Articles | Volume 21, issue 3
https://doi.org/10.5194/acp-21-1717-2021
© Author(s) 2021. 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-21-1717-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Atmospheric-methane source and sink sensitivity analysis using Gaussian process emulation
School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK
Luke M. Western
School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK
Tomás Sherwen
Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York, YO10 5DD, UK
National Centre for Atmospheric Science, University of York, York, YO10 5DD, UK
School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK
Related authors
Angharad C. Stell, Michael Bertolacci, Andrew Zammit-Mangion, Matthew Rigby, Paul J. Fraser, Christina M. Harth, Paul B. Krummel, Xin Lan, Manfredi Manizza, Jens Mühle, Simon O'Doherty, Ronald G. Prinn, Ray F. Weiss, Dickon Young, and Anita L. Ganesan
Atmos. Chem. Phys., 22, 12945–12960, https://doi.org/10.5194/acp-22-12945-2022, https://doi.org/10.5194/acp-22-12945-2022, 2022
Short summary
Short summary
Nitrous oxide is a potent greenhouse gas and ozone-depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions. We derive increasing global nitrous oxide emissions over 2011–2020, which are mainly driven by emissions between 0° and 30°N, with the highest emissions recorded in 2020.
Elena Fillola, Raul Santos-Rodriguez, Rachel Tunnicliffe, Jeffrey Clark, Nawid Keshtmand, Anita Ganesan, and Matthew Rigby
EGUsphere, https://doi.org/10.5194/egusphere-2025-2392, https://doi.org/10.5194/egusphere-2025-2392, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Satellite-based greenhouse gas measurements can be used in “inverse models” to improve emissions reporting, but one of the key components, the simulations of atmospheric transport, struggle to scale to large datasets. We introduce GATES, an AI-driven emulator that outputs transport plumes about 1000× faster than traditional models. Applied to Brazil’s methane emissions, GATES produces estimates consistent with physics-based methods, offering a scalable path for timely emissions monitoring.
Luke M. Western, Stephen Bourguet, Molly Crotwell, Lei Hu, Paul B. Krummel, Hélène De Longueville, Alistair J. Mainning, Jens Mühle, Dominique Rust, Isaac Vimont, Martin K. Vollmer, Minde An, Jgor Arduini, Andreas Engel, Paul J. Fraser, Anita L. Ganesan, Christina M. Harth, Chris Lunder, Michela Maione, Stephen A. Montzka, David Nance, Simon O’Doherty, Sunyoung Park, Stefan Reimann, Peter K. Salameh, Roland Schmidt, Kieran M. Stanley, Thomas Wagenhäuser, Dickon Young, Matt Rigby, Ronald G. Prinn, and Ray F. Weiss
EGUsphere, https://doi.org/10.5194/egusphere-2025-3000, https://doi.org/10.5194/egusphere-2025-3000, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We used atmospheric measurements to estimate emissions of two gases, called HCFC-123 and HCFC-124, that harm the ozone layer. Despite international regulation to stop their production, we found that their emissions have not fallen. This may be linked to how they are used to make other chemicals. Our findings show that some banned substances are still reaching the atmosphere, likely through leaks during chemical production, which could slow the recovery of the ozone layer.
Luke M. Western, Matthew Rigby, Jens Mühle, Paul B. Krummel, Chris R. Lunder, Simon O'Doherty, Stefan Reimann, Martin K. Vollmer, Dickon Young, Ben Adam, Paul J. Fraser, Anita L. Ganesan, Christina M. Harth, Ove Hermansen, Jooil Kim, Ray L. Langenfelds, Zoë M. Loh, Blagoj Mitrevski, Joseph R. Pitt, Peter K. Salameh, Roland Schmidt, Kieran Stanley, Ann R. Stavert, Hsiang-Jui Wang, Ray F. Weiss, and Ronald G. Prinn
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-348, https://doi.org/10.5194/essd-2025-348, 2025
Preprint under review for ESSD
Short summary
Short summary
We used global measurements and an atmospheric model to estimate how emissions and abundances of 42 chemically and radiatively important trace gases have changed over time. These gases affect the Earth's radiative balance and the ozone layer. Our data sets help track progress in reducing harmful. This work supports international efforts to protect the environment by providing clear, long-term, consistent data on how these gases are changing in the atmosphere.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Christophe Cassou, Mathias Hauser, Zeke Hausfather, June-Yi Lee, Matthew D. Palmer, Karina von Schuckmann, Aimée B. A. Slangen, Sophie Szopa, Blair Trewin, Jeongeun Yun, Nathan P. Gillett, Stuart Jenkins, H. Damon Matthews, Krishnan Raghavan, Aurélien Ribes, Joeri Rogelj, Debbie Rosen, Xuebin Zhang, Myles Allen, Lara Aleluia Reis, Robbie M. Andrew, Richard A. Betts, Alex Borger, Jiddu A. Broersma, Samantha N. Burgess, Lijing Cheng, Pierre Friedlingstein, Catia M. Domingues, Marco Gambarini, Thomas Gasser, Johannes Gütschow, Masayoshi Ishii, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Aurélien Liné, Didier P. Monselesan, Colin Morice, Jens Mühle, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Jan C. Minx, Matthew Rigby, Robert Rohde, Abhishek Savita, Sonia I. Seneviratne, Peter Thorne, Christopher Wells, Luke M. Western, Guido R. van der Werf, Susan E. Wijffels, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 17, 2641–2680, https://doi.org/10.5194/essd-17-2641-2025, https://doi.org/10.5194/essd-17-2641-2025, 2025
Short summary
Short summary
In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. Here we compile monitoring datasets to track real-world changes over time. To make our work relevant to policymakers, we follow methods from the Intergovernmental Panel on Climate Change (IPCC). Human activities are increasing the Earth's energy imbalance and driving faster sea-level rise compared to the IPCC assessment.
Helen Walter-Terrinoni, John S. Daniel, Chelsea R. Thompson, and Luke M. Western
EGUsphere, https://doi.org/10.5194/egusphere-2025-297, https://doi.org/10.5194/egusphere-2025-297, 2025
Short summary
Short summary
We have developed a model to improve our ability to estimate emissions of chemicals that are used as foam blowing agents. Some of these chemicals are ozone-depleting substances and some are greenhouse gases. For HCFC-141b, which is the focus of this study, we find that recent observations are inconsistent with our calculated emissions, with our emissions being lower. This mismatch is similar to previous findings and may have important implications for compliance with the Montreal Protocol.
Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
Earth Syst. Sci. Data, 16, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024, https://doi.org/10.5194/essd-16-2543-2024, 2024
Short summary
Short summary
Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
Hannah Chawner, Eric Saboya, Karina E. Adcock, Tim Arnold, Yuri Artioli, Caroline Dylag, Grant L. Forster, Anita Ganesan, Heather Graven, Gennadi Lessin, Peter Levy, Ingrid T. Luijkx, Alistair Manning, Penelope A. Pickers, Chris Rennick, Christian Rödenbeck, and Matthew Rigby
Atmos. Chem. Phys., 24, 4231–4252, https://doi.org/10.5194/acp-24-4231-2024, https://doi.org/10.5194/acp-24-4231-2024, 2024
Short summary
Short summary
The quantity of atmospheric potential oxygen (APO), derived from coincident measurements of carbon dioxide (CO2) and oxygen (O2), has been proposed as a tracer for fossil fuel CO2 emissions. In this model sensitivity study, we examine the use of APO for this purpose in the UK and compare our model to observations. We find that our model simulations are most sensitive to uncertainties relating to ocean fluxes and boundary conditions.
Tanja J. Schuck, Johannes Degen, Eric Hintsa, Peter Hoor, Markus Jesswein, Timo Keber, Daniel Kunkel, Fred Moore, Florian Obersteiner, Matt Rigby, Thomas Wagenhäuser, Luke M. Western, Andreas Zahn, and Andreas Engel
Atmos. Chem. Phys., 24, 689–705, https://doi.org/10.5194/acp-24-689-2024, https://doi.org/10.5194/acp-24-689-2024, 2024
Short summary
Short summary
We study the interhemispheric gradient of sulfur hexafluoride (SF6), a strong long-lived greenhouse gas. Its emissions are stronger in the Northern Hemisphere; therefore, mixing ratios in the Southern Hemisphere lag behind. Comparing the observations to a box model, the model predicts air in the Southern Hemisphere to be older. For a better agreement, the emissions used as model input need to be increased (and their spatial pattern changed), and we need to modify north–south transport.
Alison L. Redington, Alistair J. Manning, Stephan Henne, Francesco Graziosi, Luke M. Western, Jgor Arduini, Anita L. Ganesan, Christina M. Harth, Michela Maione, Jens Mühle, Simon O'Doherty, Joseph Pitt, Stefan Reimann, Matthew Rigby, Peter K. Salameh, Peter G. Simmonds, T. Gerard Spain, Kieran Stanley, Martin K. Vollmer, Ray F. Weiss, and Dickon Young
Atmos. Chem. Phys., 23, 7383–7398, https://doi.org/10.5194/acp-23-7383-2023, https://doi.org/10.5194/acp-23-7383-2023, 2023
Short summary
Short summary
Chlorofluorocarbons (CFCs) were used in Europe pre-1990, damaging the stratospheric ozone layer. Legislation has controlled production and use, and global emissions have decreased sharply. The global rate of decline in CFC-11 recently slowed and was partly attributed to illegal emission in eastern China. This study concludes that emissions of CFC-11 in western Europe have not contributed to the unexplained part of the global increase in CFC-11 observed in the last decade.
Elena Fillola, Raul Santos-Rodriguez, Alistair Manning, Simon O'Doherty, and Matt Rigby
Geosci. Model Dev., 16, 1997–2009, https://doi.org/10.5194/gmd-16-1997-2023, https://doi.org/10.5194/gmd-16-1997-2023, 2023
Short summary
Short summary
Lagrangian particle dispersion models are used extensively for the estimation of greenhouse gas (GHG) fluxes using atmospheric observations. However, these models do not scale well as data volumes increase. Here, we develop a proof-of-concept machine learning emulator that can produce outputs similar to those of the dispersion model, but 50 000 times faster, using only meteorological inputs. This works demonstrates the potential of machine learning to accelerate GHG estimations across the globe.
Viral Shah, Daniel J. Jacob, Ruijun Dang, Lok N. Lamsal, Sarah A. Strode, Stephen D. Steenrod, K. Folkert Boersma, Sebastian D. Eastham, Thibaud M. Fritz, Chelsea Thompson, Jeff Peischl, Ilann Bourgeois, Ilana B. Pollack, Benjamin A. Nault, Ronald C. Cohen, Pedro Campuzano-Jost, Jose L. Jimenez, Simone T. Andersen, Lucy J. Carpenter, Tomás Sherwen, and Mat J. Evans
Atmos. Chem. Phys., 23, 1227–1257, https://doi.org/10.5194/acp-23-1227-2023, https://doi.org/10.5194/acp-23-1227-2023, 2023
Short summary
Short summary
NOx in the free troposphere (above 2 km) affects global tropospheric chemistry and the retrieval and interpretation of satellite NO2 measurements. We evaluate free tropospheric NOx in global atmospheric chemistry models and find that recycling NOx from its reservoirs over the oceans is faster than that simulated in the models, resulting in increases in simulated tropospheric ozone and OH. Over the U.S., free tropospheric NO2 contributes the majority of the tropospheric NO2 column in summer.
Simone T. Andersen, Beth S. Nelson, Katie A. Read, Shalini Punjabi, Luis Neves, Matthew J. Rowlinson, James Hopkins, Tomás Sherwen, Lisa K. Whalley, James D. Lee, and Lucy J. Carpenter
Atmos. Chem. Phys., 22, 15747–15765, https://doi.org/10.5194/acp-22-15747-2022, https://doi.org/10.5194/acp-22-15747-2022, 2022
Short summary
Short summary
The cycling of NO and NO2 is important to understand to be able to predict O3 concentrations in the atmosphere. We have used long-term measurements from the Cape Verde Atmospheric Observatory together with model outputs to investigate the cycling of nitrogen oxide (NO) and nitrogen dioxide (NO2) in very clean marine air. This study shows that we understand the processes occurring in very clean air, but with small amounts of pollution in the air, known chemistry cannot explain what is observed.
William F. Swanson, Chris D. Holmes, William R. Simpson, Kaitlyn Confer, Louis Marelle, Jennie L. Thomas, Lyatt Jaeglé, Becky Alexander, Shuting Zhai, Qianjie Chen, Xuan Wang, and Tomás Sherwen
Atmos. Chem. Phys., 22, 14467–14488, https://doi.org/10.5194/acp-22-14467-2022, https://doi.org/10.5194/acp-22-14467-2022, 2022
Short summary
Short summary
Radical bromine molecules are seen at higher concentrations during the Arctic spring. We use the global model GEOS-Chem to test whether snowpack and wind-blown snow sources can explain high bromine concentrations. We run this model for the entire year of 2015 and compare results to observations of bromine from floating platforms on the Arctic Ocean and at Utqiaġvik. We find that the model performs best when both sources are enabled but may overestimate bromine production in summer and fall.
