegglin, M. I., Canadell, J. G., Jackson, R. B., Dlugokencky, E. J., Langenfelds, R. L., Ramonet, M., Worthy, D., and Zheng, B.: Influences of hydroxyl radicals (OH) on top-down estimates of the global and regional methane budgets, Atmos. Chem. Phys., 20, 9525–9546, https://doi.org/10.5194/acp-20-9525-2020, 2020. a
Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S. C., Miller, C. E., Dinardo, S. J., Dengel, S., Sweeney, C., Karion, A., Chang, R. Y.-W., Henderson, J. M., Murphy, P. C., Goodrich, J. P., Moreaux, V., Liljedahl, A., Watts, J. D., Kimball, J. S., Lipson, D. A., and Oechel, W. C.: Cold season emissions dominate the Arctic tundra methane budget, Proceedings of the National Academy of Sciences, 113, 40–45, https://doi.org/10.1073/pnas.1516017113, 2016. a, b, c, d, e, f
Short summary
We studied methane emissions in Arctic Alaska using satellite observations to assess how well they can monitor this important greenhouse gas. We found that emission estimates varied depending on the satellite data product and were strongly affected by assumptions in the model. Our results highlight the need for careful interpretation of emissions from Arctic satellite data and thorough testing of models, with implications for reliable climate monitoring.
We studied methane emissions in Arctic Alaska using satellite observations to assess how well...
