Comparison of observation- and inventory-based methane emissions for eight large global emitters

  1. Petrescu, Ana Maria Roxana
  2. Peters, Glen ORCID logo
  3. Engelen, Richard ORCID logo
  4. Houweling, Sander ORCID logo
  5. Brunner, Dominik ORCID logo
  6. Tsuruta, Aki ORCID logo
  7. Matthews, Bradley ORCID logo
  8. Patra, Prabir ORCID logo
  9. Belikov, Dmitry ORCID logo
  10. Thompson, Rona ORCID logo
  11. Höglund-Isaksson, Lena ORCID logo
  12. Zhang, Wenxin
  13. Segers, Arjo ORCID logo
  14. Etiope, Giuseppe ORCID logo
  15. Ciotoli, Giancarlo ORCID logo
  16. Peylin, Philippe
  17. Chevallier, Frédéric ORCID logo
  18. Aalto, Tuula ORCID logo
  19. Andrew, Robbie ORCID logo
  20. Bastviken, David ORCID logo
  21. Berchet, Antoine ORCID logo
  22. Broquet, Grégoire
  23. Conchedda, Giulia
  24. Dellaert, Stijn ORCID logo
  25. Denier van der Gon, Hugo ORCID logo
  26. Gütschow, Johannes
  27. Haussaire, Jean-Matthieu
  28. Lauerwald, Ronny ORCID logo
  29. Markkanen, Tiina ORCID logo
  30. van Peet, J.C.A. ORCID logo
  31. Pison, Isabelle ORCID logo
  32. Regnier, Pierre
  33. Sollum, Espen ORCID logo
  34. Scholze, Marko ORCID logo
  35. Tenkanen, Maria ORCID logo
  36. Tubiello, Francesco Nicola ORCID logo
  37. van der Werf, Guido ORCID logo
  38. Worden, John R.

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1000 Titel
  • Comparison of observation- and inventory-based methane emissions for eight large global emitters
1000 Autor/in
  1. Petrescu, Ana Maria Roxana |
  2. Peters, Glen |
  3. Engelen, Richard |
  4. Houweling, Sander |
  5. Brunner, Dominik |
  6. Tsuruta, Aki |
  7. Matthews, Bradley |
  8. Patra, Prabir |
  9. Belikov, Dmitry |
  10. Thompson, Rona |
  11. Höglund-Isaksson, Lena |
  12. Zhang, Wenxin |
  13. Segers, Arjo |
  14. Etiope, Giuseppe |
  15. Ciotoli, Giancarlo |
  16. Peylin, Philippe |
  17. Chevallier, Frédéric |
  18. Aalto, Tuula |
  19. Andrew, Robbie |
  20. Bastviken, David |
  21. Berchet, Antoine |
  22. Broquet, Grégoire |
  23. Conchedda, Giulia |
  24. Dellaert, Stijn |
  25. Denier van der Gon, Hugo |
  26. Gütschow, Johannes |
  27. Haussaire, Jean-Matthieu |
  28. Lauerwald, Ronny |
  29. Markkanen, Tiina |
  30. van Peet, J.C.A. |
  31. Pison, Isabelle |
  32. Regnier, Pierre |
  33. Sollum, Espen |
  34. Scholze, Marko |
  35. Tenkanen, Maria |
  36. Tubiello, Francesco Nicola |
  37. van der Werf, Guido |
  38. Worden, John R. |
1000 Verlag Copernicus Publications
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-09-25
1000 Erschienen in
1000 Quellenangabe
  • 16(9):4325-4350
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.5194/essd-16-4325-2024 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:p>Abstract. Monitoring the spatial distribution and trends in surface greenhouse gas (GHG) fluxes, as well as flux attribution to natural and anthropogenic processes, is essential to track progress under the Paris Agreement and to inform its global stocktake. This study updates earlier syntheses (Petrescu et al., 2020, 2021, 2023), provides a consolidated synthesis of CH4 emissions using bottom-up (BU) and top-down (TD) approaches for the European Union (EU), and is expanded to include seven additional countries with large anthropogenic and/or natural emissions (the USA, Brazil, China, India, Indonesia, Russia, and the Democratic Republic of the Congo (DR Congo)). Our aim is to demonstrate the use of different emission estimates to help improve national GHG emission inventories for a diverse geographical range of stakeholders. We use updated national GHG inventories (NGHGIs) reported by Annex I parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2023 and the latest available biennial update reports (BURs) reported by non-Annex I parties. Comparing NGHGIs with other approaches highlights that different system boundaries are a key source of divergence. A key system boundary difference is whether anthropogenic and natural fluxes are included and, if they are, how fluxes belonging to these two sources are partitioned. Over the studied period, the total CH4 emission estimates in the EU, the USA, and Russia show a steady decreasing trend since 1990, while for the non-Annex I emitters analyzed in this study, Brazil, China, India, Indonesia, and DR Congo, CH4 emissions have generally increased. Quantitatively, in the EU the mean of 2015–2020 anthropogenic UNFCCC NGHGIs (15±1.8 Tg CH4 yr−1) and the mean of the BU CH4 emissions (17.8 (16–19) Tg CH4 yr−1) generally agree on the magnitude, while inversions show higher emission estimates (medians of 21 (19–22) Tg CH4 yr−1 and 24 (22–25) Tg CH4 yr−1 for the three regional and six global inversions, respectively), as they include natural emissions, which for the EU were quantified at 6.6 Tg CH4 yr−1 (Petrescu et al., 2023). Similarly, for the other Annex I parties in this study (the USA and Russia), the gap between the BU anthropogenic and total TD emissions is partly explained by the natural emissions. For the non-Annex I parties, anthropogenic CH4 estimates from UNFCCC BURs show large differences compared to the other global-inventory-based estimates and even more compared to atmospheric ones. This poses an important potential challenge to monitoring the progress of the global CH4 pledge and the global stocktake. Our analysis provides a useful baseline to prepare for the influx of inventories from non-Annex I parties as regular reporting starts under the enhanced transparency framework of the Paris Agreement. By systematically comparing the BU and TD methods, this study provides recommendations for more robust comparisons of available data sources and hopes to steadily engage more parties in using observational methods to complement their UNFCCC inventories, as well as considering their natural emissions. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, future development needs to resolve knowledge gaps in the BU and TD approaches and to better quantify the remaining uncertainty. TD methods may emerge as a powerful tool to help improve NGHGIs of CH4 emissions, but further confidence is needed in the comparability and robustness of the estimates. The referenced datasets related to figures are available at https://doi.org/10.5281/zenodo.12818506 (Petrescu et al., 2024). </jats:p>
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
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    1000 Förderer Swedish National Space Agency |
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    1000 Fördernummer -
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1000 Erstellt am 2024-10-03T11:01:23.812+0200
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