Angharad C. Stell, Michael Bertolacci, Andrew Zammit-Mangion, Matthew Rigby, Paul J. Fraser, Christina M. Harth, Paul B. Krummel, Xin Lan, Manfredi Manizza, Jens Mühle, Simon O'Doherty, Ronald G. Prinn, Ray F. Weiss, Dickon Young, and Anita L. Ganesan
Atmos. Chem. Phys., 22, 12945–12960, https://doi.org/10.5194/acp-22-12945-2022, https://doi.org/10.5194/acp-22-12945-2022, 2022
Short summary
Short summary
Nitrous oxide is a potent greenhouse gas and ozone-depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions. We derive increasing global nitrous oxide emissions over 2011–2020, which are mainly driven by emissions between 0° and 30°N, with the highest emissions recorded in 2020.
Luke M. Western, Alison L. Redington, Alistair J. Manning, Cathy M. Trudinger, Lei Hu, Stephan Henne, Xuekun Fang, Lambert J. M. Kuijpers, Christina Theodoridi, David S. Godwin, Jgor Arduini, Bronwyn Dunse, Andreas Engel, Paul J. Fraser, Christina M. Harth, Paul B. Krummel, Michela Maione, Jens Mühle, Simon O'Doherty, Hyeri Park, Sunyoung Park, Stefan Reimann, Peter K. Salameh, Daniel Say, Roland Schmidt, Tanja Schuck, Carolina Siso, Kieran M. Stanley, Isaac Vimont, Martin K. Vollmer, Dickon Young, Ronald G. Prinn, Ray F. Weiss, Stephen A. Montzka, and Matthew Rigby
Atmos. Chem. Phys., 22, 9601–9616, https://doi.org/10.5194/acp-22-9601-2022, https://doi.org/10.5194/acp-22-9601-2022, 2022
Short summary
Short summary
The production of ozone-destroying gases is being phased out. Even though production of one of the main ozone-depleting gases, called HCFC-141b, has been declining for many years, the amount that is being released to the atmosphere has been increasing since 2017. We do not know for sure why this is. A possible explanation is that HCFC-141b that was used to make insulating foams many years ago is only now escaping to the atmosphere, or a large part of its production is not being reported.
Guus J. M. Velders, John S. Daniel, Stephen A. Montzka, Isaac Vimont, Matthew Rigby, Paul B. Krummel, Jens Muhle, Simon O'Doherty, Ronald G. Prinn, Ray F. Weiss, and Dickon Young
Atmos. Chem. Phys., 22, 6087–6101, https://doi.org/10.5194/acp-22-6087-2022, https://doi.org/10.5194/acp-22-6087-2022, 2022
Short summary
Short summary
The emissions of hydrofluorocarbons (HFCs) have increased significantly in the past as a result of the phasing out of ozone-depleting substances. Observations indicate that HFCs are used much less in certain refrigeration applications than previously projected. Current policies are projected to reduce emissions and the surface temperature contribution of HFCs from 0.28–0.44 °C to 0.14–0.31 °C in 2100. The Kigali Amendment is projected to reduce the contributions further to 0.04 °C in 2100.
Alice E. Ramsden, Anita L. Ganesan, Luke M. Western, Matthew Rigby, Alistair J. Manning, Amy Foulds, James L. France, Patrick Barker, Peter Levy, Daniel Say, Adam Wisher, Tim Arnold, Chris Rennick, Kieran M. Stanley, Dickon Young, and Simon O'Doherty
Atmos. Chem. Phys., 22, 3911–3929, https://doi.org/10.5194/acp-22-3911-2022, https://doi.org/10.5194/acp-22-3911-2022, 2022
Short summary
Short summary
Quantifying methane emissions from different sources is a key focus of current research. We present a method for estimating sectoral methane emissions that uses ethane as a tracer for fossil fuel methane. By incorporating variable ethane : methane emission ratios into this model, we produce emissions estimates with improved uncertainty characterisation. This method will be particularly useful for studying methane emissions in areas with complex distributions of sources.
Jens Mühle, Lambert J. M. Kuijpers, Kieran M. Stanley, Matthew Rigby, Luke M. Western, Jooil Kim, Sunyoung Park, Christina M. Harth, Paul B. Krummel, Paul J. Fraser, Simon O'Doherty, Peter K. Salameh, Roland Schmidt, Dickon Young, Ronald G. Prinn, Ray H. J. Wang, and Ray F. Weiss
Atmos. Chem. Phys., 22, 3371–3378, https://doi.org/10.5194/acp-22-3371-2022, https://doi.org/10.5194/acp-22-3371-2022, 2022
Short summary
Short summary
Emissions of the strong greenhouse gas perfluorocyclobutane (c-C4F8) into the atmosphere have been increasing sharply since the early 2000s. These c-C4F8 emissions are highly correlated with the amount of hydrochlorofluorocarbon-22 produced to synthesize polytetrafluoroethylene (known for its non-stick properties) and related chemicals. From this process, c-C4F8 by-product is vented to the atmosphere. Avoiding these unnecessary c-C4F8 emissions could reduce the climate impact of this industry.
Andrew Zammit-Mangion, Michael Bertolacci, Jenny Fisher, Ann Stavert, Matthew Rigby, Yi Cao, and Noel Cressie
Geosci. Model Dev., 15, 45–73, https://doi.org/10.5194/gmd-15-45-2022, https://doi.org/10.5194/gmd-15-45-2022, 2022
Short summary
Short summary
We present a framework for estimating the sources and sinks (flux) of carbon dioxide from satellite data. The framework is statistical and yields measures of uncertainty alongside all estimates of flux and other parameters in the underlying model. It also allows us to generate other insights, such as the size of errors and biases in the data. The primary aim of this research was to develop a fully statistical flux inversion framework for use by atmospheric scientists.
