the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The consolidated European synthesis of CO2 emissions and removals for EU27 and UK: 1990–2020
Matthew Joseph McGrath
Ana Maria Roxana Petrescu
Philippe Peylin
Robbie M. Andrew
Bradley Matthews
Frank Dentener
Juraj Balkovič
Vladislav Bastrikov
Meike Becker
Gregoire Broquet
Philippe Ciais
Audrey Fortems
Raphael Ganzenmüller
Giacomo Grassi
Ian Harris
Matthew Jones
Juergen Knauer
Matthias Kuhnert
Guillaume Monteil
Saqr Munassar
Paul I. Palmer
Glen P. Peters
Chunjing Qiu
Mart-Jan Schelhaas
Oksana Tarasova
Matteo Vizzarri
Karina Winkler
Gianpaolo Balsamo
Antoine Berchet
Peter Briggs
Patrick Brockmann
Frédéric Chevallier
Giulia Conchedda
Monica Crippa
Stijn Dellaert
Hugo A. C. Denier van der Gon
Sara Filipek
Pierre Friedlingstein
Richard Fuchs
Michael Gauss
Christoph Gerbig
Diego Guizzardi
Dirk Günther
Richard A. Houghton
Greet Janssens-Maenhout
Ronny Lauerwald
Bas Lerink
Ingrid T. Luijkx
Géraud Moulas
Marilena Muntean
Gert-Jan Nabuurs
Aurélie Paquirissamy
Lucia Perugini
Wouter Peters
Roberto Pilli
Julia Pongratz
Pierre Regnier
Marko Scholze
Yusuf Serengil
Pete Smith
Efisio Solazzo
Rona L. Thompson
Francesco N. Tubiello
Timo Vesala
Sophia Walther
Abstract. Quantification of land surface-atmosphere fluxes of carbon dioxide (CO2) fluxes and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021b). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven sectoral model results, and inverse modeling estimates, extending the previous period 1990–2018 to the year 2020 to the extent possible. BU and TD products are compared with European National Greenhouse Gas Inventories (NGHGIs) reported by Parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU Member States following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from Land Use, Land Use Change and Forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI.
For CO2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for 3392 ± 49 Tg CO2 yr-1 (926 ± 13 Tg C yr-1), while eight other BU sources report a mean value of 3340 [3238,3401] [25th,75th percentile] Tg CO2 yr-1 (948 [937,961] Tg C yr-1). The sole top-down inversion of fossil emissions currently available accounts for 3800 Tg CO2 yr-1 (1038 Tg C yr-1), a value close to that of the NGHGI, but for which uncertainty estimates are not yet available. For the net CO2 land fluxes, during the most recent five-year period including the NGHGI estimates, the NGHGI accounted for -91 ± 32 Tg C yr-1 while six other BU approaches reported a mean sink of -62 [-117,-49] Tg C yr-1 and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported -69 [-152,-5] Tg C yr-1. The five-year mean of three TD regional ensembles combined with one non-ensemble inversion of -73 Tg C yr-1 has a slightly smaller spread (0th–100th percentile of [-135,45] Tg C yr-1), and was calculated after removing land-atmosphere CO2 fluxes caused by lateral transport of carbon (crops, wood trade and inland waters) resulting in increased agreement with the the NGHGI and bottom-up approaches. Results at the sub-sector level (Forestland, Cropland, Grassland) show generally good agreement between the NGHGI and sub-sector-specific models, but results for a DGVM are mixed. Overall, for both CO2 fossil and net CO2 land fluxes, we find current independent approaches are consistent with the NGHGI at the scale of the EU27+UK. We conclude that CO2 emissions from fossil sources have decreased over the past 30 years in the EU27+UK, while large uncertainties on net uptake of CO2 by the land surface prevent trend identification. In addition, a gap on the order of 1000 Tg C yr-1 between CO2 fossil emissions and net CO2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO2 fossil emissions remain uncharacterized and are likely substantial. The data used to plot the figures are available at https://doi.org/10.5281/zenodo.7365863.
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Matthew Joseph McGrath et al.
