Articles | Volume 23, issue 5
https://doi.org/10.5194/acp-23-3325-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-3325-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Mar 2023
Research article |
| 17 Mar 2023
| 17 Mar 2023
A high-resolution satellite-based map of global methane emissions reveals missing wetland, fossil fuel, and monsoon sources
Xueying Yu
Department of Soil, Water, and Climate, University of Minnesota, Saint
Paul, MN 55108, USA
Department of Earth System Science, Stanford University, Palo Alto,
CA 94305, USA
Department of Soil, Water, and Climate, University of Minnesota, Saint
Paul, MN 55108, USA
Daven K. Henze
Department of Mechanical Engineering, University of Colorado, Boulder,
CO 80309, USA
Alexander J. Turner
Department of Atmospheric Sciences, University of Washington, Seattle,
DC 98195, USA
Alba Lorente Delgado
Earth Science Group, SRON Netherlands Institute for Space Research,
Leiden, the Netherlands
A. Anthony Bloom
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Jianxiong Sheng
Center for Global Change Science, Massachusetts Institute of
Technology, Cambridge, MA 02139, USA
Related authors
No articles found.
James Young Suk Yoon, Kelley C. Wells, Dylan B. Millet, Christian Frankenberg, Suniti Sanghavi, Abigail L. S. Swann, Joel A. Thornton, and Alexander J. Turner
Atmos. Chem. Phys., 26, 4509–4529, https://doi.org/10.5194/acp-26-4509-2026, https://doi.org/10.5194/acp-26-4509-2026, 2026
Short summary
Short summary
Isoprene is a molecule emitted by trees that is oxidized in the atmosphere within hours. Much of the isoprene globally is emitted in the remote tropics, where we have few direct observations of isoprene. Here, we use new satellite retrievals of isoprene to infer drivers of tropical isoprene variability. Across three regions, isoprene column variability is controlled by different factors, namely changes in isoprene emissions or changes in natural nitrogen oxide sources, like soils and fires.
Gunnar Myhre, Øivind Hodnebrog, Srinath Krishnan, Maria Sand, Marit Sandstad, Ragnhild B. Skeie, Lieven Clarisse, Bruno Franco, Dylan B. Millet, Kelley C. Wells, Alexander Archibald, Hannah N. Bryant, Alex T. Chaudhri, David S. Stevenson, Didier Hauglustaine, Michael Prather, J. Christopher Kaiser, Dirk J. L. Olivie, Michael Schulz, Oliver Wild, Ye Wang, Thérèse Salameh, Jason E. Williams, Philippe Le Sager, Fabien Paulot, Kostas Tsigaridis, and Haley E. Plaas
Geosci. Model Dev., 19, 2577–2591, https://doi.org/10.5194/gmd-19-2577-2026, https://doi.org/10.5194/gmd-19-2577-2026, 2026
Short summary
Short summary
Volatile organic compounds (VOCs) affect air quality and climate, but their behavior in the atmosphere is still uncertain. We launched a global research effort to compare how different models represent these compounds and to improve their accuracy. By analyzing model results alongside observations and satellite data, we aim to better understand the atmospheric composition of these compounds.
Fei Yao, Paul I. Palmer, Xiaolin Wang, Yi Wang, Gitaek T. Lee, Haolin Wang, Liang Feng, Daven K. Henze, and Rokjin J. Park
EGUsphere, https://doi.org/10.5194/egusphere-2026-1499, https://doi.org/10.5194/egusphere-2026-1499, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We assess the added value of GEMS geostationary satellite observations of tropospheric NO2 to constrain NOx emission estimates and subsequent air quality modelling over a full annual cycle across Asia. We observe a clear seasonal divide, with significant benefits outside summer while limited or even detrimental impacts during summer. Our findings clarify when and how high frequency geostationary data can be most effectively used to improve our understanding of atmospheric composition.
Xin Chen, Dylan B. Millet, Glenn M. Wolfe, Erin R. Delaria, M. Julian Deventer, Markus Müller, Arne Schiller, Kenneth Lee Thornhill, and Armin Wisthaler
EGUsphere, https://doi.org/10.5194/egusphere-2026-976, https://doi.org/10.5194/egusphere-2026-976, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Air-sea exchange is a major uncertainty source for many volatile organic compounds (VOCs). Aircraft-based eddy covariance can be used to quantify these fluxes over large regions but such applications have been limited. We used observations over the Atlantic to characterize VOC fluxes and elucidate the drivers of measurement error. Results show how sensor noise and turbulent variability interact to determine flux detectability, and define instrumental priorities for future use of this technique.
Raphaël Savelli, Dustin Carroll, Dimitris Menemenlis, Jonathan M. Lauderdale, Clément Bertin, Stephanie Dutkiewicz, Manfredi Manizza, A. Anthony Bloom, Karel Castro-Morales, Charles E. Miller, Marc Simard, Kevin W. Bowman, and Hong Zhang
Geosci. Model Dev., 19, 867–885, https://doi.org/10.5194/gmd-19-867-2026, https://doi.org/10.5194/gmd-19-867-2026, 2026
Short summary
Short summary
Accounting for carbon and nutrients in rivers is essential for resolving carbon dioxide (CO2) exchanges between the ocean and the atmosphere. In this study, we add the effect of present-day rivers to a pioneering global-ocean biogeochemistry model. This study highlights the challenge for global ocean numerical models to cover the complexity of the flow of water and carbon across the Land-to-Ocean Aquatic Continuum.
Nicholas Balasus, Daniel J. Jacob, A. Anthony Bloom, James D. East, Lucas A. Estrada, Sarah E. Hancock, Megan He, Todd A. Mooring, Alexander J. Turner, and John R. Worden
EGUsphere, https://doi.org/10.5194/egusphere-2025-6251, https://doi.org/10.5194/egusphere-2025-6251, 2026
Short summary
Short summary
We use satellite observations of methane to determine the amount of methane coming from Africa and relate this to the physical processes responsible, with relevance for climate change given methane's potency as a greenhouse gas. We find that the amount of methane coming from Africa has increased by a third across 2019–2024, driven primarily by steady increases in emissions from livestock, in addition to irregular surges from wetlands.
Nikhil Dadheech and Alexander J. Turner
Atmos. Chem. Phys., 26, 427–441, https://doi.org/10.5194/acp-26-427-2026, https://doi.org/10.5194/acp-26-427-2026, 2026
Short summary
Short summary
We developed a generalized emulator of atmospheric transport (FootNet v3) trained over the United States, enabling the emulation of both surface & column-averaged footprints at kilometer-scale resolution. We demonstrate that FootNet v3 generalizes to previously unseen regions and meteorological conditions, enabling accurate out-of-sample simulation of atmospheric transport. Flux inversion case studies show that FootNet matches or exceeds the performance of full-physics models in unseen regions.
Hui Li, Philippe Ciais, Pramod Kumar, Didier A. Hauglustaine, Frédéric Chevallier, Grégoire Broquet, Dylan B. Millet, Kelley C. Wells, Jinghui Lian, and Bo Zheng
Earth Syst. Sci. Data, 17, 7035–7054, https://doi.org/10.5194/essd-17-7035-2025, https://doi.org/10.5194/essd-17-7035-2025, 2025
Short summary
Short summary
We present the first global, multi-year maps of monthly isoprene emissions (2013–2020) derived from satellite isoprene observations, averaging 456 TgC yr-1. The dataset reveals two emission peaks linked to 2015–2016 El Niño and 2019–2020 extreme heat events, driven mainly by tropical regions such as the Amazon. It highlights the region-specific sensitivity of biogenic isoprene emissions to temperature anomalies, providing new insights into their roles in air quality and climate feedbacks.
Zhaojun Tang, Panpan Yang, Kazuyuki Miyazaki, John Worden, Helen Worden, Daven K. Henze, Dylan B. A. Jones, and Zhe Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2025-5432, https://doi.org/10.5194/egusphere-2025-5432, 2025
Short summary
Short summary
We provide a quantitative analysis of global CO emissions and drivers of atmospheric CO trends in 2003–2022, using GEOS-Chem adjoint model constrained by MOPITT satellite observations. Our results indicate a substantial decline in global anthropogenic CO emissions of 14–17 % over the two-decade period, as well as an important offsetting effect (by 37 % globally) from rising wildfire emissions on atmospheric CO decline driven by anthropogenic reductions.
Eric J. Mei, Gregory J. Hakim, Max Taniguchi-King, Dominik Stiller, and Alexander J. Turner
Atmos. Chem. Phys., 25, 15033–15045, https://doi.org/10.5194/acp-25-15033-2025, https://doi.org/10.5194/acp-25-15033-2025, 2025
Short summary
Short summary
Chemistry-climate models are used to investigate how physical climate influences the composition of the atmosphere but are slow and expensive to run. We train a linear inverse model that can replicate the behavior of chemistry-climate models at low computational cost. It captures how large-scale climate features like El Niño affect atmospheric composition and can make accurate forecasts up to a year ahead. This model enables fast hypothesis testing and estimates of past atmospheric composition.
Aoxing Zhang, Tzung-May Fu, Yuhang Wang, Enyu Xiong, Wenlu Wu, Yumin Li, Lei Zhu, Wei Tao, Kelley C. Wells, Dylan B. Millet, Zhe Wang, Bin Yuan, Min Shao, Christophe Lerot, Thomas Danckaert, Ruixiong Zhang, and Kelvin H. Bates
EGUsphere, https://doi.org/10.5194/egusphere-2025-5083, https://doi.org/10.5194/egusphere-2025-5083, 2025
Short summary
Short summary
Glyoxal, a product of volatile organic compound oxidation, influences atmospheric oxidation and aerosol formation but is underestimated in models. By improving emissions, chemistry, and marine sources in GEOS-Chem, we better reproduce observed glyoxal over land and ocean, which strengthens global oxidation capacity and aerosol formation. The results highlight glyoxal's role as a proxy of atmospheric oxidation, and emphasize the needs of accurately representing glyoxal chemistry.
Matthew S. Johnson, Sofia D. Hamilton, Seongeun Jeong, Yu Yan Cui, Dien Wu, Alex Turner, and Marc Fischer
Atmos. Chem. Phys., 25, 8475–8492, https://doi.org/10.5194/acp-25-8475-2025, https://doi.org/10.5194/acp-25-8475-2025, 2025
Short summary
Short summary
Satellites, such as NASA's Orbiting Carbon Observatory-2 and -3 (OCO-2 and OCO-3, respectively), retrieve carbon dioxide (CO2) concentrations, which provide vital information for estimating surface CO2 emissions. Here, we investigate the ability of OCO-2/3 retrievals to constrain CO2 emissions for the state of California for the major emission sectors (i.e., fossil fuels, net ecosystem exchange, and wildfire).
Nikhil Dadheech, Tai-Long He, and Alexander J. Turner
Atmos. Chem. Phys., 25, 5159–5174, https://doi.org/10.5194/acp-25-5159-2025, https://doi.org/10.5194/acp-25-5159-2025, 2025
Short summary
Short summary
We developed an efficient GHG (greenhouse gas) flux inversion framework using a machine-learning emulator (FootNet) as a surrogate for an atmospheric transport model, resulting in a 650 × speedup. Paradoxically, the flux inversion using the ML (machine-learning) model outperforms the full-physics model in our case study. We attribute this to the ML model mitigating transport errors in the GHG flux inversion.
Benjamin C. Sapper, Sean Youn, Daven K. Henze, Manjula Canagaratna, Harald Stark, and Jose L. Jimenez
Geosci. Model Dev., 18, 2891–2919, https://doi.org/10.5194/gmd-18-2891-2025, https://doi.org/10.5194/gmd-18-2891-2025, 2025
Short summary
Short summary
Positive matrix factorization (PMF) has been used by atmospheric scientists to extract underlying factors present in large datasets. This paper presents a new technique for error-weighted PMF that drastically reduces the computational costs of previously developed algorithms. We use this technique to deliver interpretable factors and solution diagnostics from an atmospheric chemistry dataset.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
Short summary
Short summary
It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Beata Opacka, Trissevgeni Stavrakou, Jean-François Müller, Isabelle De Smedt, Jos van Geffen, Eloise A. Marais, Rebekah P. Horner, Dylan B. Millet, Kelly C. Wells, and Alex B. Guenther
Atmos. Chem. Phys., 25, 2863–2894, https://doi.org/10.5194/acp-25-2863-2025, https://doi.org/10.5194/acp-25-2863-2025, 2025
Short summary
Short summary
Vegetation releases biogenic volatile organic compounds, while soils and lightning contribute to the natural emissions of nitrogen oxides into the atmosphere. These gases interact in complex ways. Using satellite data and models, we developed a new method to simultaneously optimize these natural emissions over Africa in 2019. Our approach resulted in an increase in natural emissions, supported by independent data indicating that current estimates are underestimated.