Jan C. Minx, William F. Lamb, Robbie M. Andrew, Josep G. Canadell, Monica Crippa, Niklas Döbbeling, Piers M. Forster, Diego Guizzardi, Jos Olivier, Glen P. Peters, Julia Pongratz, Andy Reisinger, Matthew Rigby, Marielle Saunois, Steven J. Smith, Efisio Solazzo, and Hanqin Tian
Earth Syst. Sci. Data, 13, 5213–5252, https://doi.org/10.5194/essd-13-5213-2021, https://doi.org/10.5194/essd-13-5213-2021, 2021
Short summary
Short summary
We provide a synthetic dataset on anthropogenic greenhouse gas (GHG) emissions for 1970–2018 with a fast-track extension to 2019. We show that GHG emissions continued to rise across all gases and sectors. Annual average GHG emissions growth slowed, but absolute decadal increases have never been higher in human history. We identify a number of data gaps and data quality issues in global inventories and highlight their importance for monitoring progress towards international climate goals.
Mark F. Lunt, Alistair J. Manning, Grant Allen, Tim Arnold, Stéphane J.-B. Bauguitte, Hartmut Boesch, Anita L. Ganesan, Aoife Grant, Carole Helfter, Eiko Nemitz, Simon J. O'Doherty, Paul I. Palmer, Joseph R. Pitt, Chris Rennick, Daniel Say, Kieran M. Stanley, Ann R. Stavert, Dickon Young, and Matt Rigby
Atmos. Chem. Phys., 21, 16257–16276, https://doi.org/10.5194/acp-21-16257-2021, https://doi.org/10.5194/acp-21-16257-2021, 2021
Short summary
Short summary
We present an evaluation of the UK's methane emissions between 2013 and 2020 using a network of tall tower measurement sites. We find emissions that are consistent in both magnitude and trend with the UK's reported emissions, with a declining trend driven by a decrease in emissions from England. The impact of various components of the modelling set-up on these findings are explored through a number of sensitivity studies.
Arseniy Karagodin-Doyennel, Eugene Rozanov, Timofei Sukhodolov, Tatiana Egorova, Alfonso Saiz-Lopez, Carlos A. Cuevas, Rafael P. Fernandez, Tomás Sherwen, Rainer Volkamer, Theodore K. Koenig, Tanguy Giroud, and Thomas Peter
Geosci. Model Dev., 14, 6623–6645, https://doi.org/10.5194/gmd-14-6623-2021, https://doi.org/10.5194/gmd-14-6623-2021, 2021
Short summary
Short summary
Here, we present the iodine chemistry module in the SOCOL-AERv2 model. The obtained iodine distribution demonstrated a good agreement when validated against other simulations and available observations. We also estimated the iodine influence on ozone in the case of present-day iodine emissions, the sensitivity of ozone to doubled iodine emissions, and when considering only organic or inorganic iodine sources. The new model can be used as a tool for further studies of iodine effects on ozone.
Xuan Wang, Daniel J. Jacob, William Downs, Shuting Zhai, Lei Zhu, Viral Shah, Christopher D. Holmes, Tomás Sherwen, Becky Alexander, Mathew J. Evans, Sebastian D. Eastham, J. Andrew Neuman, Patrick R. Veres, Theodore K. Koenig, Rainer Volkamer, L. Gregory Huey, Thomas J. Bannan, Carl J. Percival, Ben H. Lee, and Joel A. Thornton
Atmos. Chem. Phys., 21, 13973–13996, https://doi.org/10.5194/acp-21-13973-2021, https://doi.org/10.5194/acp-21-13973-2021, 2021
Short summary
Short summary
Halogen radicals have a broad range of implications for tropospheric chemistry, air quality, and climate. We present a new mechanistic description and comprehensive simulation of tropospheric halogens in a global 3-D model and compare the model results with surface and aircraft measurements. We find that halogen chemistry decreases the global tropospheric burden of ozone by 11 %, NOx by 6 %, and OH by 4 %.
Daniel Say, Alistair J. Manning, Luke M. Western, Dickon Young, Adam Wisher, Matthew Rigby, Stefan Reimann, Martin K. Vollmer, Michela Maione, Jgor Arduini, Paul B. Krummel, Jens Mühle, Christina M. Harth, Brendan Evans, Ray F. Weiss, Ronald G. Prinn, and Simon O'Doherty
Atmos. Chem. Phys., 21, 2149–2164, https://doi.org/10.5194/acp-21-2149-2021, https://doi.org/10.5194/acp-21-2149-2021, 2021
Short summary
Short summary
Perfluorocarbons (PFCs) are potent greenhouse gases with exceedingly long lifetimes. We used atmospheric measurements from a global monitoring network to track the accumulation of these gases in the atmosphere. In the case of the two most abundant PFCs, recent measurements indicate that global emissions are increasing. In Europe, we used a model to estimate regional PFC emissions. Our results show that there was no significant decline in northwest European PFC emissions between 2010 and 2019.
Rachel L. Tunnicliffe, Anita L. Ganesan, Robert J. Parker, Hartmut Boesch, Nicola Gedney, Benjamin Poulter, Zhen Zhang, Jošt V. Lavrič, David Walter, Matthew Rigby, Stephan Henne, Dickon Young, and Simon O'Doherty
Atmos. Chem. Phys., 20, 13041–13067, https://doi.org/10.5194/acp-20-13041-2020, https://doi.org/10.5194/acp-20-13041-2020, 2020
Short summary
Short summary
This study quantifies Brazil’s emissions of a potent atmospheric greenhouse gas, methane. This is in the field of atmospheric modelling and uses remotely sensed data and surface measurements of methane concentrations as well as an atmospheric transport model to interpret the data. Because of Brazil’s large emissions from wetlands, agriculture and biomass burning, these emissions affect global methane concentrations and thus are of global significance.
Guillaume Monteil, Grégoire Broquet, Marko Scholze, Matthew Lang, Ute Karstens, Christoph Gerbig, Frank-Thomas Koch, Naomi E. Smith, Rona L. Thompson, Ingrid T. Luijkx, Emily White, Antoon Meesters, Philippe Ciais, Anita L. Ganesan, Alistair Manning, Michael Mischurow, Wouter Peters, Philippe Peylin, Jerôme Tarniewicz, Matt Rigby, Christian Rödenbeck, Alex Vermeulen, and Evie M. Walton
Atmos. Chem. Phys., 20, 12063–12091, https://doi.org/10.5194/acp-20-12063-2020, https://doi.org/10.5194/acp-20-12063-2020, 2020
Short summary
Short summary
The paper presents the first results from the EUROCOM project, a regional atmospheric inversion intercomparison exercise involving six European research groups. It aims to produce an estimate of the net carbon flux between the European terrestrial ecosystems and the atmosphere for the period 2006–2015, based on constraints provided by observed CO2 concentrations and using inverse modelling techniques. The use of six different models enables us to investigate the robustness of the results.