Status: closed
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CC1: 'Comment on essd-2022-412', Alex Vermeulen, 14 Feb 2023
This paper illustrates a general problem in attribution of data used in complex scientific analyses like discussed here. Especially when data is used from other studies (GCP, Eurocom etc) that are already published in the peer reviewed literature that again base on underlying data from observations. The risk is that in the end this observational data provision that forms the basis of the analyses completely gets out of sight. This is a threat for science, as more and more funding for these so essential observations hinges on data providers being able to show the stakeholders where and how much their data has been used, preferably through data citation tracking. As long as scientists do not apply proper data citation and data providers do not always provide proper data identifiers, publishers will not feel the need to demand and track data citation, so we need to solve this chicken and egg problem together as scientists and data providers by an effort to give some attention to the data citation issue.
I would argue that for this specific meta analysis it is necessary to provide at least acknowledgment and if possible also data citations for the observational data that were essential for the inversions and calibration of the vegetation models, especially when the references to the original analyses are in some cases already lacking the correct attribution. In this paper I only could track that the SOCAT product is mentioned as (aggregated) observation based data product, but the DOI citation did not make it to the references. As far as I know many atmospheric inversions make use of the NOAA Obspack data product, complemented with other aggregations like from ICOS atmosphere, and many vegetation models or machine learning data fusion products base on FLUXNET or other (ICOS) ecosystem flux datasets. So I would recommend some effort to extend the paper with better attribution to essential (observational) datasets (in)directly used in the analyses.
Citation: https://doi.org/10.5194/essd-2022-412-CC1 -
AC2: 'Response to Alex Vermeulen', Matthew McGrath, 16 Jul 2023
We fully agree on the importance of recognizing data providers when publishing complex scientific analysis. We have already made a significant improvement in this direction compared to previous studies through extensive engagement with data providers whose products appear in all figures and analysis used in this work, including those from publicly-available model intercomparison projects like TRENDY and the GCP. This has resulted in a manuscript with 65 co-authors, 49 pages of supplementary information (including more detailed model and data descriptions), and around 180 references.
In regards to the SOCAT product, we provided a reference of Bakker et al., 2016 (l2188). No DOI exists for the version of SOCAT used in this work. However, we have provided the DOI to the most recent version at the same line, and we urge data providers to include DOIs with all versions of their work released to the public to enable accurate and transparent citations. The following has been added to the References (complete author list included in the paper, but omitted here):
“Bakker, D. C. E., …: Surface Ocean CO2 Atlas Database Version 2022 (SOCATv2022) (NCEI Accession 0253659). Subset v2021. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/1h9f-nb73. Accessed 01 July 2021.”
As noted by the commenter, some datasets are essential to atmospheric inversions, in particular the station-level observations of atmospheric carbon dioxide concentrations as a function of time. For the EUROCOM simulations, Monteil et al. (2020) and Thompson et al. (2020), already cited in our manuscript, provide good traceability for the particular observational dataset used, with several paragraphs of text and acknowledgements. We have added the citation of the atmospheric mole fraction with the following sentence at L2096: “The observational dataset used for the EUROCOM drought ensemble is accessible on the ICOS Carbon Portal (Drought 2018 Team and ICOS Atmosphere Thematic Centre, 2020).” We have also added the reference in the References:
“Drought 2018 Team, ICOS Atmosphere Thematic Centre: Drought-2018 atmospheric CO2 Mole Fraction product for 48 stations (96 sample heights), Integrated Carbon Observation System [data set], https://doi.org/10.18160/ERE9-9D85, 2020.”
We did something similar for the inversions used in the Global Carbon Budget, whose information is reported by Friedlingstein et al. (2022) in Table A4, adding explicit data references in our section GCP 2021.
L2080: “For details see Friedlingstein et al. (2022), in particular Table A4. Atmospheric observations for most model systems are taken from Cox et al. (2021) and Di Sarra et al. (2021). Note that one of the ensemble”
with the following references (full author list included in the manuscript):
“Cox, A… Multi-laboratory compilation of atmospheric carbon dioxide data for the period 1957–2019; obspack_CO2_1_GLOBALVIEWplus_v6.1_2021-03-01, NOAA Global Monitoring Laboratory [data set], https://doi.org/10.25925/20201204, 2021.