Kelley C. Wells, Dylan B. Millet, Jared F. Brewer, Vivienne H. Payne, Karen E. Cady-Pereira, Rick Pernak, Susan Kulawik, Corinne Vigouroux, Nicholas Jones, Emmanuel Mahieu, Maria Makarova, Tomoo Nagahama, Ivan Ortega, Mathias Palm, Kimberly Strong, Matthias Schneider, Dan Smale, Ralf Sussmann, and Minqiang Zhou
Atmos. Meas. Tech., 18, 695–716, https://doi.org/10.5194/amt-18-695-2025, https://doi.org/10.5194/amt-18-695-2025, 2025
Short summary
Short summary
Atmospheric volatile organic compounds (VOCs) affect both air quality and climate. Satellite measurements can help us to assess and predict their global impacts. We present new decadal (2012–2023) measurements of four key VOCs – methanol, ethene, ethyne, and hydrogen cyanide (HCN) – from the Cross-track Infrared Sounder. The measurements reflect emissions from major forests, wildfires, and industry and provide new information to advance understanding of these sources and their changes over time.
Bryan N. Duncan, Daniel C. Anderson, Arlene M. Fiore, Joanna Joiner, Nickolay A. Krotkov, Can Li, Dylan B. Millet, Julie M. Nicely, Luke D. Oman, Jason M. St. Clair, Joshua D. Shutter, Amir H. Souri, Sarah A. Strode, Brad Weir, Glenn M. Wolfe, Helen M. Worden, and Qindan Zhu
Atmos. Chem. Phys., 24, 13001–13023, https://doi.org/10.5194/acp-24-13001-2024, https://doi.org/10.5194/acp-24-13001-2024, 2024
Short summary
Short summary
Trace gases emitted to or formed within the atmosphere may be chemically or physically removed from the atmosphere. One trace gas, the hydroxyl radical (OH), is responsible for initiating the chemical removal of many trace gases, including some greenhouse gases. Despite its importance, scientists have not been able to adequately measure OH. In this opinion piece, we discuss promising new methods to indirectly constrain OH using satellite data of trace gases that control the abundance of OH.
Deepangsu Chatterjee, Randall V. Martin, Chi Li, Dandan Zhang, Haihui Zhu, Daven K. Henze, James H. Crawford, Ronald C. Cohen, Lok N. Lamsal, and Alexander M. Cede
Atmos. Chem. Phys., 24, 12687–12706, https://doi.org/10.5194/acp-24-12687-2024, https://doi.org/10.5194/acp-24-12687-2024, 2024
Short summary
Short summary
We investigate the hourly variation of NO2 columns and surface concentrations by applying the GEOS-Chem model to interpret aircraft and ground-based measurements over the US and Pandora sun photometer measurements over the US, Europe, and Asia. Corrections to the Pandora columns and finer model resolution improve the modeled representation of the summertime hourly variation of total NO2 columns to explain the weaker hourly variation in NO2 columns than at the surface.
Irene C. Dedoussi, Daven K. Henze, Sebastian D. Eastham, Raymond L. Speth, and Steven R. H. Barrett
Geosci. Model Dev., 17, 5689–5703, https://doi.org/10.5194/gmd-17-5689-2024, https://doi.org/10.5194/gmd-17-5689-2024, 2024
Short summary
Short summary
Atmospheric model gradients provide a meaningful tool for better understanding the underlying atmospheric processes. Adjoint modeling enables computationally efficient gradient calculations. We present the adjoint of the GEOS-Chem unified chemistry extension (UCX). With this development, the GEOS-Chem adjoint model can capture stratospheric ozone and other processes jointly with tropospheric processes. We apply it to characterize the Antarctic ozone depletion potential of active halogen species.
Zhendong Lu, Jun Wang, Yi Wang, Daven K. Henze, Xi Chen, Tong Sha, and Kang Sun
Atmos. Chem. Phys., 24, 7793–7813, https://doi.org/10.5194/acp-24-7793-2024, https://doi.org/10.5194/acp-24-7793-2024, 2024
Short summary
Short summary
In contrast with past work showing that the reduction of emissions was the dominant factor for the nationwide increase of surface O3 during the lockdown in China, this study finds that the variation in meteorology (temperature and other parameters) plays a more important role. This result is obtained through sensitivity simulations using a chemical transport model constrained by satellite (TROPOMI) data and calibrated with surface observations.
Brian Nathan, Joannes D. Maasakkers, Stijn Naus, Ritesh Gautam, Mark Omara, Daniel J. Varon, Melissa P. Sulprizio, Lucas A. Estrada, Alba Lorente, Tobias Borsdorff, Robert J. Parker, and Ilse Aben
Atmos. Chem. Phys., 24, 6845–6863, https://doi.org/10.5194/acp-24-6845-2024, https://doi.org/10.5194/acp-24-6845-2024, 2024
Short summary
Short summary
Venezuela's Lake Maracaibo region is notoriously hard to observe from space and features intensive oil exploitation, although production has strongly decreased in recent years. We estimate methane emissions using 2018–2020 TROPOMI satellite observations with national and regional transport models. Despite the production decrease, we find relatively constant emissions from Lake Maracaibo between 2018 and 2020, indicating that there could be large emissions from abandoned infrastructure.
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 Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Alba Lorente, Zichong Chen, Xiao Lu, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Margaux Winter, Shuang Ma, A. Anthony Bloom, John R. Worden, Robert N. Stavins, and Cynthia A. Randles
Atmos. Chem. Phys., 24, 5069–5091, https://doi.org/10.5194/acp-24-5069-2024, https://doi.org/10.5194/acp-24-5069-2024, 2024
Short summary
Short summary
We quantify 2019 methane emissions in the contiguous US (CONUS) at a ≈ 25 km × 25 km resolution using satellite methane observations. We find a 13 % upward correction to the 2023 US Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) for 2019, with large corrections to individual states, urban areas, and landfills. This may present a challenge for US climate policies and goals, many of which target significant reductions in methane emissions.
Berend J. Schuit, Joannes D. Maasakkers, Pieter Bijl, Gourav Mahapatra, Anne-Wil van den Berg, Sudhanshu Pandey, Alba Lorente, Tobias Borsdorff, Sander Houweling, Daniel J. Varon, Jason McKeever, Dylan Jervis, Marianne Girard, Itziar Irakulis-Loitxate, Javier Gorroño, Luis Guanter, Daniel H. Cusworth, and Ilse Aben
Atmos. Chem. Phys., 23, 9071–9098, https://doi.org/10.5194/acp-23-9071-2023, https://doi.org/10.5194/acp-23-9071-2023, 2023
Short summary
Short summary
Using two machine learning models, which were trained on TROPOMI methane satellite data, we detect 2974 methane plumes, so-called super-emitters, in 2021. We detect methane emissions globally related to urban areas or landfills, coal mining, and oil and gas production. Using our monitoring system, we identify 94 regions with frequent emissions. For 12 locations, we target high-resolution satellite instruments to enlarge and identify the exact infrastructure responsible for the emissions.
Brandon Bottorff, Michelle M. Lew, Youngjun Woo, Pamela Rickly, Matthew D. Rollings, Benjamin Deming, Daniel C. Anderson, Ezra Wood, Hariprasad D. Alwe, Dylan B. Millet, Andrew Weinheimer, Geoff Tyndall, John Ortega, Sebastien Dusanter, Thierry Leonardis, James Flynn, Matt Erickson, Sergio Alvarez, Jean C. Rivera-Rios, Joshua D. Shutter, Frank Keutsch, Detlev Helmig, Wei Wang, Hannah M. Allen, Johnathan H. Slade, Paul B. Shepson, Steven Bertman, and Philip S. Stevens
Atmos. Chem. Phys., 23, 10287–10311, https://doi.org/10.5194/acp-23-10287-2023, https://doi.org/10.5194/acp-23-10287-2023, 2023
Short summary
Short summary
The hydroxyl (OH), hydroperoxy (HO2), and organic peroxy (RO2) radicals play important roles in atmospheric chemistry and have significant air quality implications. Here, we compare measurements of OH, HO2, and total peroxy radicals (XO2) made in a remote forest in Michigan, USA, to predictions from a series of chemical models. Lower measured radical concentrations suggest that the models may be missing an important radical sink and overestimating the rate of ozone production in this forest.
Nicholas Balasus, Daniel J. Jacob, Alba Lorente, Joannes D. Maasakkers, Robert J. Parker, Hartmut Boesch, Zichong Chen, Makoto M. Kelp, Hannah Nesser, and Daniel J. Varon
Atmos. Meas. Tech., 16, 3787–3807, https://doi.org/10.5194/amt-16-3787-2023, https://doi.org/10.5194/amt-16-3787-2023, 2023
Short summary
Short summary
We use machine learning to remove biases in TROPOMI satellite observations of atmospheric methane, with GOSAT observations serving as a reference. We find that the TROPOMI biases relative to GOSAT are related to the presence of aerosols and clouds, the surface brightness, and the specific detector that makes the observation aboard TROPOMI. The resulting blended TROPOMI+GOSAT product is more reliable for quantifying methane emissions.
Ruosi Liang, Yuzhong Zhang, Wei Chen, Peixuan Zhang, Jingran Liu, Cuihong Chen, Huiqin Mao, Guofeng Shen, Zhen Qu, Zichong Chen, Minqiang Zhou, Pucai Wang, Robert J. Parker, Hartmut Boesch, Alba Lorente, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 8039–8057, https://doi.org/10.5194/acp-23-8039-2023, https://doi.org/10.5194/acp-23-8039-2023, 2023
Short summary
Short summary
We compare and evaluate East Asian methane emissions inferred from different satellite observations (GOSAT and TROPOMI). The results show discrepancies over northern India and eastern China. Independent ground-based observations are more consistent with TROPOMI-derived emissions in northern India and GOSAT-derived emissions in eastern China.
Daniel J. Varon, Daniel J. Jacob, Benjamin Hmiel, Ritesh Gautam, David R. Lyon, Mark Omara, Melissa Sulprizio, Lu Shen, Drew Pendergrass, Hannah Nesser, Zhen Qu, Zachary R. Barkley, Natasha L. Miles, Scott J. Richardson, Kenneth J. Davis, Sudhanshu Pandey, Xiao Lu, Alba Lorente, Tobias Borsdorff, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 7503–7520, https://doi.org/10.5194/acp-23-7503-2023, https://doi.org/10.5194/acp-23-7503-2023, 2023
Short summary
Short summary
We use TROPOMI satellite observations to quantify weekly methane emissions from the US Permian oil and gas basin from May 2018 to October 2020. We find that Permian emissions are highly variable, with diverse economic and activity drivers. The most important drivers during our study period were new well development and natural gas price. Permian methane intensity averaged 4.6 % and decreased by 1 % per year.
Alexander J. Norton, A. Anthony Bloom, Nicholas C. Parazoo, Paul A. Levine, Shuang Ma, Renato K. Braghiere, and T. Luke Smallman
Biogeosciences, 20, 2455–2484, https://doi.org/10.5194/bg-20-2455-2023, https://doi.org/10.5194/bg-20-2455-2023, 2023
Short summary
Short summary
This study explores how the representation of leaf phenology affects our ability to predict changes to the carbon balance of land ecosystems. We calibrate a new leaf phenology model against a diverse range of observations at six forest sites, showing that it improves the predictive capability of the processes underlying the ecosystem carbon balance. We then show how changes in temperature and rainfall affect the ecosystem carbon balance with this new model.