Swaleha Inamdar, Liselotte Tinel, Rosie Chance, Lucy J. Carpenter, Prabhakaran Sabu, Racheal Chacko, Sarat C. Tripathy, Anvita U. Kerkar, Alok K. Sinha, Parli Venkateswaran Bhaskar, Amit Sarkar, Rajdeep Roy, Tomás Sherwen, Carlos Cuevas, Alfonso Saiz-Lopez, Kirpa Ram, and Anoop S. Mahajan
Atmos. Chem. Phys., 20, 12093–12114, https://doi.org/10.5194/acp-20-12093-2020, https://doi.org/10.5194/acp-20-12093-2020, 2020
Short summary
Short summary
Iodine chemistry is generating a lot of interest because of its impacts on the oxidising capacity of the marine boundary and depletion of ozone. However, one of the challenges has been predicting the right levels of iodine in the models, which depend on parameterisations for emissions from the sea surface. This paper discusses the different parameterisations available and compares them with observations, showing that our current knowledge is still insufficient, especially on a regional scale.
Cited articles
Bergamaschi, P., Brenninkmeijer, C. A. M., Hahn, M., Röckmann, T.,
Scharffe, D. H., Crutzen, P. J., Elansky, N. F., Belikov, I. B., Trivett, N.
B. A., and Worthy, D. E. J.: Isotope analysis based source identification
for atmospheric CH4 and CO sampled across Russia using the Trans-Siberian
railroad, J. Geophys. Res.-Atmos., 103, 8227–8235,
https://doi.org/10.1029/97JD03738, 1998. a
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. a, b, c
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. a, b
Bousquet, P., Ringeval, B., Pison, I., Dlugokencky, E. J., Brunke, E.-G., Carouge, C., Chevallier, F., Fortems-Cheiney, A., Frankenberg, C., Hauglustaine, D. A., Krummel, P. B., Langenfelds, R. L., Ramonet, M., Schmidt, M., Steele, L. P., Szopa, S., Yver, C., Viovy, N., and Ciais, P.: Source attribution of the changes in atmospheric methane for 2006–2008, Atmos. Chem. Phys., 11, 3689–3700, https://doi.org/10.5194/acp-11-3689-2011, 2011. a, b, c
Burkholder, J. B., Sander, S. P., Abbatt, J., Barker, J. R., Huie, R. E., Kolb,
C. E., Kurylo, M. J., Orkin, V. L., Wilmouth, D. M., and Wine, P. H.:
Chemical Kinetics and Photochemical Data for Use in Atmospheric Studies,
Evaluation Number 18, Tech. Rep. 10, Jet Propulsion Laboratory, Pasadena,
https://doi.org/10.1002/kin.550171010, 2015. a
Chang, W., Applegate, P. J., Haran, M., and Keller, K.: Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties, Geosci. Model Dev., 7, 1933–1943, https://doi.org/10.5194/gmd-7-1933-2014, 2014. a
Chen, Y.-H. and Prinn, R. G.: Estimation of atmospheric methane emissions
between 1996 and 2001 using a three-dimensional global chemical transport
model, J. Geophys. Res.-Atmos., 111, D10307,
https://doi.org/10.1029/2005JD006058, 2006. a, b, c
Coplen, T. B.: Guidelines and recommended terms for expression of
stable-isotope-ratio and gas-ratio measurement results, Rapid Commun. Mass Sp., 25, 2538–2560, https://doi.org/10.1002/rcm.5129, 2011. a
Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., van Aardenne,
J. A., Monni, S., Doering, U., Olivier, J. G. J., Pagliari, V., and
Janssens-Maenhout, G.: Gridded emissions of air pollutants for the period
1970–2012 within EDGAR v4.3.2, Earth Syst. Sci. Data, 10, 1987–2013,
https://doi.org/10.5194/essd-10-1987-2018, 2018. a, b, c, d, e, f
Crowley, J., Saueressig, G., Bergamaschi, P., Fischer, H., and Harris, G.:
Carbon kinetic isotope effect in the reaction CH4+Cl: a relative rate study
using FTIR spectroscopy, Chem. Phys. Lett., 303, 268–274,
https://doi.org/10.1016/S0009-2614(99)00243-2, 1999. a
Dlugokencky, E., Lang, P., Crotwell, A., Mund, J., Crotwell, M., and Thoning,
K.: Atmospheric Methane Dry Air Mole Fractions from the NOAA ESRL Carbon
Cycle Cooperative Global Air Sampling Network, 1983-2016, Version:
2017-07-28, available at: ftp://aftp.cmdl.noaa.gov/data/trace_gases/ch4/flask/surface/ (last access: 11 April 2018),
2017. a
Dlugokencky, E. J., Steele, L. P., Lang, P. M., and Masarie, K. A.: The growth
rate and distribution of atmospheric methane, J. Geophys.
