the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global greenhouse gas reconciliation 2022
Philippe Ciais
Liting Hu
Adrien Martinez
Marielle Saunois
Rona L. Thompson
Kushal Tibrewal
Wouter Peters
Brendan Byrne
Giacomo Grassi
Paul I. Palmer
Ingrid T. Luijkx
Junjie Liu
Xuekun Fang
Tengjiao Wang
Hanqin Tian
Katsumasa Tanaka
Ana Bastos
Stephen Sitch
Benjamin Poulter
Clément Albergel
Aki Tsuruta
Shamil Maksyutov
Rajesh Janardanan
Yosuke Niwa
Joël Thanwerdas
Dmitry Belikov
Arjo Segers
Frédéric Chevallier
Download
- Final revised paper (published on 18 Mar 2025)
- Supplement to the final revised paper
- Preprint (discussion started on 05 Jul 2024)
- Supplement to the preprint
Interactive discussion
Status: closed
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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
Peer review completion