“Di Sarra, A. G.,…Multi-laboratory compilation of atmospheric carbon dioxide data for the years 2020–2021; obspack_CO2_1_NRT_v6.1.1_2021-05-17, NOAA Global Monitoring Laboratory [data set], https://doi.org/10.25925/20210517, 2021.”
In addition, we have made explicit data usage for inversions performed for the VERIFY project and not reported elsewhere by incorporating text and references as possible into the Appendix on regional inversions:
L1923: “For the regional inversions, atmospheric observations of CO2 were taken from multiple sources. For CarboScopeRegional, atmospheric observations were taken from the ICOS 2021.1 ATC (ICOS RI, 2021) and the GlobalViewPlus 6.1 product (Schuldt et al., 2021a). For the CIF-CHIMERE inversions, atmospheric observations of CO2 for the period 2005-2020 were taken from the ICOS 2021.1 ATC (ICOS RI, 2021) and SNO_SIFA L2 (SNO-IFA, 2023) releases, along with data distributed through the GlobalViewPlus 6.1 product (Schuldt et al., 2021a). For LUMIA inversions, atmospheric observations of CO2 for the period 2006-2018 were taken from the dataset prepared for the 2018 drought task force initiative (Thompson et al., 2020). For the more recent years, data were used from the ICOS 2021.1 ATC release (ICOS RI, 2021), along with data distributed through the GlobalViewPlus 7.0 product (Schuldt et al., 2021b), and, for four sites, data distributed through the World Data Center for Greenhouse Gases. “
The situation for bottom-up models is slightly different. We do not report results from machine-learning upscaled Fluxnet products (such as Fluxcom) in the current work. While evaluation against eddy covariance sites (such as FLUXNET and ICOS) is likely the norm for dynamic global vegetation models used in the TRENDY ensemble, it’s unclear how many models rely on such results for parameterization of various processes, nor how important these processes are for the overall model behavior compared to those which use other data streams. In other words, even if evaluations are run, it’s unclear to what extent models are changed in response to results. As such, we prefer to trust original citations of the models to resolve these questions, and we defer to editorial policies of the journal for additional citation standards.
Citation: https://doi.org/10.5194/essd-2022-412-AC2
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AC2: 'Response to Alex Vermeulen', Matthew McGrath, 16 Jul 2023
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RC1: 'Comment on essd-2022-412', Anonymous Referee #1, 13 Mar 2023
Overall, this paper is well written and would be helpful, particularly for those communities working closely with the datasets used in the paper. The dataset itself may not be something unique, instead, this paper could support the future GHG emissions inventory synthesis work in Europe and potentially other Annex I parties in the world. However, possibly by the nature of this type of dataset, the text seems to be a bit lengthy and tedious, especially the result section. Although the authors described the details of the data sources and models used in this work, the dataset itself may not be easy to use/understand for users new to this field. A brief description of targeted users, potential data usage/application, and a list of key take-home messages (in the intro or conclusion) may help the reader to grasp the paper's contents (and narrow down sections relevant to the readers). Plese see the attached documents for more detail comments.
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AC3: 'Response to Anonymous Referee #1', Matthew McGrath, 16 Jul 2023
Thank you for the positive comments.
For the general comments, we take note of the length of the text and have made modifications to make it more accessible for new readers to the field.
1) We have added Table A1 in the Appendix summarizing terminology and acronyms. We have included a reference to this table at several places in the text, also responding to a comment from John Miller.
2) l175-188 (the final paragraph in the Introduction) provide a general overview of the focus of the paper. We have added a new paragraph following this one which contains a brief description of targeted users, potential data usage/application, and a list of key take-home messages in order to help the reader narrow down sections relevant to their interests.