Res., 99, 17021–17043, https://doi.org/10.1029/94jd01245, 1994. a
Emmons, L. K., Walters, S., Hess, P. G., Lamarque, J.-F., Pfister, G. G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S. L., and Kloster, S.: Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3, 43–67, https://doi.org/10.5194/gmd-3-43-2010, 2010. a
Etiope, G. and Milkov, A. V.: A new estimate of global methane flux from
onshore and shallow submarine mud volcanoes to the atmosphere, Environmental
Geology, 46, 997–1002, https://doi.org/10.1007/s00254-004-1085-1, 2004. a, b
Etminan, M., Myhre, G., Highwood, E. J., and Shine, K. P.: Radiative forcing
of carbon dioxide, methane, and nitrous oxide: A significant revision of the
methane radiative forcing, Geophys. Res. Lett., 43,
12614–12623, https://doi.org/10.1002/2016GL071930, 2016. a
Farah, M., Birrell, P., Conti, S., and Angelis, D. D.: Bayesian Emulation and
Calibration of a Dynamic Epidemic Model for A/H1N1 Influenza, J. Am. Stat. Assoc., 109, 1398–1411,
https://doi.org/10.1080/01621459.2014.934453, 2014. a
Ganesan, A. L., Stell, A. C., Gedney, N., Comyn-Platt, E., Hayman, G., Rigby,
M., Poulter, B., and Hornibrook, E.: Spatially Resolved Isotopic Source
Signatures of Wetland Methane Emissions, Geophys. Res. Lett., 45,
3737–3745, https://doi.org/10.1002/2018GL077536, 2018. a
Gay, D. M.: Usage Summary for Selected Optimization Routines (PORT
Mathematical Subroutine Library, Optimization chapter), Tech. Rep. 153,
AT&T Bell Laboratories, Murray Hill, NJ 07974, 1990. a
Hein, R., Crutzen, P. J., and Heimann, M.: An inverse modeling approach to
investigate the global atmospheric methane cycle, Global Biogeochem. Cy., 11, 43–76, https://doi.org/10.1029/96GB03043, 1997. a
Houweling, S., Kaminski, T., Dentener, F., Lelieveld, J., and Heimann, M.:
Inverse modeling of methane sources and sinks using the adjoint of a global
transport model, J. Geophys. Res.-Atmos., 104,
26137–26160, https://doi.org/10.1029/1999JD900428, 1999. a, b, c
Kennedy, M., Anderson, C., O'Hagan, A., Lomas, M., Woodward, I., and Gosling,
J. P.: Quantifying uncertainty in the biospheric carbon flux for England and
Wales, J. R. Stat. Soc., 171, 109–135, 2008. a
King, S. L., Quay, P. D., and Lansdown, J. M.: The 13C/ 12C kinetic isotope
effect for soil oxidation of methane at ambient atmospheric concentrations,
J. Geophys. Res., 94, 18273–18277,
https://doi.org/10.1029/JD094iD15p18273, 1989. a
Kirschke, S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G.,
Dlugokencky, E. J., Bergamaschi, P., Bergmann, D., Blake, D. R., Bruhwiler,
L., Cameron-Smith, P., Castaldi, S., Chevallier, F., Feng, L., Fraser, A.,
Heimann, M., Hodson, E. L., Houweling, S., Josse, B., Fraser, P. J., Krummel,
P. B., Lamarque, J.-F., Langenfelds, R. L., Le Quéré, C., Naik,
V., O'Doherty, S., Palmer, P. I., Pison, I., Plummer, D., Poulter, B., Prinn,
R. G., Rigby, M., Ringeval, B., Santini, M., Schmidt, M., Shindell, D. T.,
Simpson, I. J., Spahni, R., Steele, L. P., Strode, S. A., Sudo, K., Szopa,
S., van der Werf, G. R., Voulgarakis, A., van Weele, M., Weiss, R. F.,
Williams, J. E., and Zeng, G.: Three decades of global methane sources and
sinks, Nat. Geosci., 6, 813–823, https://doi.org/10.1038/ngeo1955, 2013. a
Lambert, G. and Schmidt, S.: Reevaluation of the oceanic flux of methane:
Uncertainties and long term variations, Chemosphere, 26, 579–589,
https://doi.org/10.1016/0045-6535(93)90443-9, 1993. a, b
Lee, L. A., Carslaw, K. S., Pringle, K. J., Mann, G. W., and Spracklen, D. V.: Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters, Atmos. Chem. Phys., 11, 12253–12273, https://doi.org/10.5194/acp-11-12253-2011, 2011. a
Lee, L. A., Carslaw, K. S., Pringle, K. J., and Mann, G. W.: Mapping the uncertainty in global CCN using emulation, Atmos. Chem. Phys., 12, 9739–9751, https://doi.org/10.5194/acp-12-9739-2012, 2012. a
McKay, M. D., Beckman, R. J., and Conover, W. J.: A Comparison of Three
Methods for Selecting Values of Input Variables in the Analysis of Output
From a Computer Code, Technometrics, 21, 239–245,
https://doi.org/10.1080/00401706.2000.10485979, 1979. a
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. a, b, c, d
Miller, J. B., Mack, K. A., Dissly, R., White, J. W., Dlugokencky, E. J., and
Tans, P. P.: Development of analytical methods and measurements of 13C/12C
in atmospheric CH4 from the NOAA Climate Monitoring and Diagnostics
Laboratory Global Air Sampling Network, J. Geophys. Res.-Atmos., 107, 4178, https://doi.org/10.1029/2001JD000630, 2002. a
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. a
Morris, M. D. and Mitchell, T. J.: Exploratory designs for computational
experiments, J. Stat. Plan. Infer., 43, 381–402,
https://doi.org/10.1016/0378-3758(94)00035-T, 1995. a
Murguia-Flores, F., Arndt, S., Ganesan, A. L., Murray-Tortarolo, G., and Hornibrook, E. R. C.: Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil, Geosci. Model Dev., 11, 2009–2032, https://doi.org/10.5194/gmd-11-2009-2018, 2018. a, b
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J.,
Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T.,
Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and
Natural Radiative Forcing, in: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T., Qin, D.,
Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y.,
Bex, V., and Midgley, P., Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 659–740, https://doi.org/10.1017/CBO9781107415324.018, 2013. a
Naus, S., Montzka, S. A., Pandey, S., Basu, S., Dlugokencky, E. J., and Krol, M.: Constraints and biases in a tropospheric two-box model of OH, Atmos. Chem. Phys., 19, 407–424, https://doi.org/10.5194/acp-19-407-2019, 2019. a, b
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., 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., 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. a, b, c, d, e
O'Hagan, A.: Bayesian analysis of computer code outputs: A tutorial,
Reliability Engineering and System Safety, 91, 1290–1300,
https://doi.org/10.1016/j.ress.2005.11.025, 2006. a, b
Olson, R., Ruckert, K. L., Chang, W., Keller, K., Haran, M., and An, S. I.:
Stilt: Easy emulation of time series AR(1) computer model output in
multidimensional parameter space, The R Journal, 10, 209–225,
https://doi.org/10.32614/RJ-2018-049, 2018. a
Patra, P. K., Houweling, S., Krol, M., Bousquet, P., Belikov, D., Bergmann, D., Bian, H., Cameron-Smith, P., Chipperfield, M. P., Corbin, K., Fortems-Cheiney, A., Fraser, A., Gloor, E., Hess, P., Ito, A., Kawa, S. R., Law, R. M., Loh, Z., Maksyutov, S., Meng, L., Palmer, P. I., Prinn, R. G., Rigby, M., Saito, R., and Wilson, C.: TransCom model simulations of CH4 and related species: linking transport, surface flux and chemical loss with CH4 variability in the troposphere and lower stratosphere, Atmos. Chem. Phys., 11, 12813–12837, https://doi.org/10.5194/acp-11-12813-2011, 2011. a, b, c
Patra, P. K., Krol, M. C., Montzka, S. A., Arnold, T., Atlas, E. L., Lintner,
B. R., Stephens, B. B., Xiang, B., Elkins, J. W., Fraser, P. J., Ghosh, A.,
Hintsa, E. J., Hurst, D. F., Ishijima, K., Krummel, P. B., Miller, B. R.,
Miyazaki, K., Moore, F. L., Mühle, J., O'Doherty, S., Prinn, R. G.,
Steele, L. P., Takigawa, M., Wang, H. J., Weiss, R. F., Wofsy, S. C., and
Young, D.: Observational evidence for interhemispheric hydroxyl-radical
parity, Nature, 513, 219–223, https://doi.org/10.1038/nature13721, 2014. a
Quay, P., Stutsman, J., Wilbur, D., Snover, A., Dlugokencky, E., and Brown, T.:
The isotopic composition of atmospheric methane, Global Biogeochem. Cy., 13, 445–461, https://doi.org/10.1029/1998GB900006, 1999. a
Rasmussen, C. and Williams, K.: Gaussian Processes for Machine Learning, The
MIT Press, Cambridge, Massachusetts, 248 pp., 2006. a
Reeburgh, W. S., Hirsch, A. I., Sansone, F. J., Popp, B. N., and Rust, T. M.:
Carbon kinetic isotope effect accompanying microbial oxidation of methane in
boreal forest soils, Geochim. Cosmochim. Ac., 61, 4761–4767,
https://doi.org/10.1016/S0016-7037(97)00277-9, 1997. a
Regayre, L. A., Johnson, J. S., Yoshioka, M., Pringle, K. J., Sexton, D. M. H., Booth, B. B. B., Lee, L. A., Bellouin, N., and Carslaw, K. S.: Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF, Atmos. Chem. Phys., 18, 9975–10006, https://doi.org/10.5194/acp-18-9975-2018, 2018. a
Rice, A. L., Butenhoff, C. L., Teama, D. G., Röger, F. H., Khalil, M.