Zichong Chen, Daniel J. Jacob, Ritesh Gautam, Mark Omara, Robert N. Stavins, Robert C. Stowe, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Drew C. Pendergrass, and Sarah Hancock
Atmos. Chem. Phys., 23, 5945–5967, https://doi.org/10.5194/acp-23-5945-2023, https://doi.org/10.5194/acp-23-5945-2023, 2023
Short summary
Short summary
We quantify methane emissions from individual countries in the Middle East and North Africa by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We show that the ability to simply relate oil/gas emissions to activity metrics is compromised by stochastic nature of local infrastructure and management practices. We find that the industry target for oil/gas methane intensity is achievable through associated gas capture, modern infrastructure, and centralized operations.
Forwood Wiser, Bryan K. Place, Siddhartha Sen, Havala O. T. Pye, Benjamin Yang, Daniel M. Westervelt, Daven K. Henze, Arlene M. Fiore, and V. Faye McNeill
Geosci. Model Dev., 16, 1801–1821, https://doi.org/10.5194/gmd-16-1801-2023, https://doi.org/10.5194/gmd-16-1801-2023, 2023
Short summary
Short summary
We developed a reduced model of atmospheric isoprene oxidation, AMORE-Isoprene 1.0. It was created using a new Automated Model Reduction (AMORE) method designed to simplify complex chemical mechanisms with minimal manual adjustments to the output. AMORE-Isoprene 1.0 has improved accuracy and similar size to other reduced isoprene mechanisms. When included in the CRACMM mechanism, it improved the accuracy of EPA’s CMAQ model predictions for the northeastern USA compared to observations.
Alba Lorente, Tobias Borsdorff, Mari C. Martinez-Velarte, and Jochen Landgraf
Atmos. Meas. Tech., 16, 1597–1608, https://doi.org/10.5194/amt-16-1597-2023, https://doi.org/10.5194/amt-16-1597-2023, 2023
Short summary
Short summary
In the TROPOMI methane data, there are few false methane anomalies that can be misinterpreted as enhancements caused by strong emission sources. These artefacts are caused by features of the underlying surfaces that are not well characterized in the retrieval algorithm. Here we improve the representation of the surface reflectance dependency with wavelength in the forward model, removing the artificial localized CH4 enhancements found in several locations like Siberia, Australia and Algeria.
Robert J. Parker, Chris Wilson, Edward Comyn-Platt, Garry Hayman, Toby R. Marthews, A. Anthony Bloom, Mark F. Lunt, Nicola Gedney, Simon J. Dadson, Joe McNorton, Neil Humpage, Hartmut Boesch, Martyn P. Chipperfield, Paul I. Palmer, and Dai Yamazaki
Biogeosciences, 19, 5779–5805, https://doi.org/10.5194/bg-19-5779-2022, https://doi.org/10.5194/bg-19-5779-2022, 2022
Short summary
Short summary
Wetlands are the largest natural source of methane, one of the most important climate gases. The JULES land surface model simulates these emissions. We use satellite data to evaluate how well JULES reproduces the methane seasonal cycle over different tropical wetlands. It performs well for most regions; however, it struggles for some African wetlands influenced heavily by river flooding. We explain the reasons for these deficiencies and highlight how future development will improve these areas.
Alba Lorente, Tobias Borsdorff, Mari C. Martinez-Velarte, Andre Butz, Otto P. Hasekamp, Lianghai Wu, and Jochen Landgraf
Atmos. Meas. Tech., 15, 6585–6603, https://doi.org/10.5194/amt-15-6585-2022, https://doi.org/10.5194/amt-15-6585-2022, 2022
Short summary
Short summary
The TROPOspheric Monitoring Instrument (TROPOMI) performs observations over ocean in every orbit, enhancing the monitoring capabilities of methane from space. In the sun glint geometry the mirror-like reflection at the water surface provides a signal that is high enough to retrieve methane with high accuracy and precision. We present 4 years of methane concentrations over the ocean, and we assess its quality. We also show the importance of ocean observations to quantify total CH4 emissions.
Vanessa Selimovic, Damien Ketcherside, Sreelekha Chaliyakunnel, Catherine Wielgasz, Wade Permar, Hélène Angot, Dylan B. Millet, Alan Fried, Detlev Helmig, and Lu Hu
Atmos. Chem. Phys., 22, 14037–14058, https://doi.org/10.5194/acp-22-14037-2022, https://doi.org/10.5194/acp-22-14037-2022, 2022
Short summary
Short summary
Arctic warming has led to an increase in plants that emit gases in response to stress, but how these gases affect regional chemistry is largely unknown due to lack of observational data. Here we present the most comprehensive gas-phase measurements for this area to date and compare them to predictions from a global transport model. We report 78 gas-phase species and investigate their importance to atmospheric chemistry in the area, with broader implications for similar plant types.
Maria Paula Pérez-Peña, Jenny A. Fisher, Dylan B. Millet, Hisashi Yashiro, Ray L. Langenfelds, Paul B. Krummel, and Scott H. Kable
Atmos. Chem. Phys., 22, 12367–12386, https://doi.org/10.5194/acp-22-12367-2022, https://doi.org/10.5194/acp-22-12367-2022, 2022
Short summary
Short summary
We used two atmospheric models to test the implications of previously unexplored aldehyde photochemistry on the atmospheric levels of molecular hydrogen (H2). We showed that the new photochemistry from aldehydes produces more H2 over densely forested areas. Compared to the rest of the world, it is over these forested regions where the produced H2 is more likely to be removed. The results highlight that other processes that contribute to atmospheric H2 levels should be studied further.
Lu Shen, Ritesh Gautam, Mark Omara, Daniel Zavala-Araiza, Joannes D. Maasakkers, Tia R. Scarpelli, Alba Lorente, David Lyon, Jianxiong Sheng, Daniel J. Varon, Hannah Nesser, Zhen Qu, Xiao Lu, Melissa P. Sulprizio, Steven P. Hamburg, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022, https://doi.org/10.5194/acp-22-11203-2022, 2022
Short summary
Short summary
We use 22 months of TROPOMI satellite observations to quantity methane emissions from the oil (O) and natural gas (G) sector in the US and Canada at the scale of both individual basins as well as country-wide aggregates. We find that O/G-related methane emissions are underestimated in these inventories by 80 % for the US and 40 % for Canada, and 70 % of the underestimate in the US is from five O/G basins, including Permian, Haynesville, Anadarko, Eagle Ford, and Barnett.
Zichong Chen, Daniel J. Jacob, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Elise Penn, and Xueying Yu
Atmos. Chem. Phys., 22, 10809–10826, https://doi.org/10.5194/acp-22-10809-2022, https://doi.org/10.5194/acp-22-10809-2022, 2022
Short summary
Short summary
We quantify methane emissions in China and contributions from different sectors by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We find that anthropogenic methane emissions for China are underestimated in the national inventory. Our estimate of emissions indicates a small life-cycle loss rate, implying net climate benefits from the current
coal-to-gasenergy transition in China. However, this small loss rate can be misleading given China's high gas imports.
Matthias Schneider, Benjamin Ertl, Qiansi Tu, Christopher J. Diekmann, Farahnaz Khosrawi, Amelie N. Röhling, Frank Hase, Darko Dubravica, Omaira E. García, Eliezer Sepúlveda, Tobias Borsdorff, Jochen Landgraf, Alba Lorente, André Butz, Huilin Chen, Rigel Kivi, Thomas Laemmel, Michel Ramonet, Cyril Crevoisier, Jérome Pernin, Martin Steinbacher, Frank Meinhardt, Kimberly Strong, Debra Wunch, Thorsten Warneke, Coleen Roehl, Paul O. Wennberg, Isamu Morino, Laura T. Iraci, Kei Shiomi, Nicholas M. Deutscher, David W. T. Griffith, Voltaire A. Velazco, and David F. Pollard
Atmos. Meas. Tech., 15, 4339–4371, https://doi.org/10.5194/amt-15-4339-2022, https://doi.org/10.5194/amt-15-4339-2022, 2022
Short summary
Short summary
We present a computationally very efficient method for the synergetic use of level 2 remote-sensing data products. We apply the method to IASI vertical profile and TROPOMI total column space-borne methane observations and thus gain sensitivity for the tropospheric methane partial columns, which is not achievable by the individual use of TROPOMI and IASI. These synergetic effects are evaluated theoretically and empirically by inter-comparisons to independent references of TCCON, AirCore, and GAW.
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, https://doi.org/10.5194/gmd-15-5787-2022, 2022
Short summary
Short summary
Reducing atmospheric methane emissions is critical to slow near-term climate change. Globally surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to quantify methane emissions across user-selected regions of interest using TROPOMI satellite observations.
Jonas Hachmeister, Oliver Schneising, Michael Buchwitz, Alba Lorente, Tobias Borsdorff, John P. Burrows, Justus Notholt, and Matthias Buschmann
Atmos. Meas. Tech., 15, 4063–4074, https://doi.org/10.5194/amt-15-4063-2022, https://doi.org/10.5194/amt-15-4063-2022, 2022
Short summary
Short summary
Sentinel-5P trace gas retrievals rely on elevation data in their calculations. Outdated or inaccurate data can lead to significant errors in e.g. dry-air mole fractions of methane (XCH4). We show that the use of inadequate elevation data leads to strong XCH4 anomalies in Greenland. Similar problems can be expected for other regions with inaccurate elevation data. However, we expect these to be more localized. We show that updating elevation data used in the retrieval solves this issue.
John R. Worden, Daniel H. Cusworth, Zhen Qu, Yi Yin, Yuzhong Zhang, A. Anthony Bloom, Shuang Ma, Brendan K. Byrne, Tia Scarpelli, Joannes D. Maasakkers, David Crisp, Riley Duren, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 6811–6841, https://doi.org/10.5194/acp-22-6811-2022, https://doi.org/10.5194/acp-22-6811-2022, 2022
Short summary
Short summary
This paper is intended to accomplish two goals: 1) describe a new algorithm by which remotely sensed measurements of methane or other tracers can be used to not just quantify methane fluxes, but also attribute these fluxes to specific sources and regions and characterize their uncertainties, and 2) use this new algorithm to provide methane emissions by sector and country in support of the global stock take.
Andreas Schneider, Tobias Borsdorff, Joost aan de Brugh, Alba Lorente, Franziska Aemisegger, David Noone, Dean Henze, Rigel Kivi, and Jochen Landgraf
Atmos. Meas. Tech., 15, 2251–2275, https://doi.org/10.5194/amt-15-2251-2022, https://doi.org/10.5194/amt-15-2251-2022, 2022
Short summary
Short summary
This paper presents an extended H₂O/HDO total column dataset from short-wave infrared measurements by TROPOMI including cloudy and clear-sky scenes. Coverage is tremendously increased compared to previous TROPOMI HDO datasets. The new dataset is validated against recent ground-based FTIR measurements from TCCON and against aircraft measurements over the ocean. The use of the new dataset is demonstrated with a case study of a cold air outbreak in January 2020.
Johannes Gensheimer, Alexander J. Turner, Philipp Köhler, Christian Frankenberg, and Jia Chen
Biogeosciences, 19, 1777–1793, https://doi.org/10.5194/bg-19-1777-2022, https://doi.org/10.5194/bg-19-1777-2022, 2022
Short summary
Short summary
We develop a convolutional neural network, named SIFnet, that increases the spatial resolution of SIF from TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations, together with a broad range of high-resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis.
Helen L. Fitzmaurice, Alexander J. Turner, Jinsol Kim, Katherine Chan, Erin R. Delaria, Catherine Newman, Paul Wooldridge, and Ronald C. Cohen
Atmos. Chem. Phys., 22, 3891–3900, https://doi.org/10.5194/acp-22-3891-2022, https://doi.org/10.5194/acp-22-3891-2022, 2022
Short summary
Short summary
On-road emissions are thought to vary widely from existing predictions, as the effects of the age of the vehicle fleet, the performance of emission control systems, and variations in speed are difficult to assess under ambient driving conditions. We present an observational approach to characterize on-road emissions and show that the method is consistent with other approaches to within ~ 3 %.