L201: “The dataset assembled in this paper (McGrath et al., 2023) provides annual values of carbon dioxide emissions and sinks in fossil and LULUCF sectors for the EU27+UK across a range of data products based on different methodologies. This enables, for example, researchers producing datasets based on new methods a source of evaluation in the form of a best-estimate range of values. Decision makers may also find the results useful for targeting mitigation efforts in the EU27+UK by providing a more complete subsectorial breakdown. While NGHGIs already provide detailed data-based disaggregation based on activities, the dataset here adds additional constraints from independent data and models used outside of the inventory community. In addition, this paper outlines a methodology by which users of country-level CO2 emission data can compare datasets against NGHGIs and identify where agreement occurs for the right (and wrong) reasons. Section 3.1 highlights the extreme difference between current fossil emissions and uptake by the land surface. Section 3.2 looks at an ensemble of bottom-up estimates of fossil CO2 emissions, in addition to a preliminary inversion using atmospheric NO2 observations as a constraint. Sections 3.3.2 and 3.3.3 show that better agreement between the NGHGI and other models occurs when the models are driven strongly be category-specific data in forestry, grasslands, and croplands, as opposed to more generalized models created to couple to atmospheric models in global climate projections. Section 3.3.4 highlights the challenges currently facing comparison of atmospheric inversion models with NGHGIs, while simultaneously showing improvement by accounting for net emissions for lateral transfer of carbon between countries. Section 3.4 provides more discussion around uncertainties in both top-down and bottom-up estimates.”
3) In an effort to both reduce the length of the results section and to balance discussion around CO2 fossil and LULUCF emissions, we have removed text from Section 3.3 (LULUCF results), in particular a large section of 3.3.4 which is summarized elsewhere in the literature, and Section 3.3.2 (in response as well to concerns over the understandability of Figure 3). We have kept Figure 3 in the Appendix in case it is useful for readers familiar with the previous paper, and we reference it once in the main text. This eliminates almost 100 lines and one figure from the LULUCF Results section. Comments from John Miller led to additional shortening, which served to further balance presentation of CO2 fossil and LULUCF fluxes.
For the specific comments,
l148-163: Modifications made in response to the general comments above have reduced the number of detailed comparisons in the LULUCF results, and consequently address the comment here.
l390-395: We have added references to specific figures, taking into account additional text in response to John Miller:
L481: “An overview of all CO2 fossil and land datasets in this work (Fig. 1) leads to a series of conclusions: 1) Regardless of the method used (NGHGI, bottom-up models, top-down models), the timeseries of annual fluxes from fossil CO2 emissions rest almost one order of magnitude higher than removals from CO2 uptake/removal by the land surface and well outside uncertainty estimates (Figs. 1a-c); 2) Uncertainties are much larger in the LULUCF estimates than in the fossil CO2 estimates, regardless if one represents uncertainty by internal random error (i.e., the NGHGI totals in Fig. 1a, and the sub-sector LULUCF fluxes in Fig. 1d) or ensemble spread (i.e., bottom-up models in Fig. 1b, and the sub-sector LULUCF fluxes in Fig. 1e); 3) Interannual variability (IAV) is much more present in non-NGHGI LULUCF datasets (colored lines in Figs. 1b,c,e) than in NGHGI LULUCF datasets (Figs. 1a,d) or any of the fossil datasets (black lines in all subplots)."
l413-415: The caption text reads, “Panels (d) and (e) include a breakdown of the LULUCF flux”. Top-down fluxes are not included in panels (d) and (e). For clarity, we have changed this text to explicitly mention bottom-up fluxes: “include a breakdown of the [bottom-up] LULUCF flux”. In addition, the “CO2” in l414 has been changed so that the “2” is a subscript.
l484-1024: We have modified the beginning of Section 3.3 (l485-487) to help interpretation of the differences between the results in this work and those from Petrescu et al. (2021b). In particular, large changes can be seen when significant processes are added to a model (e.g., with ORCHIDEE), as this case is no longer simply a result of adding additional years of forcing data and re-running the previous model. We have also added this information to Table 2. Note that the actual graphs showing the "old" results have been removed, but we still think this text is relevant.
