the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global Greenhouse Gas Reconciliation 2022
Abstract. In this study, we provide an update of 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 NGHGIs database was updated with inventory data until February 2023 by compiling data from periodical 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 slow declining or stable trend in emissions. Much denser sampling and higher atmospheric CO2 and CH4 concentrations by different satellites, are 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 objective of their pledges. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.10841716 (Deng et al., 2024).
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Status: final response (author comments only)
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RC1: 'Comment on essd-2024-103', Christian DiMaria, 29 Jul 2024
Please see the attached PDF for my comments and suggestions.
- AC1: 'Reply on RC1', Zhu Deng, 03 Oct 2024
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RC2: 'Comment on essd-2024-103', Anonymous Referee #2, 06 Sep 2024
Review of Global “Greenhouse Gas Reconciliation 2022”
Summary- In this manuscript, authors present an updated dataset based on Deng et al (2022) which presents a dataset that can be used to compare GHG emissions from national inventories to those based on model ensembles. Specifically, their method (amongst other things) uses inversions of modelled estimates from ensembles to reconcile the top-down approach taken by models with the bottom-up inventories. This paper is well written and clearly an important contribution to the literature. I recommend publication after minor revisions. Also, I apologize to the authors and the editors for my delayed review (I was on leave and could not get to this).
Comments-
- National LUC CO2 emissions/uptake- From the manuscript, I gathered that the authors have used the LUC emissions/uptake from the Global Carbon Project (specifically atmospheric inversions of the LUC emissions/uptake data). However, starting recently, the GCP has been updated to provide national inventories of LUC emissions and uptake (See the paper from Gasser et al which introduced this- https://bg.copernicus.org/articles/17/4075/2020/). This nationalized data is only available from 3 models. But the reason its important to discuss this is because the Gasser et al. work was done specifically to compare LUC emissions data to national inventories. Can the authors compare the national CO2 data from inversions to the nationalized data? By the way the data itself is available here- https://www.icos-cp.eu/science-and-impact/global-carbon-budget/2022 (See the third spreadsheet). I believe this will add more robustness to the validation.
- CH4 inversions- For the CH4 inversions, the authors suggest that some inversions optimize within sectors while others provide total gridded emissions. When total gridded emissions are available, prior fluxes are used to allocate emissions to sectors. Could you elaborate which inversions were differentiated by sectors and which were not? Also does assigning sectoral information based on priors involve any uncertainty? Perhaps this was discussed in the previous paper already and this paper just needs to mention that. But regardless, a discussion of this point would be helpful.
- Wood fuel burning vs fire – I appreciate the discussion by the authors when it comes to discussing the limitations when separating out the emissions from fire vs those from regular wood fuel burning. However, there has been some work recently to separate out wood fuel burning emissions which are non-renewable. Specifically, this paper-https://essd.copernicus.org/articles/15/2179/2023/essd-15-2179-2023.html. Can the authors compare their wood fuel emissions to the emissions as shown in the article here? Once again, this would make the results more robust more than anything else.
- Lateral carbon transport by crop and wood products- This lateral carbon transport is a really interesting aspect of your work. However, could you highlight this aspect more in the results section? Could you perhaps discuss the extent to which these emissions/uptakes affect total emissions/uptake. Also are these just based on primary product trade (e.g. roundwood) or do they include primary and secondary trade (e.g. roundwood would be primary but wood pulp, sawn wood would be secondary)
- Heatmap of countries selected for inversion data- Based on the discussion on lines 99-101 on page 3, it would be interesting to see the countries selected as a heatmap just to understand what portion of emissions are covered globally by emissions type.
Other minor comments-
- Lines 92 on Page 3, it seems that there is a typo “ Atmosphericnversions” should be separate words.
- Line 170 on Page 7- What do advection and convection schemes mean? Can an explanation be added in a footnote or maybe even be explained in text?
- Line 176 on Page 7- Is there a reference missing for “GAINS”?
- Lines 290-293 on Page 11. “ However, the differences in the calculated results among the four methods were smaller compared to the variations observed in the inversions (see Deng et al. (2022) Fig 9).” I think you need a sentence after this just summarizing what the differences are before you explain the method used.
- Lines 443-444 on Page 17- Formatting is off for this sentence- “post fire biomass changes suggest that fire emissions have exceeded regrowth on average in Western Canada and Alaska until ≈ 2010”
Citation: https://doi.org/10.5194/essd-2024-103-RC2 - AC2: 'Reply on RC2', Zhu Deng, 03 Oct 2024
Data sets
Global greenhouse gas reconciliation 2022 Zhu Deng, Philippe Ciais, Liting Hu, Tengjiao Wang, Adrien Martinez, Marielle Saunois, Rona L. Thompson, and Frédéric Chevallier https://doi.org/10.5281/zenodo.10841716
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