Elias C. Massoud, A. Anthony Bloom, Marcos Longo, John T. Reager, Paul A. Levine, and John R. Worden
Hydrol. Earth Syst. Sci., 26, 1407–1423, https://doi.org/10.5194/hess-26-1407-2022, https://doi.org/10.5194/hess-26-1407-2022, 2022
Short summary
Short summary
The water balance on river basin scales depends on a number of soil physical processes. Gaining information on these quantities using observations is a key step toward improving the skill of land surface hydrology models. In this study, we use data from the Gravity Recovery and Climate Experiment (NASA-GRACE) to inform and constrain these hydrologic processes. We show that our model is able to simulate the land hydrologic cycle for a watershed in the Amazon from January 2003 to December 2012.
Yan Yang, A. Anthony Bloom, Shuang Ma, Paul Levine, Alexander Norton, Nicholas C. Parazoo, John T. Reager, John Worden, Gregory R. Quetin, T. Luke Smallman, Mathew Williams, Liang Xu, and Sassan Saatchi
Geosci. Model Dev., 15, 1789–1802, https://doi.org/10.5194/gmd-15-1789-2022, https://doi.org/10.5194/gmd-15-1789-2022, 2022
Short summary
Short summary
Global carbon and water have large uncertainties that are hard to quantify in current regional and global models. Field observations provide opportunities for better calibration and validation of current modeling of carbon and water. With the unique structure of CARDAMOM, we have utilized the data assimilation capability and designed the benchmarking framework by using field observations in modeling. Results show that data assimilation improves model performance in different aspects.
Rupert Holzinger, Oliver Eppers, Kouji Adachi, Heiko Bozem, Markus Hartmann, Andreas Herber, Makoto Koike, Dylan B. Millet, Nobuhiro Moteki, Sho Ohata, Frank Stratmann, and Atsushi Yoshida
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-95, https://doi.org/10.5194/acp-2022-95, 2022
Revised manuscript not accepted
Short summary
Short summary
In spring 2018 the research aircraft Polar 5 conducted flights in the Arctic atmosphere. The flight operation was from Station Nord in Greenland, 1700 km north of the Arctic Circle (81°43'N, 17°47'W). Using a mass spectrometer we measured more than 100 organic compounds in the air. We found a clear signature of natural organic compounds that are transported from forests to the high Arctic. These compounds have the potential to change the cloud cover and energy budget of the Arctic region.
Stephanie G. Stettz, Nicholas C. Parazoo, A. Anthony Bloom, Peter D. Blanken, David R. Bowling, Sean P. Burns, Cédric Bacour, Fabienne Maignan, Brett Raczka, Alexander J. Norton, Ian Baker, Mathew Williams, Mingjie Shi, Yongguang Zhang, and Bo Qiu
Biogeosciences, 19, 541–558, https://doi.org/10.5194/bg-19-541-2022, https://doi.org/10.5194/bg-19-541-2022, 2022
Short summary
Short summary
Uncertainty in the response of photosynthesis to temperature poses a major challenge to predicting the response of forests to climate change. In this paper, we study how photosynthesis in a mountainous evergreen forest is limited by temperature. This study highlights that cold temperature is a key factor that controls spring photosynthesis. Including the cold-temperature limitation in an ecosystem model improved its ability to simulate spring photosynthesis.
Xiao Lu, Daniel J. Jacob, Haolin Wang, Joannes D. Maasakkers, Yuzhong Zhang, Tia R. Scarpelli, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Hannah Nesser, A. Anthony Bloom, Shuang Ma, John R. Worden, Shaojia Fan, Robert J. Parker, Hartmut Boesch, Ritesh Gautam, Deborah Gordon, Michael D. Moran, Frances Reuland, Claudia A. Octaviano Villasana, and Arlyn Andrews
Atmos. Chem. Phys., 22, 395–418, https://doi.org/10.5194/acp-22-395-2022, https://doi.org/10.5194/acp-22-395-2022, 2022
Short summary
Short summary
We evaluate methane emissions and trends for 2010–2017 in the gridded national emission inventories for the United States, Canada, and Mexico by inversion of in situ and satellite methane observations. We find that anthropogenic methane emissions for all three countries are underestimated in the national inventories, largely driven by oil emissions. Anthropogenic methane emissions in the US peak in 2014, in contrast to the report of a steadily decreasing trend over 2010–2017 from the US EPA.
Qiansi Tu, Frank Hase, Matthias Schneider, Omaira García, Thomas Blumenstock, Tobias Borsdorff, Matthias Frey, Farahnaz Khosrawi, Alba Lorente, Carlos Alberti, Juan J. Bustos, André Butz, Virgilio Carreño, Emilio Cuevas, Roger Curcoll, Christopher J. Diekmann, Darko Dubravica, Benjamin Ertl, Carme Estruch, Sergio Fabián León-Luis, Carlos Marrero, Josep-Anton Morgui, Ramón Ramos, Christian Scharun, Carsten Schneider, Eliezer Sepúlveda, Carlos Toledano, and Carlos Torres
Atmos. Chem. Phys., 22, 295–317, https://doi.org/10.5194/acp-22-295-2022, https://doi.org/10.5194/acp-22-295-2022, 2022
Short summary
Short summary
We use different methane ground- and space-based remote sensing data sets for investigating the emission strength of three waste disposal sites close to Madrid. We present a method that uses wind-assigned anomalies for deriving emission strengths from satellite data and estimate their uncertainty to 9–14 %. The emission strengths estimated from the remote sensing data sets are significantly larger than the values published in the official register.
Alexander J. Turner, Philipp Köhler, Troy S. Magney, Christian Frankenberg, Inez Fung, and Ronald C. Cohen
Biogeosciences, 18, 6579–6588, https://doi.org/10.5194/bg-18-6579-2021, https://doi.org/10.5194/bg-18-6579-2021, 2021
Short summary
Short summary
This work builds a high-resolution estimate (500 m) of gross primary productivity (GPP) over the US using satellite measurements of solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) between 2018 and 2020. We identify ecosystem-specific scaling factors for estimating gross primary productivity (GPP) from TROPOMI SIF. Extreme precipitation events drive four regional GPP anomalies that account for 28 % of year-to-year GPP differences across the US.
Xueying Yu, Dylan B. Millet, and Daven K. Henze
Geosci. Model Dev., 14, 7775–7793, https://doi.org/10.5194/gmd-14-7775-2021, https://doi.org/10.5194/gmd-14-7775-2021, 2021
Short summary
Short summary
We conduct observing system simulation experiments to test how well inverse analyses of high-resolution satellite data from sensors such as TROPOMI can quantify methane emissions. Inversions can improve monthly flux estimates at 25 km even with a spatially biased prior or model transport errors, but results are strongly degraded when both are present. We further evaluate a set of alternate formalisms to overcome limitations of the widely used scale factor approach that arise for missing sources.
Sabour Baray, Daniel J. Jacob, Joannes D. Maasakkers, Jian-Xiong Sheng, Melissa P. Sulprizio, Dylan B. A. Jones, A. Anthony Bloom, and Robert McLaren
Atmos. Chem. Phys., 21, 18101–18121, https://doi.org/10.5194/acp-21-18101-2021, https://doi.org/10.5194/acp-21-18101-2021, 2021
Short summary
Short summary
We use 2010–2015 surface and satellite observations to disentangle methane from anthropogenic and natural sources in Canada. Using a chemical transport model (GEOS-Chem), the mismatch between modelled and observed methane concentrations can be used to infer emissions according to Bayesian statistics. Compared to prior knowledge, we show higher anthropogenic emissions attributed to energy and/or agriculture in Western Canada and lower natural emissions from Boreal wetlands.
Alexander A. T. Bui, Henry W. Wallace, Sarah Kavassalis, Hariprasad D. Alwe, James H. Flynn, Matt H. Erickson, Sergio Alvarez, Dylan B. Millet, Allison L. Steiner, and Robert J. Griffin
Atmos. Chem. Phys., 21, 17031–17050, https://doi.org/10.5194/acp-21-17031-2021, https://doi.org/10.5194/acp-21-17031-2021, 2021
Short summary
Short summary
Differences in atmospheric species above and below a forest canopy provide insight into the relative importance of local mixing, long-range transport, and chemical processes in determining vertical gradients in atmospheric particles in a forested environment. This helps in understanding the flux of climate-relevant material out of the forest to the atmosphere. We studied this in a remote forest using vertically resolved measurements of gases and particles.
Dandan Wei, Hariprasad D. Alwe, Dylan B. Millet, Brandon Bottorff, Michelle Lew, Philip S. Stevens, Joshua D. Shutter, Joshua L. Cox, Frank N. Keutsch, Qianwen Shi, Sarah C. Kavassalis, Jennifer G. Murphy, Krystal T. Vasquez, Hannah M. Allen, Eric Praske, John D. Crounse, Paul O. Wennberg, Paul B. Shepson, Alexander A. T. Bui, Henry W. Wallace, Robert J. Griffin, Nathaniel W. May, Megan Connor, Jonathan H. Slade, Kerri A. Pratt, Ezra C. Wood, Mathew Rollings, Benjamin L. Deming, Daniel C. Anderson, and Allison L. Steiner
Geosci. Model Dev., 14, 6309–6329, https://doi.org/10.5194/gmd-14-6309-2021, https://doi.org/10.5194/gmd-14-6309-2021, 2021
Short summary
Short summary
Over the past decade, understanding of isoprene oxidation has improved, and proper representation of isoprene oxidation and isoprene-derived SOA (iSOA) formation in canopy–chemistry models is now recognized to be important for an accurate understanding of forest–atmosphere exchange. The updated FORCAsT version 2.0 improves the estimation of some isoprene oxidation products and is one of the few canopy models currently capable of simulating SOA formation from monoterpenes and isoprene.
Mahesh Kumar Sha, Bavo Langerock, Jean-François L. Blavier, Thomas Blumenstock, Tobias Borsdorff, Matthias Buschmann, Angelika Dehn, Martine De Mazière, Nicholas M. Deutscher, Dietrich G. Feist, Omaira E. García, David W. T. Griffith, Michel Grutter, James W. Hannigan, Frank Hase, Pauli Heikkinen, Christian Hermans, Laura T. Iraci, Pascal Jeseck, Nicholas Jones, Rigel Kivi, Nicolas Kumps, Jochen Landgraf, Alba Lorente, Emmanuel Mahieu, Maria V. Makarova, Johan Mellqvist, Jean-Marc Metzger, Isamu Morino, Tomoo Nagahama, Justus Notholt, Hirofumi Ohyama, Ivan Ortega, Mathias Palm, Christof Petri, David F. Pollard, Markus Rettinger, John Robinson, Sébastien Roche, Coleen M. Roehl, Amelie N. Röhling, Constantina Rousogenous, Matthias Schneider, Kei Shiomi, Dan Smale, Wolfgang Stremme, Kimberly Strong, Ralf Sussmann, Yao Té, Osamu Uchino, Voltaire A. Velazco, Corinne Vigouroux, Mihalis Vrekoussis, Pucai Wang, Thorsten Warneke, Tyler Wizenberg, Debra Wunch, Shoma Yamanouchi, Yang Yang, and Minqiang Zhou
Atmos. Meas. Tech., 14, 6249–6304, https://doi.org/10.5194/amt-14-6249-2021, https://doi.org/10.5194/amt-14-6249-2021, 2021
Short summary
Short summary
This paper presents, for the first time, Sentinel-5 Precursor methane and carbon monoxide validation results covering a period from November 2017 to September 2020. For this study, we used global TCCON and NDACC-IRWG network data covering a wide range of atmospheric and surface conditions across different terrains. We also show the influence of a priori alignment, smoothing uncertainties and the sensitivity of the validation results towards the application of advanced co-location criteria.
Zhen Qu, Daniel J. Jacob, Lu Shen, Xiao Lu, Yuzhong Zhang, Tia R. Scarpelli, Hannah Nesser, Melissa P. Sulprizio, Joannes D. Maasakkers, A. Anthony Bloom, John R. Worden, Robert J. Parker, and Alba L. Delgado
Atmos. Chem. Phys., 21, 14159–14175, https://doi.org/10.5194/acp-21-14159-2021, https://doi.org/10.5194/acp-21-14159-2021, 2021
Short summary
Short summary
The recent launch of TROPOMI offers an unprecedented opportunity to quantify the methane budget from a top-down perspective. We use TROPOMI and the more mature GOSAT methane observations to estimate methane emissions and get consistent global budgets. However, TROPOMI shows biases over regions where surface albedo is small and provides less information for the coarse-resolution inversion due to the larger error correlations and spatial variations in the number of observations.
