Articles | Volume 21, issue 3
https://doi.org/10.5194/acp-21-1717-2021
https://doi.org/10.5194/acp-21-1717-2021
Research article
 | 
09 Feb 2021
Research article |  | 09 Feb 2021

Atmospheric-methane source and sink sensitivity analysis using Gaussian process emulation

Angharad C. Stell, Luke M. Western, Tomás Sherwen, and Matthew Rigby

Related authors

Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion
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

Related subject area

Subject: Gases | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
A CO2–Δ14CO2 inversion setup for estimating European fossil CO2 emissions
Carlos Gómez-Ortiz, Guillaume Monteil, Sourish Basu, and Marko Scholze
Atmos. Chem. Phys., 25, 397–424, https://doi.org/10.5194/acp-25-397-2025,https://doi.org/10.5194/acp-25-397-2025, 2025
Short summary
Maximum ozone concentrations in the southwestern US and Texas: implications of the growing predominance of the background contribution
David D. Parrish, Ian C. Faloona, and Richard G. Derwent
Atmos. Chem. Phys., 25, 263–289, https://doi.org/10.5194/acp-25-263-2025,https://doi.org/10.5194/acp-25-263-2025, 2025
Short summary
Derivation of atmospheric reaction mechanisms for volatile organic compounds by the SAPRC mechanism generation system (MechGen)
William P. L. Carter, Jia Jiang, John J. Orlando, and Kelley C. Barsanti
Atmos. Chem. Phys., 25, 199–242, https://doi.org/10.5194/acp-25-199-2025,https://doi.org/10.5194/acp-25-199-2025, 2025
Short summary
Seasonal, regional, and vertical characteristics of high-carbon-monoxide plumes along with their associated ozone anomalies, as seen by IAGOS between 2002 and 2019
Thibaut Lebourgeois, Bastien Sauvage, Pawel Wolff, Béatrice Josse, Virginie Marécal, Yasmine Bennouna, Romain Blot, Damien Boulanger, Hannah Clark, Jean-Marc Cousin, Philippe Nedelec, and Valérie Thouret
Atmos. Chem. Phys., 24, 13975–14004, https://doi.org/10.5194/acp-24-13975-2024,https://doi.org/10.5194/acp-24-13975-2024, 2024
Short summary
The potential of drone observations to improve air quality predictions by 4D-Var
Hassnae Erraji, Philipp Franke, Astrid Lampert, Tobias Schuldt, Ralf Tillmann, Andreas Wahner, and Anne Caroline Lange
Atmos. Chem. Phys., 24, 13913–13934, https://doi.org/10.5194/acp-24-13913-2024,https://doi.org/10.5194/acp-24-13913-2024, 2024
Short summary

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
Download
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.
Altmetrics
Final-revised paper
Preprint