L594: “The following graphs occasionally show large differences compared to previously-reported values. This may happen when the model has undergone substantial changes since the work of Petrescu et al. (2021b), such as the case with ORCHIDEE and the addition of a dynamic nitrogen cycle coupled to the carbon cycle. Such cases are both identified in the text as appropriate and as well as in Table 2.”
l552: In response to the concerns over length; the LULUCF focus of the manuscript; and the understandability of Figure 3, we have removed Figure 3 and Section 3.3.2 as it was not essential and focused only on NGHGIs without connecting solidly to the other datasets. We have placed the figure in the Appendix with a reference from the text in the section on total bottom-up and top-down LULUCF estimates.
l1025: We have made this an independent section (3.4).
l1169: We have added the caveat in the conclusion suggested by the referee, taking into account a comment by John Miller:
L1086: “Fossil CO2 emissions are more straightforward to estimate than ecosystem fluxes due to extensive data collection around fuel production and trade, assuming that fuel statistics and accurate emission factors are available.”
For the technical corrections, we have: 1) corrected the font of “the notations” at l307; 2) put in bold “Forest land” at l591 to be consistent with following subsections (and removed the bold at l592, also for consistency); 3) removed the "old" results, and thus the faded panels, from all plots in response to this and a comment from John Miller; 4) doubled-checked the formatting of all section titles to be consistent.
Note that Figure 11 was removed in response to comments by John Miller, and thus this reviewer’s comment about the alignment of the caption is moot.
Citation: https://doi.org/10.5194/essd-2022-412-AC3
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AC3: 'Response to Anonymous Referee #1', Matthew McGrath, 16 Jul 2023
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RC2: 'Comment on essd-2022-412', John Miller, 09 Jun 2023
Please see attached .pdf, part 1 with general comments and part 2 with specific comments within th annotated manuscript. Please also note that the low score for completeness reflects the absence of datailed "data" availability for gridded model fluxes, etc., as described in the general comments.
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AC4: 'Response to John Miller', Matthew McGrath, 16 Jul 2023
We are happy that the reviewer finds our work to be “hugely impressive”, and that he has taken the time to go through the main text and make significant comments. It’s clear the reviewer has made an effort to understand the entire text, which helps improve the overall readability, accuracy, and consistency of our manuscript. We detail our responses below. We first respond to those comments requiring substantial amounts of work/reflection, which are open to interpretation, or to which we hold an alternative view. For straightforward comments (33 out of around 170 comments), we have made the proposed changes and noted them at the end of our response. Some of the comments in the text are covered by the general reviewer comments (1-5), and in those cases we group them together.
We apologize for any comments we may have missed.
Due to the length of this comment (32 pages), we have attached the entirety of the comment as a .pdf, in order to keep the formatting. We feel this helps clarity.
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AC4: 'Response to John Miller', Matthew McGrath, 16 Jul 2023
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AC1: 'Comment on essd-2022-412', Matthew McGrath, 16 Jul 2023
We have made more than 1000 modifications to the manuscript in response to reviewer and editorial comments. None of these change the main conclusions. Most of them aim to improve readability or serve as typographical corrections; a small number deal with technical oversights, which we have corrected. We thank all three reviewers for their time. We have added a line to the Acknowledgments: “MJM also thanks the three reviewers, in particular John Miller, for insightful comments which improved the quality of the final manuscript.”
Throughout our responses, we refer to line numbers in the original manuscript with a lowercase “l”, while line numbers in the final submitted manuscript (without track changes, i.e., the clean version) begin with a capital “L”.
We have taken seriously comments by two reviewers on the length and balance between the different parts. We summarize our changes in the following table (bold values indicate major changes):
We have heard and responded to reviewer concerns about length and balance between fossil and land fluxes. We initially removed two figures and almost 100 lines of text (placing one in the Appendix). Despite this, the length of the manuscript and the major sections were not very different, due to additional clarifications requested by reviewers. We therefore merged six other figures together into three, moving one of the original six to the Appendix as it illustrated a point we refer to in the main text, and combined the sections with those figures (Cropland and Grassland became a single section, as did bottom-up and top-down comparisons to LULUCF NGHGIs). This resulted in modest gains in the Forest land section, significant gains in the new combined Grassland/Cropland (25%, targeted for reduction due to the emissions being considerably lower than those on Forest land), and significant gains in the new combined LULUCF BU/TD (25%, while avoiding further reductions as this section is relatively more important to the overall picture). The Results section is now more balanced between fossil and land fluxes (3.8:1 ratio of land to other, down from 6.1:1 in the initial version). This is all shown in the attached table.