A. K., and Rasmussen, R. A.: Atmospheric methane isotopic record favors
fossil sources flat in 1980s and 1990s with recent increase, P. Natl. Acad. Sci. USA, 113,
10791–10796, https://doi.org/10.1073/pnas.1522923113, 2016. a, b
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu,
E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S.,
Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster,
R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder,
C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and
Woollen, J.: MERRA : NASA's Modern-Era Retrospective Analysis for Research
and Applications, J. Climate, 24, 3624–3648,
https://doi.org/10.1175/JCLI-D-11-00015.1, 2011. a
Rigby, M., Prinn, R. G., Fraser, P. J., Simmonds, P. G., Langenfelds, R. L.,
Huang, J., Cunnold, D. M., Steele, L. P., Krummel, P. B., Weiss, R. F.,
O'Doherty, S., Salameh, P. K., Wang, H. J., Harth, C. M., Mühle, J.,
and Porter, L. W.: Renewed growth of atmospheric methane, Geophys. Res. Lett., 35, L22805, https://doi.org/10.1029/2008GL036037, 2008. a, b
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. a, b, c, d, e, f, g, h, i
Saltelli, A. and Annoni, P.: How to avoid a perfunctory sensitivity analysis,
Environ. Modell. Softw., 25, 1508–1517,
https://doi.org/10.1016/j.envsoft.2010.04.012, 2010. a, b
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D.,
Saisana, M., and Tarantola, S.: Global Sensitivity Analysis: The Primer,
Wiley, Chichester, United Kingdom, https://doi.org/10.1111/j.1751-5823.2008.00062_17.x,
2000. a, b, c
Saueressig, G., Bergamaschi, P., Crowley, J. N., Fischer, H., and Harris,
G. W.: Carbon kinetic isotope effect in the reaction of CH4 with Cl atoms,
Geophys. Res. Lett., 22, 1225–1228, https://doi.org/10.1029/95GL00881, 1995. a
Saueressig, G., Crowley, J. N., Bergamaschi, P., Brühl, C.,
Brenninkmeijer, C. A. M., and Fischer, H.: Carbon 13 and D kinetic isotope
effects in the reactions of CH 4 with O( 1 D ) and OH: New laboratory
measurements and their implications for the isotopic composition of
stratospheric methane, J. Geophys. Res.-Atmos., 106,
23127–23138, https://doi.org/10.1029/2000JD000120, 2001. a, b
Saunois, M., Bousquet, P., Poulter, B., Peregon, A., Ciais, P., Canadell, J. G., Dlugokencky, E. J., Etiope, G., Bastviken, D., Houweling, S., Janssens-Maenhout, G., Tubiello, F. N., Castaldi, S., Jackson, R. B., Alexe, M., Arora, V. K., Beerling, D. J., Bergamaschi, P., Blake, D. R., Brailsford, G., Brovkin, V., Bruhwiler, L., Crevoisier, C., Crill, P., Covey, K., Curry, C., Frankenberg, C., Gedney, N., Höglund-Isaksson, L., Ishizawa, M., Ito, A., Joos, F., Kim, H.-S., Kleinen, T., Krummel, P., Lamarque, J.-F., Langenfelds, R., Locatelli, R., Machida, T., Maksyutov, S., McDonald, K. C., Marshall, J., Melton, J. R., Morino, I., Naik, V., O'Doherty, S., Parmentier, F.-J. W., Patra, P. K., Peng, C., Peng, S., Peters, G. P., Pison, I., Prigent, C., Prinn, R., Ramonet, M., Riley, W. J., Saito, M., Santini, M., Schroeder, R., Simpson, I. J., Spahni, R., Steele, P., Takizawa, A., Thornton, B. F., Tian, H., Tohjima, Y., Viovy, N., Voulgarakis, A., van Weele, M., van der Werf, G. R., Weiss, R., Wiedinmyer, C., Wilton, D. J., Wiltshire, A., Worthy, D., Wunch, D., Xu, X., Yoshida, Y., Zhang, B., Zhang, Z., and Zhu, Q.: The global methane budget 2000–2012, Earth Syst. Sci. Data, 8, 697–751, https://doi.org/10.5194/essd-8-697-2016, 2016. a, b, c, d, e
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. a, b, c, d, e
Schwietzke, S., Sherwood, O. A., Bruhwiler, L. M. P., Miller, J. B., Etiope,
G., Dlugokencky, E. J., White, J. W. C., Pieter, P. T., Michel, S. E.,
Arling, V. A., Vaughn, B. H., and James, W.: Upward revision of global
fossil fuel methane emissions based on isotope database, Nature, 538,
88–91, https://doi.org/10.1038/nature19797, 2016. a, b
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. a, b
Simpson, I. J., Rowland, F. S., Meinardi, S., and Blake, D. R.: Influence of
biomass burning during recent fluctuations in the slow growth of global
tropospheric methane, Geophys. Res. Lett., 33, L22808,
https://doi.org/10.1029/2006GL027330, 2006. a
Snover, A. K. and Quay, P. D.: Hydrogen and carbon kinetic isotope effects
during soil uptake of atmospheric methane, Global Biogeochem. Cy., 14,
25–39, https://doi.org/10.1029/1999GB900089, 2000. a
Spivakovsky, C. M., Logan, J. A., Montzka, S. A., Balkanski, Y. J.,
Foreman-Fowler, M., Jones, D. B. A., Horowitz, L. W., Fusco, A. C.,
Brenninkmeijer, C. A. M., Prather, M. J., Wofsy, S. C., and McElroy, M. B.:
Three-dimensional climatological distribution of tropospheric OH: Update and
evaluation, J. Geophys. Res.-Atmos., 105, 8931–8980,
https://doi.org/10.1029/1999JD901006, 2000. a, b
Stell, A. C.: Global methane freshwater emission map for atmospheric
