Global greenhouse gas reconciliation 2022

Creators: Deng, Zhu^{1, 2}; Ciais, Philippe³; Hu, Liting⁴; Martinez, Adrien³; Saunois, Marielle³; Thompson, Rona L.⁵; Tibrewal, Kushal³; Peters, Wouter^{6, 7}; Byrne, Brendan⁸; Grassi, Giacomo⁹; Palmer, Paul I.¹⁰; Luijkx, Ingrid T.⁶; Liu, Zhu²; Liu, Junjie^{8, 11}; Fang, Xuekun⁴; Wang, Tengjiao¹²; Tian, Hanqin¹³; Tanaka, Katsumasa^{3, 14}; Bastos, Ana¹⁵; Sitch, Stephen¹⁶; Poulter, Benjamin¹⁷; Albergel, Clément¹⁸; Tsuruta, Aki¹⁹; Maksyutov, Shamil¹⁴; Janardanan, Rajesh¹⁴; Niwa, Yosuke^{14, 20}; Zheng, Bo²; Thanwerdas, Joël²¹; Belikov, Dmitry²²; Segers, Arjo²³; Chevallier, Frédéric³

1. University of Hong Kong
2. Tsinghua University
3. Laboratoire des Sciences du Climat et de l'Environnement
4. Zhejiang University
5. Norwegian Institute for Air Research
6. Wageningen University & Research
7. University of Groningen
8. Jet Propulsion Lab
9. Joint Research Centre
10. University of Edinburgh
11. California Institute of Technology
12. Shandong University
13. Boston College
14. National Institute for Environmental Studies
15. Leipzig University
16. University of Exeter
17. Goddard Space Flight Center
18. European Centre for Space Applications and Telecommunications
19. Finnish Meteorological Institute
20. Japan Meteorological Agency
21. Swiss Federal Laboratories for Materials Science and Technology
22. Chiba University
23. Netherlands Organisation for Applied Scientific Research

Abstract

In this study, we provide an update on the methodology and data used by Deng et al. (2022) to compare the national greenhouse gas inventories (NGHGIs) and atmospheric inversion model ensembles contributed by international research teams coordinated by the Global Carbon Project. The comparison framework uses transparent processing of the net ecosystem exchange fluxes of carbon dioxide (CO2) from inversions to provide estimates of terrestrial carbon stock changes over managed land that can be used to evaluate NGHGIs. For methane (CH4), and nitrous oxide (N2O), we separate anthropogenic emissions from natural sources based directly on the inversion results to make them compatible with NGHGIs. Our global harmonized NGHGI database was updated with inventory data until February 2023 by compiling data from periodical United Nations Framework Convention on Climate Change (UNFCCC) inventories by Annex I countries and sporadic and less detailed emissions reports by non-Annex I countries given by national communications and biennial update reports. For the inversion data, we used an ensemble of 22 global inversions produced for the most recent assessments of the global budgets of CO2, CH4, and N2O coordinated by the Global Carbon Project with ancillary data. The CO2 inversion ensemble in this study goes through 2021, building on our previous report from 1990 to 2019, and includes three new satellite inversions compared to the previous study and an improved managed-land mask. As a result, although significant differences exist between the CO2 inversion estimates, both satellite and in situ inversions over managed lands indicate that Russia and Canada had a larger land carbon sink in recent years than reported in their NGHGIs, while the NGHGIs reported a significant upward trend of carbon sink in Russia but a downward trend in Canada. For CH4 and N2O, the results of the new inversion ensembles are extended to 2020. Rapid increases in anthropogenic CH4 emissions were observed in developing countries, with varying levels of agreement between NGHGIs and inversion results, while developed countries showed a slowly declining or stable trend in emissions. Much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites, coordinated into a global constellation, is expected in the coming years. The methodology proposed here to compare inversion results with NGHGIs can be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objectives of their pledges. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.13887128 (Deng et al., 2024).

Copyright and License

Funding

This research has been supported by the European Space Agency Climate Change Initiative RECCAP2 project (grant no. ESRIN/4000123002/18/I-NB) and the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals (grant no. CBAS2023ORP06).