Yi Yin, Frederic Chevallier, Philippe Ciais, Philippe Bousquet, Marielle Saunois, Bo Zheng, John Worden, A. Anthony Bloom, Robert J. Parker, Daniel J. Jacob, Edward J. Dlugokencky, and Christian Frankenberg
Atmos. Chem. Phys., 21, 12631–12647, https://doi.org/10.5194/acp-21-12631-2021, https://doi.org/10.5194/acp-21-12631-2021, 2021
Short summary
Short summary
The growth of methane, the second-most important anthropogenic greenhouse gas after carbon dioxide, has been accelerating in recent years. Using an ensemble of multi-tracer atmospheric inversions constrained by surface or satellite observations, we show that global methane emissions increased by nearly 1 % per year from 2010–2017, with leading contributions from the tropics and East Asia.
Benjamin A. Nault, Duseong S. Jo, Brian C. McDonald, Pedro Campuzano-Jost, Douglas A. Day, Weiwei Hu, Jason C. Schroder, James Allan, Donald R. Blake, Manjula R. Canagaratna, Hugh Coe, Matthew M. Coggon, Peter F. DeCarlo, Glenn S. Diskin, Rachel Dunmore, Frank Flocke, Alan Fried, Jessica B. Gilman, Georgios Gkatzelis, Jacqui F. Hamilton, Thomas F. Hanisco, Patrick L. Hayes, Daven K. Henze, Alma Hodzic, James Hopkins, Min Hu, L. Greggory Huey, B. Thomas Jobson, William C. Kuster, Alastair Lewis, Meng Li, Jin Liao, M. Omar Nawaz, Ilana B. Pollack, Jeffrey Peischl, Bernhard Rappenglück, Claire E. Reeves, Dirk Richter, James M. Roberts, Thomas B. Ryerson, Min Shao, Jacob M. Sommers, James Walega, Carsten Warneke, Petter Weibring, Glenn M. Wolfe, Dominique E. Young, Bin Yuan, Qiang Zhang, Joost A. de Gouw, and Jose L. Jimenez
Atmos. Chem. Phys., 21, 11201–11224, https://doi.org/10.5194/acp-21-11201-2021, https://doi.org/10.5194/acp-21-11201-2021, 2021
Short summary
Short summary
Secondary organic aerosol (SOA) is an important aspect of poor air quality for urban regions around the world, where a large fraction of the population lives. However, there is still large uncertainty in predicting SOA in urban regions. Here, we used data from 11 urban campaigns and show that the variability in SOA production in these regions is predictable and is explained by key emissions. These results are used to estimate the premature mortality associated with SOA in urban regions.
Ilya Stanevich, Dylan B. A. Jones, Kimberly Strong, Martin Keller, Daven K. Henze, Robert J. Parker, Hartmut Boesch, Debra Wunch, Justus Notholt, Christof Petri, Thorsten Warneke, Ralf Sussmann, Matthias Schneider, Frank Hase, Rigel Kivi, Nicholas M. Deutscher, Voltaire A. Velazco, Kaley A. Walker, and Feng Deng
Atmos. Chem. Phys., 21, 9545–9572, https://doi.org/10.5194/acp-21-9545-2021, https://doi.org/10.5194/acp-21-9545-2021, 2021
Short summary
Short summary
We explore the utility of a weak-constraint (WC) four-dimensional variational (4D-Var) data assimilation scheme for mitigating systematic errors in methane simulation in the GEOS-Chem model. We use data from the Greenhouse Gases Observing Satellite (GOSAT) and show that, compared to the traditional 4D-Var approach, the WC scheme improves the agreement between the model and independent observations. We find that the WC corrections to the model provide insight into the source of the errors.
Na Zhao, Xinyi Dong, Kan Huang, Joshua S. Fu, Marianne Tronstad Lund, Kengo Sudo, Daven Henze, Tom Kucsera, Yun Fat Lam, Mian Chin, and Simone Tilmes
Atmos. Chem. Phys., 21, 8637–8654, https://doi.org/10.5194/acp-21-8637-2021, https://doi.org/10.5194/acp-21-8637-2021, 2021
Short summary
Short summary
Black carbon acts as a strong climate forcer, especially in vulnerable pristine regions such as the Arctic. This work utilizes ensemble modeling results from the task force Hemispheric Transport of Air Pollution Phase 2 to investigate the responses of Arctic black carbon and surface temperature to various source emission reductions. East Asia contributed the most to Arctic black carbon. The response of Arctic temperature to black carbon was substantially more sensitive than the global average.
Zichong Chen, Junjie Liu, Daven K. Henze, Deborah N. Huntzinger, Kelley C. Wells, Stephen Sitch, Pierre Friedlingstein, Emilie Joetzjer, Vladislav Bastrikov, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Etsushi Kato, Sebastian Lienert, Danica L. Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Benjamin Poulter, Hanqin Tian, Andrew J. Wiltshire, Sönke Zaehle, and Scot M. Miller
Atmos. Chem. Phys., 21, 6663–6680, https://doi.org/10.5194/acp-21-6663-2021, https://doi.org/10.5194/acp-21-6663-2021, 2021
Short summary
Short summary
NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite observes atmospheric CO2 globally. We use a multiple regression and inverse model to quantify the relationships between OCO-2 and environmental drivers within individual years for 2015–2018 and within seven global biomes. Our results point to limitations of current space-based observations for inferring environmental relationships but also indicate the potential to inform key relationships that are very uncertain in process-based models.
Caroline A. Famiglietti, T. Luke Smallman, Paul A. Levine, Sophie Flack-Prain, Gregory R. Quetin, Victoria Meyer, Nicholas C. Parazoo, Stephanie G. Stettz, Yan Yang, Damien Bonal, A. Anthony Bloom, Mathew Williams, and Alexandra G. Konings
Biogeosciences, 18, 2727–2754, https://doi.org/10.5194/bg-18-2727-2021, https://doi.org/10.5194/bg-18-2727-2021, 2021
Short summary
Short summary
Model uncertainty dominates the spread in terrestrial carbon cycle predictions. Efforts to reduce it typically involve adding processes, thereby increasing model complexity. However, if and how model performance scales with complexity is unclear. Using a suite of 16 structurally distinct carbon cycle models, we find that increased complexity only improves skill if parameters are adequately informed. Otherwise, it can degrade skill, and an intermediate-complexity model is optimal.
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.
Anderson, D. C., Duncan, B. N., Fiore, A. M., Baublitz, C. B.,
Follette-Cook, M. B., Nicely, J. M., and Wolfe, G. M.: Spatial and temporal
variability in the hydroxyl (OH) radical: understanding the role of
large-scale climate features and their influence on OH through its dynamical
and photochemical drivers, Atmos. Chem. Phys., 21, 6481–6508,
https://doi.org/10.5194/acp-21-6481-2021, 2021.
APEI: Canada's air pollutant emissions inventory, https://open.canada.ca/data/en/dataset/fa1c88a8-bf78-4fcb-9c1e-2a5534b92131, 2020 (last access: 3 March 2023).
Baker, A. K., Schuck, T. J., Brenninkmeijer, C. A. M., Rauthe-Schöch,
A., Slemr, F., van Velthoven, P. F. J., and Lelieveld, J.: Estimating the
contribution of monsoon-related biogenic production to methane emissions
from South Asia using CARIBIC observations, Geophys. Res. Lett.,
39, L10813, https://doi.org/10.1029/2012GL051756, 2012.
Barichivich, J., Gloor, E., Peylin, P., Brienen, R. J. W., Schöngart,
J., Espinoza, J. C., and Pattnayak, K. C.: Recent intensification of Amazon
flooding extremes driven by strengthened Walker circulation, Sci.
Adv., 4, eaat8785, https://doi.org/10.1126/sciadv.aat8785, 2018.
Bergamaschi, P., Houweling, S., Segers, A., Krol, M., Frankenberg, C.,
Scheepmaker, R. A., Dlugokencky, E., Wofsy, S. C., Kort, E. A., Sweeney, C.,
Schuck, T., Brenninkmeijer, C., Chen, H., Beck, V., and Gerbig, C.:
Atmospheric CH4 in the first decade of the 21st century: Inverse modeling
analysis using SCIAMACHY satellite retrievals and NOAA surface measurements,
J. Geophys. Res.-Atmos., 118, 7350–7369,
https://doi.org/10.1002/jgrd.50480, 2013.
Bloom, A. A., Bowman, K. W., Lee, M., Turner, A. J., Schroeder, R., Worden, J. R., Weidner, R., McDonald, K. C., and Jacob, D. J.: A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0), Geosci. Model Dev., 10, 2141–2156, https://doi.org/10.5194/gmd-10-2141-2017, 2017.
Bousserez, N., Henze, D. K., Perkins, A., Bowman, K. W., Lee, M., Liu, J.,
Deng, F., and Jones, D. B. A.: Improved analysis-error covariance matrix for
high-dimensional variational inversions: application to source estimation
using a 3D atmospheric transport model, Q. J. Roy. Meteor. Soc., 141,
1906–1921, https://doi.org/10.1002/qj.2495, 2015.
Bousserez, N., Henze, D. K., Rooney, B., Perkins, A., Wecht, K. J., Turner,
A. J., Natraj, V., and Worden, J. R.: Constraints on methane emissions in
North America from future geostationary remote-sensing measurements,
Atmos. Chem. Phys., 16, 6175–6190, https://doi.org/10.5194/acp-16-6175-2016,
2016.
BP: British Petroleum Company Limited, https://www.bp.com,
last access: 25 April 2022.
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.
Brune, W. H., Miller, D. O., Thames, A. B., Allen, H. M., Apel, E. C.,
Blake, D. R., Bui, T. P., Commane, R., Crounse, J. D., Daube, B. C., Diskin,
G. S., DiGangi, J. P., Elkins, J. W., Hall, S. R., Hanisco, T. F., Hannun,
R. A., Hintsa, E. J., Hornbrook, R. S., Kim, M. J., McKain, K., Moore, F.
L., Neuman, J. A., Nicely, J. M., Peischl, J., Ryerson, T. B., St. Clair, J.
M., Sweeney, C., Teng, A. P., Thompson, C., Ullmann, K., Veres, P. R.,
Wennberg, P. O., and Wolfe, G. M.: Exploring oxidation in the remote free
troposphere: Insights from Atmospheric Tomography (ATom), J. Geophys. Res.-Atmos., 125, e2019JD031685, https://doi.org/10.1029/2019JD031685,
2020.
Buchwitz, M., Schneising, O., Reuter, M., Heymann, J., Krautwurst, S.,
Bovensmann, H., Burrows, J. P., Boesch, H., Parker, R. J., Somkuti, P.,
Detmers, R. G., Hasekamp, O. P., Aben, I., Butz, A., Frankenberg, C., and
Turner, A. J.: Satellite-derived methane hotspot emission estimates using a
fast data-driven method, Atmos. Chem. Phys., 17, 5751–5774,
https://doi.org/10.5194/acp-17-5751-2017, 2017.
Burkholder, J., Sander, S., Abbatt, J., Barker, J., Cappa, C., Crounse, J.,
Dibble, T., Huie, R., Kolb, C., and Kurylo, M.: Chemical kinetics and
photochemical data for use in atmospheric studies, JPL Data Eval., 19, https://jpldataeval.jpl.nasa.gov,
2020.
Chen, H., Karion, A., Rella, C. W., Winderlich, J., Gerbig, C., Filges, A.,
Newberger, T., Sweeney, C., and Tans, P. P.: Accurate measurements of carbon
monoxide in humid air using the cavity ring-down spectroscopy (CRDS)
technique, Atmos. Meas. Tech., 6, 1031–1040,
https://doi.org/10.5194/amt-6-1031-2013, 2013.
Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Lo Vullo, E., Solazzo,
E., Monforti-Ferrario, F., Olivier, J., and Vignati, E.: EDGAR v5.0
Greenhouse Gas Emissions, European Commission [data set],
https://data.europa.eu/doi/10.2904/JRC_DATASET_EDGAR (last access: 3 March 2023), 2019.