In addition to changes made to reviewer comments, we have made the following modifications.
The author “Fortems, A.” was changed to “Fortems-Cheiney, A.”.
The affiliation “UniSystems Company, Milan, Italy” has been added for authors M. Crippa and E. Solazzo. The JRC affiliation has been retained for M. Crippa, but dropped for E. Solazzo. The second affiliation “Universita degli Studi di Milano, Milano, Italy” has been added for M. Vizzarri.
Some minor typographical and formatting errors were corrected while responding to comments below.
We added some subsection numbers into the appendix, as the length made it difficult to quickly reference just by the name of the subsection. We also harmonized cross-referencing and section/subsection title formats, using the styles "Heading 1", "Heading 2", "Heading 3", and "Heading 4", and avoiding all colored font.
A new version of the data has been uploaded to Zenodo (https://doi.org/10.5281/zenodo.8148461). The primary change was the uncertainty of the NGHGI GL estimates, which was greatly reduced after an error was discovered. None of the conclusions were altered. We have replaced the old DOI address in the paper by this one.
Text in the appendix related to an inland water dataset was removed, as the dataset was neither updated nor used in this work (a related, but different dataset is described in the section on lateral fluxes).
In addition, five modifications were requested by ESSD upon initial manuscript submission. We have included those changes here:
- The citation (McGrath et al., 2023) was added to the DOI https://doi.org/10.5281/zenodo.8148461 in the abstract (note the update for the dataset version and year, as well)
- The Grassi et al (2022b) reference has finished review and been published. We have updated the citation to reflect this, and updated the citation to Grassi et al. (2023) due to the change in year. No other works are listed “in review”. We have removed two references listed as “in prep”.
- We have mentioned that one of the co-authors is a member of the editorial board of ESSD under the headline "Competing interests".
- We removed blue text in multiple places due to automatic hyperlinking and sectioning (changes not always tracked).
- “Sweden” was added to affiliation #15.
Note that figure and section numbers have been modified as a result of efforts to shorten the main text.
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AC2: 'Response to Alex Vermeulen', Matthew McGrath, 16 Jul 2023
We fully agree on the importance of recognizing data providers when publishing complex scientific analysis. We have already made a significant improvement in this direction compared to previous studies through extensive engagement with data providers whose products appear in all figures and analysis used in this work, including those from publicly-available model intercomparison projects like TRENDY and the GCP. This has resulted in a manuscript with 65 co-authors, 49 pages of supplementary information (including more detailed model and data descriptions), and around 180 references.
In regards to the SOCAT product, we provided a reference of Bakker et al., 2016 (l2188). No DOI exists for the version of SOCAT used in this work. However, we have provided the DOI to the most recent version at the same line, and we urge data providers to include DOIs with all versions of their work released to the public to enable accurate and transparent citations. The following has been added to the References (complete author list included in the paper, but omitted here):
“Bakker, D. C. E., …: Surface Ocean CO2 Atlas Database Version 2022 (SOCATv2022) (NCEI Accession 0253659). Subset v2021. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/1h9f-nb73. Accessed 01 July 2021.”
As noted by the commenter, some datasets are essential to atmospheric inversions, in particular the station-level observations of atmospheric carbon dioxide concentrations as a function of time. For the EUROCOM simulations, Monteil et al. (2020) and Thompson et al. (2020), already cited in our manuscript, provide good traceability for the particular observational dataset used, with several paragraphs of text and acknowledgements. We have added the citation of the atmospheric mole fraction with the following sentence at L2096: “The observational dataset used for the EUROCOM drought ensemble is accessible on the ICOS Carbon Portal (Drought 2018 Team and ICOS Atmosphere Thematic Centre, 2020).” We have also added the reference in the References:
“Drought 2018 Team, ICOS Atmosphere Thematic Centre: Drought-2018 atmospheric CO2 Mole Fraction product for 48 stations (96 sample heights), Integrated Carbon Observation System [data set], https://doi.org/10.18160/ERE9-9D85, 2020.”
We did something similar for the inversions used in the Global Carbon Budget, whose information is reported by Friedlingstein et al. (2022) in Table A4, adding explicit data references in our section GCP 2021.