Acknowledgement

The authors are very grateful to the atmosphere inversion model developers Chris Wilson, Christian Rödenbeck, Kelley Wells, Liesbeth Florentie, Naveen Chandra, Peter Bergamaschi, Prabir Patra, and Yi Yin for the availability of their global CO₂, CH₄, and N₂O inversion data and acknowledge many other data providers (measurements, models, inventories, atmospheric inversions, hybrid products, etc.) that are directly or indirectly used in this synthesis. The PyVAR-CAMS N₂O modeling results were funded through the Copernicus Atmospheric Monitoring Service, implemented by ECMWF on behalf of the European Commission, and were generated using computing resources from LSCE. Rona Thompson would also like to acknowledge the support of Frederic Chevallier in providing the PyVAR-CAMS N₂O inversion results. Philippe Ciais, Frédéric Chevallier, and Marielle Saunois acknowledge support from the European Space Agency Climate Change Initiative RECCAP2 project (ESRIN/4000123002/18/I-NB). Zhu Liu acknowledges support by the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals (CBAS2023ORP06).

Conflict of Interest

At least one of the (co-)authors is a member of the editorial board of Earth System Science Data. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

Contributions

PC, FC, MS, RLT, and ZD designed and coordinated the study. PC, MS, RLT, and FC designed the framework of atmosphere inversion data processing. ZD, PC, LH, MS, RLT, and FC performed the post-processing and analysis and wrote the paper. ZD, LH, and TW compiled the national greenhouse gas inventories. MS, RLT, HT, and FC gathered the global atmosphere inversion datasets of CO₂, CH₄, and N₂O. GG contributed the managed-land mask of Brazil and Canada. FC processed the atmosphere inversion data with masks of managed lands and country boundaries. AT, SM, RJ, YN, BZ, JT, DB, and AS contributed the unpublished CH₄ inversion data. All authors contributed to the full text.

Supplemental Material

The supplement related to this article is available online at https://doi.org/10.5194/essd-17-1121-2025-supplement.

Data Availability

Processed GHG (CO₂, CH₄, and N₂O) data from inverse models and UNFCCC NGHGIs are available at https://doi.org/10.5281/zenodo.13887128 (Deng et al., 2024).

This dataset contains five data files, described as follows.

The file “Inversions_CO₂_v2022.csv” includes the NEE CO₂ flux from managed lands for the nine CO₂ inverse models. It includes eight fields: years (from 1960 to 2021), country, value (unit: Tg C yr⁻¹), sector (“land”: without the adjustment of lateral C flux; “land_cor”: with lateral C flux adjustment), source, gas, observation (“in situ”: in situ-based; “satellite”: satellite-based), and version (“CO₂_ML_v2022” only).

The file “Inversions_CH₄_v2022.csv” includes CH₄ flux from anthropogenic sources for the six CH₄ inverse models. It includes eight fields: years (from 2000 to 2020), country, value (unit: Tg CH₄ yr⁻¹), sector (“agrw”: agriculture and waste; “fos”: fossil fuel; “ant”: anthropogenic, i.e., agrw + fos), source, gas, observation (“in situ”: in situ-based; “satellite”: satellite-based), and version (“CH₄_2022_V1”: uses EDGAR as priors; “CH₄_2022_V2”: uses GAINS as priors).

The file “Inversions_N2O_v2022.csv” includes the anthropogenic N₂O flux from managed lands for the four N₂O inverse models. It includes eight fields: years (from 1995 to 2020), country, value (unit: Tg N₂O yr⁻¹), sector (“ant” only, for anthropogenic), source, gas, observation (“in situ” only, for in situ-based), and version (“N2O_ML_v2022” only).

The file “lateral_CO₂_v2022.csv” includes the national lateral C flux from rivers and trade.

The file “NGHGIs_v2022.csv” includes the national inventory data collected from UNFCCC NGHGIs (unit: Gg yr⁻¹).

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