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,
Sci. Data, 7, 1–17, https://doi.org/10.1038/s41597-020-0462-2, 2020.
Cusworth, D. H., Bloom, A. A., Ma, S., Miller, C. E., Bowman, K., Yin, Y.,
Maasakkers, J. D., Zhang, Y., Scarpelli, T. R., Qu, Z., Jacob, D. J., and
Worden, J. R.: A Bayesian framework for deriving sector-based methane
emissions from top-down fluxes, Commun. Earth Environ., 2,
1–8, https://doi.org/10.1038/s43247-021-00312-6, 2021.
Darmenov, A. S. and Silva, A. D.: The Quick Fire Emissions Dataset (QFED):
Documentation of versions 2.1, 2.2 and 2.4, NASA technical report series on
global modeling and data assimilation, NASA TM-2015-104606, Vol. 38, 2015.
EDGAR: EDGAR v5.0 Global Greenhouse Gas
Emissions, European Commission [data set], https://edgar.jrc.ec.europa.eu/overview.php?v=50_GHG (last access: 3 March 2023), 2019.
EDGAR: EDGAR v6.0 Global Greenhouse Gas Emissions, European Commission [data set]
https://edgar.jrc.ec.europa.eu/dataset_ghg60 (last access: 3 March 2023), 2021.
EIA: US Energy Information Administration Independent Statistics and
Analysis, EIA, https://www.eia.gov, last access: 25 April 2022.
ERA5 reanalyses: Copernicus Climate Change Service (C3S) Climate Data Store
(CDS), ECMWF [data set],
https://cds.climate.copernicus.eu/cdsapp#!/search?text=ERA5 back extension&type=dataset,
(last access: 25 April 2022), 2019.
FAOSTAT: Food and Agriculture Organization of the United Nations, FAOSTAT, http://www.fao.org/faostat/en/#data, last access: 25 April 2022.
Franco, B., Mahieu, E., Emmons, L. K., Tzompa-Sosa, Z. A., Fischer, E. V.,
Sudo, K., Bovy, B., Conway, S., Griffin, D., Hannigan, J. W., Strong, K.,
and Walker, K. A.: Evaluating ethane and methane emissions associated with
the development of oil and natural gas extraction in North America,
Environ. Res. Lett., 11, 044010, https://doi.org/10.1088/1748-9326/11/4/044010,
2016.
Frankenberg, C., Aben, I., Bergamaschi, P., Dlugokencky, E. J., van Hees,
R., Houweling, S., van der Meer, P., Snel, R., and Tol, P.: Global
column-averaged methane mixing ratios from 2003 to 2009 as derived from
SCIAMACHY: Trends and variability, J. Geophys. Res.-Atmos., 116, D04302, https://doi.org/10.1029/2010JD014849, 2011.
Fung, I., John, J., Lerner, J., Matthews, E., Prather, M., Steele, L. P.,
and Fraser, P. J.: Three-dimensional model synthesis of the global methane
cycle, J. Geophys. Res.-Atmos., 96, 13033–13065,
https://doi.org/10.1029/91jd01247, 1991.
GMAO (Global Modeling and Assimilation Office): GEOS Near-Real Time Data Products, https://gmao.gsfc.nasa.gov/GMAO_products/NRT_products.php (last access: 3 March 2023), 2013.
GOSAT: Greenhouse Gases Observing Satellite, GOSAT,
http://www.gosat.nies.go.jp/en/index.html, last access: 25 April 2022.
Gonzalez, A., Millet, D. B., Yu, X., Wells, K. C., Griffis, T. J., Baier, B.
C., Campbell, P. C., Choi, Y., DiGangi, J. P., Gvakharia, A., Halliday, H.
S., Kort, E. A., McKain, K., Nowak, J. B., and Plant, G.: Fossil versus
nonfossil CO sources in the US: New airborne constraints from ACT-America
and GEM, Geophys. Res. Lett., 48, e2021GL093361,
https://doi.org/10.1029/2021GL093361, 2021.
Heald, C. L., Jacob, D. J., Jones, D. B. A., Palmer, P. I., Logan, J. A.,
Streets, D. G., Sachse, G. W., Gille, J. C., Hoffman, R. N., and Nehrkorn,
T.: Comparative inverse analysis of satellite (MOPITT) and aircraft
(TRACE-P) observations to estimate Asian sources of carbon monoxide, J. Geophys. Res.-Atmos., 109, D23306, https://doi.org/10.1029/2004jd005185,
2004.
Henze, D. K., Hakami, A., and Seinfeld, J. H.: Development of the adjoint of
GEOS-Chem, Atmos. Chem. Phys., 7, 2413–2433,
https://doi.org/10.5194/acp-7-2413-2007, 2007.
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G.,
Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T.
C., Dawidowski, L., Kholod, N., Kurokawa, J. I., Li, M., Liu, L., Lu, Z.,
Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014)
anthropogenic emissions of reactive gases and aerosols from the Community
Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408,
https://doi.org/10.5194/gmd-11-369-2018, 2018.
Hozo, S. P., Djulbegovic, B., and Hozo, I.:
Estimating the mean and variance from the median, range, and the size of a
sample, BMC Med. Res. Methodol., 5, 13, https://doi.org/10.1186/1471-2288-5-13,
2005.
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.
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.
Hu, L., Millet, D. B., Baasandorj, M., Griffis, T. J., Turner, P., Helmig,
D., Curtis, A. J., and Hueber, J.: Isoprene emissions and impacts over an
ecological transition region in the U.S. Upper Midwest inferred from tall
tower measurements, J. Geophys. Res.-Atmos., 120,
3553–3571, https://doi.org/10.1002/2014JD022732, 2015.
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, https://doi.org/10.1017/9781009157896, 2021.
Irakulis-Loitxate, I., Gorroño, J., Zavala-Araiza, D., and Guanter, L.:
Satellites detect a methane ultra-emission event from an offshore platform
in the Gulf of Mexico, Environ. Sci. Technol. Lett., 9,
520–525, https://doi.org/10.1021/acs.estlett.2c00225, 2022.
Jackson, R. B., Saunois, M., Bousquet, P., Canadell, J. G., Poulter, B.,
Stavert, A. R., Bergamaschi, P., Niwa, Y., Segers, A., and Tsuruta, A.:
Increasing anthropogenic methane emissions arise equally from agricultural
and fossil fuel sources, Environ. Res. Lett., 15, 071002,
https://doi.org/10.1088/1748-9326/ab9ed2, 2020.
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.
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
Geosci. Remote Sens. Lett., 16, 1402–1406,
https://doi.org/10.1109/LGRS.2019.2898779, 2019.
Katzenberger, A., Schewe, J., Pongratz, J., and Levermann, A.: Robust
increase of Indian monsoon rainfall and its variability under future warming
in CMIP6 models, Earth Syst. Dynam., 12, 367–386,
https://doi.org/10.5194/esd-12-367-2021, 2021.
Kleinen, T., Mikolajewicz, U., and Brovkin, V.: Terrestrial methane
emissions from the Last Glacial Maximum to the preindustrial period, Clim. Past, 16, 575–595, https://doi.org/10.5194/cp-16-575-2020, 2020.
Kort, E. A., Smith, M. L., Murray, L. T., Gvakharia, A., Brandt, A. R.,
Peischl, J., Ryerson, T. B., Sweeney, C., and Travis, K.: Fugitive emissions
from the Bakken shale illustrate role of shale production in global ethane
shift, Geophys. Res. Lett., 43, 4617–4623, https://doi.org/10.1002/2016GL068703,
2016.
Koster, R. D., Darmenov, A. S., and da Silva, A. M.: The Quick Fire
Emissions Dataset (QFED): Documentation of Versions 2.1, 2.2 and 2.4, Vol.
38, Technical Report Series on Global Modeling and Data Assimilation,
https://gmao.gsfc.nasa.gov/pubs/docs/Darmenov796.pdf (last access: 3 March 2022), 2015.
Krol, M. and Lelieveld, J.: Can the variability in tropospheric OH be
deduced from measurements of 1,1,1-trichloroethane (methyl chloroform)?,
J. Geophys. Res.-Atmos., 108, 4125, https://doi.org/10.1029/2002JD002423,
2003.
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.
Lehner, B. and Döll, P.: Development and validation of a global database
of lakes, reservoirs and wetlands, J. Hydrol., 296, 1–22,
https://doi.org/10.1016/j.jhydrol.2004.03.028, 2004.
Li, M., Karu, E., Brenninkmeijer, C., Fischer, H., Lelieveld, J., and
Williams, J.: Tropospheric OH and stratospheric OH and Cl concentrations
determined from CH4, CH3Cl, and SF6 measurements, npj Clim.
Atmos. Sci., 1, 29, https://doi.org/10.1038/s41612-018-0041-9, 2018.
Lin, S.-J. and Rood, R. B.: Multidimensional Flux-Form Semi-Lagrangian
transport schemes, Mon. Weather Rev., 124, 2046–2070,
https://doi.org/10.1175/1520-0493(1996)124<2046:mffslt>2.0.co;2, 1996.
Liu, M., van der A, R., van Weele, M., Eskes, H., Lu, X., Veefkind, P., de
Laat, J., Kong, H., Wang, J., Sun, J., Ding, J., Zhao, Y., and Weng, H.: A
new divergence method to quantify methane emissions using observations of
Sentinel-5P TROPOMI, Geophys. Res. Lett., 48, e2021GL094151,
https://doi.org/10.1029/2021GL094151, 2021.
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.
Lorente, A., Borsdorff, T., Martinez-Velarte, M. C., and Landgraf, J.: Accounting for surface reflectance spectral features in TROPOMI methane retrievals, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-255, in review, 2022.
Lu, X., Jacob, D. J., Zhang, Y., Maasakkers, J. D., Sulprizio, M. P., Shen,
L., Qu, Z., Scarpelli, T. R., Nesser, H., Yantosca, R. M., Sheng, J.,
Andrews, A., Parker, R. J., Boesch, H., Bloom, A. A., and Ma, S.: Global
methane budget and trend, 2010–2017: complementarity of inverse analyses
using in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT)
observations, Atmos. Chem. Phys., 21, 4637–4657,
https://doi.org/10.5194/acp-21-4637-2021, 2021.
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.
Lunt, M. F., Palmer, P. I., Lorente, A., Borsdorff, T., Landgraf, J.,
Parker, R. J., and Boesch, H.: Rain-fed pulses of methane from East Africa
during 2018–2019 contributed to atmospheric growth rate, Environ.
Res. Lett., 16, 024021, https://doi.org/10.1088/1748-9326/abd8fa, 2021.
Ma, S., Worden, J. R., Bloom, A. A., Zhang, Y., Poulter, B., Cusworth, D.
H., Yin, Y., Pandey, S., Maasakkers, J. D., Lu, X., Shen, L., Sheng, J.,
Frankenberg, C., Miller, C. E., and Jacob, D. J.: Satellite constraints on
the latitudinal distribution and temperature sensitivity of wetland methane
emissions, AGU Adv., 2, e2021AV000408, https://doi.org/10.1029/2021AV000408, 2021.
Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Scarpelli, T. R., Nesser,
H., Sheng, J. X., Zhang, Y., Hersher, M., Bloom, A. A., Bowman, K. W.,
Worden, J. R., Janssens-Maenhout, G., and Parker, R. J.: Global distribution
of methane emissions, emission trends, and OH concentrations and trends
inferred from an inversion of GOSAT satellite data for 2010–2015,
Atmos. Chem. Phys., 19, 7859–7881, https://doi.org/10.5194/acp-19-7859-2019,
2019.
Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Turner, A. J., Weitz, M.,
Wirth, T., Hight, C., DeFigueiredo, M., Desai, M., Schmeltz, R., Hockstad,
L., Bloom, A. A., Bowman, K. W., Jeong, S., and Fischer, M. L.: Gridded
national inventory of U.S. methane emissions, Environ. Sci.
Technol., 50, 13123–13133, https://doi.org/10.1021/acs.est.6b02878, 2016.
Maasakkers, J. D., Varon, D. J., Elfarsdóttir, A., McKeever, J., Jervis,
D., Mahapatra, G., Pandey, S., Lorente, A., Borsdorff, T., Foorthuis, L. R.,
Schuit, B. J., Tol, P., van Kempen, T. A., van Hees, R., and Aben, I.: Using
satellites to uncover large methane emissions from landfills, Sci.