modelling, available at: https://osf.io/q9f8p/ (last access: 25 August 2020), https://doi.org/10.17605/OSF.IO/Q9F8P, 2020a. a, b
Stell, A. C.: Atmospheric methane source and sink sensitivity analysis using
Gaussian process emulation, available at: https://osf.io/z435m/ (last access: 8 January 2021), https://doi.org/10.17605/OSF.IO/Z435M,
2020b. a
Strode, S. A., Wang, J. S., Manyin, M., Duncan, B., Hossaini, R., Keller, C. A., Michel, S. E., and White, J. W. C.: Strong sensitivity of the isotopic composition of methane to the plausible range of tropospheric chlorine, Atmos. Chem. Phys., 20, 8405–8419, https://doi.org/10.5194/acp-20-8405-2020, 2020. a
Tans, P. P.: A note on isotopic ratios and the global atmospheric methane
budget, Global Biogeochem. Cy., 11, 77–81, https://doi.org/10.1029/96GB03940,
1997. a
Thanwerdas, J., Saunois, M., Berchet, A., Pison, I., Hauglustaine, D., Ramonet, M., Crevoisier, C., Baier, B., Sweeney, C., and Bousquet, P.: Impact of atomic chlorine on the modelling of total methane and its 13C : 12C isotopic ratio at global scale, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2019-925, 2019. a
Turner, A. J., Jacob, D. J., Benmergui, J., Wofsy, S. C., Maasakkers, J. D.,
Butz, A., Hasekamp, O., and Biraud, S. C.: A large increase in U.S. methane
emissions over the past decade inferred from satellite data and surface
observations, Geophys. Res. Lett., 43, 2218–2224,
https://doi.org/10.1002/2016GL067987, 2016. a
Tyler, S. C., Crill, P. M., and Brailsford, G. W.: 13C/12C Fractionation of
methane during oxidation in a temperate forested soil, Geochim. Cosmochim. Ac., 58, 1625–1633, https://doi.org/10.1016/0016-7037(94)90564-9, 1994. a
Tyler, S. C., Ajie, H. O., Rice, A. L., and Cicerone, R. J.: Experimentally
determined kinetic isotope effects in the reaction of CH4 with Cl:
Implications for atmospheric CH4, Geophys. Res. Lett., 27,
1715–1718, https://doi.org/10.1029/1999GL011168, 2000. a
van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S., Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen, T. T.: Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10, 11707–11735, https://doi.org/10.5194/acp-10-11707-2010, 2010. a, b
Vernon, I., Goldstein, M., and Bower, R. G.: Galaxy Formation: a Bayesian
Uncertainty Analysis, Bayesian Analysis, 5, 619–669,
https://doi.org/10.1214/10-BA524, 2010. a, b
Wang, X., Jacob, D. J., Eastham, S. D., Sulprizio, M. P., Zhu, L., Chen, Q., Alexander, B., Sherwen, T., Evans, M. J., Lee, B. H., Haskins, J. D., Lopez-Hilfiker, F. D., Thornton, J. A., Huey, G. L., and Liao, H.: The role of chlorine in global tropospheric chemistry, Atmos. Chem. Phys., 19, 3981–4003, https://doi.org/10.5194/acp-19-3981-2019, 2019. a
White, J., Vaughn, B., and Michel, S.: Stable Isotopic Composition of
Atmospheric Methane (13C) from the NOAA ESRL Carbon Cycle Cooperative Global
Air Sampling Network, 1998-2016, Version: 2018-01-31, available at:
ftp://aftp.cmdl.noaa.gov/data/trace_gases/ch4c13/flask/ (last access: 11 April 2018),
2018. a
Whiticar, M. and Schaefer, H.: Constraining past global tropospheric methane
budgets with carbon and hydrogen isotope ratios in ice, Philos. T. Roy. Soc. A, 365, 1793–1828, https://doi.org/10.1098/rsta.2007.2048, 2007. a
Wild, O., Voulgarakis, A., O'Connor, F., Lamarque, J.-F., Ryan, E. M., and Lee, L.: Global sensitivity analysis of chemistry–climate model budgets of tropospheric ozone and OH: exploring model diversity, Atmos. Chem. Phys., 20, 4047–4058, https://doi.org/10.5194/acp-20-4047-2020, 2020. a
Worden, J. R., Bloom, A. A., Pandey, S., Jiang, Z., Worden, H. M., Walker,
T. W., Houweling, S., and Röckmann, T.: Reduced biomass burning
emissions reconcile conflicting estimates of the post-2006 atmospheric
methane budget, Nat. Commun., 8, 2227,
https://doi.org/10.1038/s41467-017-02246-0, 2017. a, b, c, d
Yan, X., Akiyama, H., Yagi, K., and Akimoto, H.: Global estimations of the
inventory and mitigation potential of methane emissions from rice cultivation
conducted using the 2006 Intergovernmental Panel on Climate Change
Guidelines, Global Biogeochem. Cy., 23, GB2002,
https://doi.org/10.1029/2008GB003299, 2009. a, b
Short summary
Although it is the second-most important greenhouse gas, our understanding of the atmospheric-methane budget is limited. The uncertainty highlights the need for new tools to investigate sources and sinks. Here, we use a Gaussian process emulator to efficiently approximate the response of atmospheric-methane observations to changes in the most uncertain emission or loss processes. With this new method, we rigorously quantify the sensitivity of atmospheric observations to budget uncertainties.
Although it is the second-most important greenhouse gas, our understanding of the...
Altmetrics
Final-revised paper
Preprint