L2080: “For details see Friedlingstein et al. (2022), in particular Table A4. Atmospheric observations for most model systems are taken from Cox et al. (2021) and Di Sarra et al. (2021). Note that one of the ensemble”
with the following references (full author list included in the manuscript):
“Cox, A… Multi-laboratory compilation of atmospheric carbon dioxide data for the period 1957–2019; obspack_CO2_1_GLOBALVIEWplus_v6.1_2021-03-01, NOAA Global Monitoring Laboratory [data set], https://doi.org/10.25925/20201204, 2021.
“Di Sarra, A. G.,…Multi-laboratory compilation of atmospheric carbon dioxide data for the years 2020–2021; obspack_CO2_1_NRT_v6.1.1_2021-05-17, NOAA Global Monitoring Laboratory [data set], https://doi.org/10.25925/20210517, 2021.”
In addition, we have made explicit data usage for inversions performed for the VERIFY project and not reported elsewhere by incorporating text and references as possible into the Appendix on regional inversions:
L1923: “For the regional inversions, atmospheric observations of CO2 were taken from multiple sources. For CarboScopeRegional, atmospheric observations were taken from the ICOS 2021.1 ATC (ICOS RI, 2021) and the GlobalViewPlus 6.1 product (Schuldt et al., 2021a). For the CIF-CHIMERE inversions, atmospheric observations of CO2 for the period 2005-2020 were taken from the ICOS 2021.1 ATC (ICOS RI, 2021) and SNO_SIFA L2 (SNO-IFA, 2023) releases, along with data distributed through the GlobalViewPlus 6.1 product (Schuldt et al., 2021a). For LUMIA inversions, atmospheric observations of CO2 for the period 2006-2018 were taken from the dataset prepared for the 2018 drought task force initiative (Thompson et al., 2020). For the more recent years, data were used from the ICOS 2021.1 ATC release (ICOS RI, 2021), along with data distributed through the GlobalViewPlus 7.0 product (Schuldt et al., 2021b), and, for four sites, data distributed through the World Data Center for Greenhouse Gases. “
The situation for bottom-up models is slightly different. We do not report results from machine-learning upscaled Fluxnet products (such as Fluxcom) in the current work. While evaluation against eddy covariance sites (such as FLUXNET and ICOS) is likely the norm for dynamic global vegetation models used in the TRENDY ensemble, it’s unclear how many models rely on such results for parameterization of various processes, nor how important these processes are for the overall model behavior compared to those which use other data streams. In other words, even if evaluations are run, it’s unclear to what extent models are changed in response to results. As such, we prefer to trust original citations of the models to resolve these questions, and we defer to editorial policies of the journal for additional citation standards.
Citation: https://doi.org/10.5194/essd-2022-412-AC2 -
AC3: 'Response to Anonymous Referee #1', Matthew McGrath, 16 Jul 2023
Thank you for the positive comments.
For the general comments, we take note of the length of the text and have made modifications to make it more accessible for new readers to the field.
1) We have added Table A1 in the Appendix summarizing terminology and acronyms. We have included a reference to this table at several places in the text, also responding to a comment from John Miller.
2) l175-188 (the final paragraph in the Introduction) provide a general overview of the focus of the paper. We have added a new paragraph following this one which contains a brief description of targeted users, potential data usage/application, and a list of key take-home messages in order to help the reader narrow down sections relevant to their interests.