Adv., 8, eabn9683, https://doi.org/10.1126/sciadv.abn9683, 2022.
Maps Saudi Arabia: Saudi Arabia oil fields map, https://maps-saudi-arabia.com/saudi-arabia-oil-fields-map, last access: 25 April 2022.
McNorton, J., Wilson, C., Gloor, M., Parker, R. J., Boesch, H., Feng, W.,
Hossaini, R., and Chipperfield, M. P.: Attribution of recent increases in
atmospheric methane through 3-D inverse modelling, Atmos. Chem.
Phys., 18, 18149–18168, https://doi.org/10.5194/acp-18-18149-2018, 2018.
McNorton, J., Chipperfield, M. P., Gloor, M., Wilson, C., Feng, W., Hayman,
G. D., Rigby, M., Krummel, P. B., O'Doherty, S., Prinn, R. G., Weiss, R. F.,
Young, D., Dlugokencky, E., and Montzka, S. A.: Role of OH variability in
the stalling of the global atmospheric CH4 growth rate from 1999 to 2006,
Atmos. Chem. Phys., 16, 7943–7956, https://doi.org/10.5194/acp-16-7943-2016,
2016.
Miller, S. M., Michalak, A. M., Detmers, R. G., Hasekamp, O. P., Bruhwiler,
L. M. P., and Schwietzke, S.: China's coal mine methane regulations have not
curbed growing emissions, Nat. Commun., 10, 303,
https://doi.org/10.1038/s41467-018-07891-7, 2019.
Monteil, G., Houweling, S., Butz, A., Guerlet, S., Schepers, D., Hasekamp,
O., Frankenberg, C., Scheepmaker, R., Aben, I., and Röckmann, T.:
Comparison of CH4 inversions based on 15 months of GOSAT and SCIAMACHY
observations, J. Geophys. Res.-Atmos., 118,
11807-811823, https://doi.org/10.1002/2013jd019760, 2013.
Montzka, S. A., Krol, M., Dlugokencky, E., Hall, B., Jöckel, P., and
Lelieveld, J.: Small interannual variability of global atmospheric hydroxyl,
Science, 331, 67–69, https://doi.org/10.1126/science.1197640, 2011.
Moorthi, S. and Suarez, M. J.: Relaxed Arakawa-Schubert, A parameterization
of moist convection for general circulation models, Mon. Weather Rev.,
120, 978–1002, https://doi.org/10.1175/1520-0493(1992)120<0978:rasapo>2.0.co;2, 1992.
Murray, L. T., Logan, J. A., and Jacob, D. J.: Interannual variability in
tropical tropospheric ozone and OH: The role of lightning, J. Geophys. Res.-Atmos., 118, 11468–411480,
https://doi.org/10.1002/jgrd.50857, 2013.
Naik, V., Voulgarakis, A., Fiore, A. M., Horowitz, L. W., Lamarque, J. F.,
Lin, M., Prather, M. J., Young, P. J., Bergmann, D., Cameron-Smith, P. J.,
Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty, R., Eyring, V.,
Faluvegi, G., Folberth, G. A., Josse, B., Lee, Y. H., MacKenzie, I. A.,
Nagashima, T., van Noije, T. P. C., Plummer, D. A., Righi, M., Rumbold, S.
T., Skeie, R., Shindell, D. T., Stevenson, D. S., Strode, S., Sudo, K.,
Szopa, S., and Zeng, G.: Preindustrial to present-day changes in
tropospheric hydroxyl radical and methane lifetime from the Atmospheric
Chemistry and Climate Model Intercomparison Project (ACCMIP), Atmos.
Chem. Phys., 13, 5277–5298, https://doi.org/10.5194/acp-13-5277-2013, 2013.
NEIC (National Emissions Inventory Collaborative): 2016beta emissions modeling platform, IWDW, http://views.cira.colostate.edu/wiki/wiki/10197 (last access: 3 March 2023), 2019.
Nepstad, L. S., Gerber, J. S., Hill, J. D., Dias, L. C. P., Costa, M. H.,
and West, P. C.: Pathways for recent Cerrado soybean expansion: extending
the soy moratorium and implementing integrated crop livestock systems with
soybeans, Environ. Res. Lett., 14, 044029,
https://doi.org/10.1088/1748-9326/aafb85, 2019.
Nisbet, E. G., Dlugokencky, E. J., Manning, M. R., Lowry, D., Fisher, R. E.,
France, J. L., Michel, S. E., Miller, J. B., White, J. W. C., Vaughn, B.,
Bousquet, P., Pyle, J. A., Warwick, N. J., Cain, M., Brownlow, R., Zazzeri,
G., Lanoisellé, M., Manning, A. C., Gloor, E., Worthy, D. E. J., Brunke,
E.-G., Labuschagne, C., Wolff, E. W., and Ganesan, A. L.: Rising atmospheric
methane: 2007–2014 growth and isotopic shift, Global Biogeochem. Cy.,
30, 1356–1370, https://doi.org/10.1002/2016GB005406, 2016.
NOAA Global Monitoring Laboratory: https://www.esrl.noaa.gov/gmd,
last access: 25 April 2022.
NS Energy: Khazzan Tight Gas Project,
https://www.nsenergybusiness.com/projects/khazzan-tight-gas-project-oman,
last access: 25 April 2022.
Palmer, P. I., Feng, L., Lunt, M. F., Parker, R. J., Bösch, H., Lan, X.,
Lorente, A., and Borsdorff, T.: The added value of satellite observations of
methane forunderstanding the contemporary methane budget, Philos.
T. R. Soc. A, 379, 20210106, https://doi.org/10.1098/rsta.2021.0106, 2021.
Pandey, S., Gautam, R., Houweling, S., van der Gon, H. D., Sadavarte, P.,
Borsdorff, T., Hasekamp, O., Landgraf, J., Tol, P., van Kempen, T.,
Hoogeveen, R., van Hees, R., Hamburg, S. P., Maasakkers, J. D., and Aben,
I.: Satellite observations reveal extreme methane leakage from a natural gas
well blowout, P. Natl. Acad. Sci. USA, 116,
26376–26381, https://doi.org/10.1073/pnas.1908712116, 2019.
Parker, R. J., Wilson, C., Bloom, A. A., Comyn-Platt, E., Hayman, G.,
McNorton, J., Boesch, H., and Chipperfield, M. P.: Exploring constraints on
a wetland methane emission ensemble (WetCHARTs) using GOSAT observations,
Biogeosciences, 17, 5669–5691, https://doi.org/10.5194/bg-17-5669-2020, 2020.
Patra, P. K., Krol, M. C., Prinn, R. G., Takigawa, M., Mühle, J.,
Montzka, S. A., Lal, S., Yamashita, Y., Naus, S., Chandra, N., Weiss, R. F.,
Krummel, P. B., Fraser, P. J., O'Doherty, S., and Elkins, J. W.: Methyl
chloroform continues to constrain the hydroxyl (OH) variability in the
troposphere, J. Geophys. Res.-Atmos., 126,
e2020JD033862, https://doi.org/10.1029/2020JD033862, 2021.
Peischl, J., Karion, A., Sweeney, C., Kort, E., Smith, M., Brandt, A.,
Yeskoo, T., Aikin, K., Conley, S., and Gvakharia, A.: Quantifying
atmospheric methane emissions from oil and natural gas production in the
Bakken shale region of North Dakota, J. Geophys. Res.-Atmos., 121, 6101–6111, 2016.
Peng, S., Lin, X., Thompson, R. L., Xi, Y., Liu, G., Hauglustaine, D., Lan,
X., Poulter, B., Ramonet, M., Saunois, M., Yin, Y., Zhang, Z., Zheng, B.,
and Ciais, P.: Wetland emission and atmospheric sink changes explain methane
growth in 2020, Nature, 612, 477–482, https://doi.org/10.1038/s41586-022-05447-w, 2022.
Prather, M. J., Holmes, C. D., and Hsu, J.: Reactive greenhouse gas
scenarios: Systematic exploration of uncertainties and the role of
atmospheric chemistry, Geophys. Res. Lett., 39, L09803,
https://doi.org/10.1029/2012GL051440, 2012.
Prinn, R., Huang, J., Weiss, R., Cunnold, D., Fraser, P., Simmonds, P.,
McCulloch, A., Harth, C., Salameh, P., and O'doherty, S.: Evidence for
substantial variations of atmospheric hydroxyl radicals in the past two
decades, Science, 292, 1882–1888, 2001.
Prinn, R. G., Huang, J., Weiss, R. F., Cunnold, D. M., Fraser, P. J.,
Simmonds, P. G., McCulloch, A., Harth, C., Reimann, S., Salameh, P.,
O'Doherty, S., Wang, R. H. J., Porter, L. W., Miller, B. R., and Krummel, P.
B.: Evidence for variability of atmospheric hydroxyl radicals over the past
quarter century, Geophys. Res. Lett., 32, L07809,
https://doi.org/10.1029/2004GL022228, 2005.
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.
Rigby, M., Montzka, S. A., Prinn, R. G., White, J. W. C., Young, D.,
O'Doherty, S., Lunt, M. F., Ganesan, A. L., Manning, A. J., Simmonds, P. G.,
Salameh, P. K., Harth, C. M., Mühle, J., Weiss, R. F., Fraser, P. J.,
Steele, L. P., Krummel, P. B., McCulloch, A., and Park, S.: Role of
atmospheric oxidation in recent methane growth, P. Natl.
Acad. Sci. USA, 114, 5373–5377, https://doi.org/10.1073/pnas.1616426114, 2017.
Scarpelli, T. R., Jacob, D. J., Maasakkers, J. D., Sulprizio, M. P., Sheng,
J. X., Rose, K., Romeo, L., Worden, J. R., and Janssens-Maenhout, G.: A
global gridded (0.1∘ × 0.1∘) inventory of
methane emissions from oil, gas, and coal exploitation based on national
reports to the United Nations Framework Convention on Climate Change, Earth
Syst. Sci. Data, 12, 563–575, https://doi.org/10.5194/essd-12-563-2020, 2020.
Scarpelli, T. R., Jacob, D. J., Grossman, S., Lu, X., Qu, Z., Sulprizio, M.
P., Zhang, Y., Reuland, F., Gordon, D., and Worden, J. R.: Updated Global
Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas,
and coal sectors: evaluation with inversions of atmospheric methane
observations, Atmos. Chem. Phys., 22, 3235–3249,
https://doi.org/10.5194/acp-22-3235-2022, 2022.
Schaefer, H., Fletcher, S. E. M., Veidt, C., Lassey, K. R., Brailsford, G.
W., Bromley, T. M., Dlugokencky, E. J., Michel, S. E., Miller, J. B., Levin,
I., Lowe, D. C., Martin, R. J., Vaughn, B. H., and White, J. W. C.: A
21st-century shift from fossil-fuel to biogenic methane emissions indicated
by 13CH4, Science, 352, 80–84, https://doi.org/10.1126/science.aad2705, 2016.
Schneising, O., Buchwitz, M., Reuter, M., Vanselow, S., Bovensmann, H., and
Burrows, J. P.: Remote sensing of methane leakage from natural gas and
petroleum systems revisited, Atmos. Chem. Phys., 20,
9169–9182, https://doi.org/10.5194/acp-20-9169-2020, 2020.
Sentinel-5 Precursor/TROPOMI Level 2 Product User Manual: Methane [data set]:
https://sentinel.esa.int/documents/,
last access: 29 November 2022.
Shen, L., Zavala-Araiza, D., Gautam, R., Omara, M., Scarpelli, T., Sheng,
J., Sulprizio, M. P., Zhuang, J., Zhang, Y., Qu, Z., Lu, X., Hamburg, S. P.,
and Jacob, D. J.: Unravelling a large methane emission discrepancy in Mexico
using satellite observations, Remote Sens. Environ., 260, 112461,
https://doi.org/10.1016/j.rse.2021.112461, 2021.
Sheng, J., Song, S., Zhang, Y., Prinn, R. G., and Janssens-Maenhout, G.:
Bottom-Up estimates of coal mine methane emissions in China: a gridded
inventory, emission factors, and trends, Environ. Sci.
Technol. Lett., 6, 473–478, https://doi.org/10.1021/acs.estlett.9b00294, 2019.
Sheng, J.-X., Jacob, D. J., Maasakkers, J. D., Sulprizio, M. P.,
Zavala-Araiza, D., and Hamburg, S. P.: A high-resolution (0.1∘ × 0.1∘) inventory of methane emissions from Canadian
and Mexican oil and gas systems, Atmos. Environ., 158, 211–215,
https://doi.org/10.1016/j.atmosenv.2017.02.036, 2017.
Sheng, J. X., Jacob, D. J., Turner, A. J., Maasakkers, J. D., Benmergui, J.,
Bloom, A. A., Arndt, C., Gautam, R., Zavala-Araiza, D., Boesch, H., and
Parker, R. J.: 2010–2016 methane trends over Canada, the United States, and
Mexico observed by the GOSAT satellite: contributions from different source
sectors, Atmos. Chem. Phys., 18, 12257–12267,
https://doi.org/10.5194/acp-18-12257-2018, 2018.
Sherwen, T., Schmidt, J. A., Evans, M. J., Carpenter, L. J., Großmann,
K., Eastham, S. D., Jacob, D. J., Dix, B., Koenig, T. K., Sinreich, R.,
Ortega, I., Volkamer, R., Saiz-Lopez, A., Prados-Roman, C., Mahajan, A. S.,
and Ordóñez, C.: Global impacts of tropospheric halogens (Cl, Br, I)
on oxidants and composition in GEOS-Chem, Atmos. Chem. Phys.,
16, 12239–12271, https://doi.org/10.5194/acp-16-12239-2016, 2016.
Sonavale, K., Shaikh, M., Kadam, M., and Pokharkar, V.: Livestock sector in
India: a critical analysis, Asian J. Agr. Extens., 38,
51–62, 2020.
Stevenson, D. S., Derwent, R. G., Wild, O., and Collins, W. J.: COVID-19 lockdown emission reductions have the potential to explain over half of the coincident increase in global atmospheric methane, Atmos. Chem. Phys., 22, 14243–14252, https://doi.org/10.5194/acp-22-14243-2022, 2022.
Sun, K., Zhu, L., Cady-Pereira, K., Chan Miller, C., Chance, K., Clarisse,
L., Coheur, P. F., González Abad, G., Huang, G., Liu, X., Van Damme, M.,
Yang, K., and Zondlo, M.: A physics-based approach to oversample
multi-satellite, multispecies observations to a common grid, Atmos.
Meas. Tech., 11, 6679–6701, https://doi.org/10.5194/amt-11-6679-2018, 2018.
TCCON: Total Carbon Column Observing Network [data set],
https://tccondata.org/2014 (last access: 25 April 2022), 2014.
Turner, A. J.,
Frankenberg, C., Wennberg, P. O., and Jacob, D. J.: Ambiguity in the causes
for decadal trends in atmospheric methane and hydroxyl, P.
Natl. Acad. Sci. USA, 114, 5367–5372, https://doi.org/10.1073/pnas.1616020114, 2017.
Turner, A. J., Fung, I., Naik, V., Horowitz, L. W., and Cohen, R. C.:
Modulation of hydroxyl variability by ENSO in the absence of external
forcing, P.
Natl. Acad. Sci. USA, 115, 8931–8936,
https://doi.org/10.1073/pnas.1807532115, 2018a.
Turner, A. J., Jacob, D. J., Benmergui, J., Brandman, J., White, L., and
Randles, C. A.: Assessing the capability of different satellite observing
configurations to resolve the distribution of methane emissions at kilometer
scales, Atmos. Chem. Phys., 18, 8265–8278,
https://doi.org/10.5194/acp-18-8265-2018, 2018b.
Turner, A. J., Jacob, D. J., Wecht, K. J., Maasakkers, J. D., Lundgren, E.,
Andrews, A. E., Biraud, S. C., Boesch, H., Bowman, K. W., Deutscher, N. M.,
Dubey, M. K., Griffith, D. W. T., Hase, F., Kuze, A., Notholt, J., Ohyama,
H., Parker, R., Payne, V. H., Sussmann, R., Sweeney, C., Velazco, V. A.,
Warneke, T., Wennberg, P. O., and Wunch, D.: Estimating global and North
American methane emissions with high spatial resolution using GOSAT
satellite data, Atmos. Chem. Phys., 15, 7049–7069,
https://doi.org/10.5194/acp-15-7049-2015, 2015.
UNFCCC: United Nations Framework Convention on Climate Change,
https://unfccc.int, last access: 25 April 2021.
Varon, D. J., McKeever, J., Jervis, D., Maasakkers, J. D., Pandey, S.,
Houweling, S., Aben, I., Scarpelli, T., and Jacob, D. J.: Satellite
discovery of anomalously large methane point sources from oil/gas
production, Geophys. Res. Lett., 46, 13507–13516,
https://doi.org/10.1029/2019gl083798, 2019.
Wecht, K. J., Jacob, D. J., Frankenberg, C., Jiang, Z., and Blake, D. R.:
Mapping of North American methane emissions with high spatial resolution by
inversion of SCIAMACHY satellite data, J. Geophys. Res.-Atmos., 119, 7741–7756, https://doi.org/10.1002/2014jd021551, 2014.
Wolfe, G. M., Nicely, J. M., St. Clair, J. M., Hanisco, T. F., Liao, J.,
Oman, L. D., Brune, W. B., Miller, D., Thames, A., González Abad, G.,
Ryerson, T. B., Thompson, C. R., Peischl, J., McKain, K., Sweeney, C.,
Wennberg, P. O., Kim, M., Crounse, J. D., Hall, S. R., Ullmann, K., Diskin,
G., Bui, P., Chang, C., and Dean-Day, J.: Mapping hydroxyl variability
throughout the global remote troposphere via synthesis of airborne and
satellite formaldehyde observations, P. Natl. Acad.
Sci. USA, 116, 11171–11180, https://doi.org/10.1073/pnas.1821661116, 2019.
Worden, J. R., Cusworth, D. H., Qu, Z., Yin, Y., Zhang, Y., Bloom, A. A.,
Ma, S., Byrne, B. K., Scarpelli, T., Maasakkers, J. D., Crisp, D., Duren,
R., and Jacob, D. J.: The 2019 methane budget and uncertainties at
1∘ resolution and each country through Bayesian integration Of
GOSAT total column methane data and a priori inventory estimates,
Atmos. Chem. Phys., 22, 6811–6841, https://doi.org/10.5194/acp-22-6811-2022,
2022.
Wu, S., Mickley, L. J., Jacob, D. J., Logan, J. A., Yantosca, R. M., and
Rind, D.: Why are there large differences between models in global budgets
of tropospheric ozone?, J. Geophys. Res.-Atmos., 112,
D05302, https://doi.org/10.1029/2006JD007801, 2007.
Xiao, Y., Logan, J. A., Jacob, D. J., Hudman, R. C., Yantosca, R., and
Blake, D. R.: Global budget of ethane and regional constraints on U.S.
sources, J. Geophys. Res.-Atmos., 113, D21306,
https://doi.org/10.1029/2007jd009415, 2008.
Yu, X., Millet, D. B., and Henze, D. K.: How well can inverse analyses of
high-resolution satellite data resolve heterogeneous methane fluxes?
Observing system simulation experiments with the GEOS-Chem adjoint model
(v35), Geosci. Model Dev., 14, 7775–7793,
https://doi.org/10.5194/gmd-14-7775-2021, 2021a.
Yu, X., Millet, D. B., and Henze, D. K.: Code updates of GEOS-Chem Adjoint
v35 for TROPOMI methane 4D-Var Inversion, University of Minnesota [code], https://doi.org/10.13020/g5xc-nj81, 2021b.
Yu, X., Millet, D. B., Wells, K. C., Griffis, T. J., Chen, X., Baker, J. M.,
Conley, S. A., Smith, M. L., Gvakharia, A., Kort, E. A., Plant, G., and
Wood, J. D.: Top-Down constraints on methane point source emissions from
animal agriculture and waste based on new airborne measurements in the U.S.
Upper Midwest, J. Geophys. Res.-Biogeo., 125,
e2019JG005429, https://doi.org/10.1029/2019jg005429, 2020.
Yu, X., Millet, D. B., Wells, K. C., Henze, D. K., Cao, H., Griffis, T. J.,
Kort, E. A., Plant, G., Deventer, M. J., Kolka, R. K., Roman, D. T., Davis,
K. J., Desai, A. R., Baier, B. C., McKain, K., Czarnetzki, A. C., and Bloom,
A. A.: Aircraft-based inversions quantify the importance of wetlands and
livestock for Upper Midwest methane emissions, Atmos. Chem.
Phys., 21, 951–971, https://doi.org/10.5194/acp-21-951-2021, 2021c.
Zavala-Araiza, D., Omara, M., Gautam, R., Smith, M. L., Pandey, S., Aben,
I., Almanza-Veloz, V., Conley, S., Houweling, S., Kort, E. A., Maasakkers,
J. D., Molina, L. T., Pusuluri, A., Scarpelli, T., Schwietzke, S., Shen, L.,
Zavala, M., and Hamburg, S. P.: A tale of two regions: methane emissions
from oil and gas production in offshore/onshore Mexico, Environ.
Res. Lett., 16, 024019, https://doi.org/10.1088/1748-9326/abceeb, 2021.
Zhang, Y., Jacob, D. J., Maasakkers, J. D., Sulprizio, M. P., Sheng, J. X.,
Gautam, R., and Worden, J.: Monitoring global tropospheric OH concentrations
using satellite observations of atmospheric methane, Atmos. Chem.
Phys., 18, 15959–15973, https://doi.org/10.5194/acp-18-15959-2018, 2018.
Zhang, Y., Jacob, D. J., Lu, X., Maasakkers, J. D., Scarpelli, T. R., Sheng,
J. X., Shen, L., Qu, Z., Sulprizio, M. P., Chang, J., Bloom, A. A., Ma, S.,
Worden, J., Parker, R. J., and Boesch, H.: Attribution of the accelerating
increase in atmospheric methane during 2010–2018 by inverse analysis of
GOSAT observations, Atmos. Chem. Phys., 21, 3643–3666,
https://doi.org/10.5194/acp-21-3643-2021, 2021.
Zhang, Y., Gautam, R., Pandey, S., Omara, M., Maasakkers, J. D., Sadavarte,
P., Lyon, D., Nesser, H., Sulprizio, M. P., Varon, D. J., Zhang, R.,
Houweling, S., Zavala-Araiza, D., Alvarez, R. A., Lorente, A., Hamburg, S.
P., Aben, I., and Jacob, D. J.: Quantifying methane emissions from the
largest oil-producing basin in the United States from space, Sci.
Adv., 6, eaaz5120, https://doi.org/10.1126/sciadv.aaz5120, 2020.
Zhao, Y., Saunois, M., Bousquet, P., Lin, X., Berchet, A., Hegglin, M. I.,
Canadell, J. G., Jackson, R. B., Hauglustaine, D. A., Szopa, S., Stavert, A.
R., Abraham, N. L., Archibald, A. T., Bekki, S., Deushi, M., Jöckel, P.,
Josse, B., Kinnison, D., Kirner, O., Marécal, V., O'Connor, F. M.,
Plummer, D. A., Revell, L. E., Rozanov, E., Stenke, A., Strode, S., Tilmes,
S., Dlugokencky, E. J., and Zheng, B.: Inter-model comparison of global
hydroxyl radical (OH) distributions and their impact on atmospheric methane
over the 2000–2016 period, Atmos. Chem. Phys., 19,
13701–13723, https://doi.org/10.5194/acp-19-13701-2019, 2019.
Short summary
We combine satellite measurements with a novel downscaling method to map global methane emissions at 0.1°×0.1° resolution. These fine-scale emission estimates reveal unreported emission hotspots and shed light on the roles of agriculture, wetlands, and fossil fuels for regional methane budgets. The satellite-derived emissions point in particular to missing fossil fuel emissions in the Middle East and to a large emission underestimate in South Asia that appears to be tied to monsoon rainfall.
We combine satellite measurements with a novel downscaling method to map global methane...
Altmetrics
Final-revised paper
Preprint