L201: “The dataset assembled in this paper (McGrath et al., 2023) provides annual values of carbon dioxide emissions and sinks in fossil and LULUCF sectors for the EU27+UK across a range of data products based on different methodologies. This enables, for example, researchers producing datasets based on new methods a source of evaluation in the form of a best-estimate range of values. Decision makers may also find the results useful for targeting mitigation efforts in the EU27+UK by providing a more complete subsectorial breakdown. While NGHGIs already provide detailed data-based disaggregation based on activities, the dataset here adds additional constraints from independent data and models used outside of the inventory community. In addition, this paper outlines a methodology by which users of country-level CO2 emission data can compare datasets against NGHGIs and identify where agreement occurs for the right (and wrong) reasons. Section 3.1 highlights the extreme difference between current fossil emissions and uptake by the land surface. Section 3.2 looks at an ensemble of bottom-up estimates of fossil CO2 emissions, in addition to a preliminary inversion using atmospheric NO2 observations as a constraint. Sections 3.3.2 and 3.3.3 show that better agreement between the NGHGI and other models occurs when the models are driven strongly be category-specific data in forestry, grasslands, and croplands, as opposed to more generalized models created to couple to atmospheric models in global climate projections. Section 3.3.4 highlights the challenges currently facing comparison of atmospheric inversion models with NGHGIs, while simultaneously showing improvement by accounting for net emissions for lateral transfer of carbon between countries. Section 3.4 provides more discussion around uncertainties in both top-down and bottom-up estimates.”
3) In an effort to both reduce the length of the results section and to balance discussion around CO2 fossil and LULUCF emissions, we have removed text from Section 3.3 (LULUCF results), in particular a large section of 3.3.4 which is summarized elsewhere in the literature, and Section 3.3.2 (in response as well to concerns over the understandability of Figure 3). We have kept Figure 3 in the Appendix in case it is useful for readers familiar with the previous paper, and we reference it once in the main text. This eliminates almost 100 lines and one figure from the LULUCF Results section. Comments from John Miller led to additional shortening, which served to further balance presentation of CO2 fossil and LULUCF fluxes.
For the specific comments,
l148-163: Modifications made in response to the general comments above have reduced the number of detailed comparisons in the LULUCF results, and consequently address the comment here.
l390-395: We have added references to specific figures, taking into account additional text in response to John Miller:
L481: “An overview of all CO2 fossil and land datasets in this work (Fig. 1) leads to a series of conclusions: 1) Regardless of the method used (NGHGI, bottom-up models, top-down models), the timeseries of annual fluxes from fossil CO2 emissions rest almost one order of magnitude higher than removals from CO2 uptake/removal by the land surface and well outside uncertainty estimates (Figs. 1a-c); 2) Uncertainties are much larger in the LULUCF estimates than in the fossil CO2 estimates, regardless if one represents uncertainty by internal random error (i.e., the NGHGI totals in Fig. 1a, and the sub-sector LULUCF fluxes in Fig. 1d) or ensemble spread (i.e., bottom-up models in Fig. 1b, and the sub-sector LULUCF fluxes in Fig. 1e); 3) Interannual variability (IAV) is much more present in non-NGHGI LULUCF datasets (colored lines in Figs. 1b,c,e) than in NGHGI LULUCF datasets (Figs. 1a,d) or any of the fossil datasets (black lines in all subplots)."
l413-415: The caption text reads, “Panels (d) and (e) include a breakdown of the LULUCF flux”. Top-down fluxes are not included in panels (d) and (e). For clarity, we have changed this text to explicitly mention bottom-up fluxes: “include a breakdown of the [bottom-up] LULUCF flux”. In addition, the “CO2” in l414 has been changed so that the “2” is a subscript.
l484-1024: We have modified the beginning of Section 3.3 (l485-487) to help interpretation of the differences between the results in this work and those from Petrescu et al. (2021b). In particular, large changes can be seen when significant processes are added to a model (e.g., with ORCHIDEE), as this case is no longer simply a result of adding additional years of forcing data and re-running the previous model. We have also added this information to Table 2. Note that the actual graphs showing the "old" results have been removed, but we still think this text is relevant.
L594: “The following graphs occasionally show large differences compared to previously-reported values. This may happen when the model has undergone substantial changes since the work of Petrescu et al. (2021b), such as the case with ORCHIDEE and the addition of a dynamic nitrogen cycle coupled to the carbon cycle. Such cases are both identified in the text as appropriate and as well as in Table 2.”
l552: In response to the concerns over length; the LULUCF focus of the manuscript; and the understandability of Figure 3, we have removed Figure 3 and Section 3.3.2 as it was not essential and focused only on NGHGIs without connecting solidly to the other datasets. We have placed the figure in the Appendix with a reference from the text in the section on total bottom-up and top-down LULUCF estimates.
l1025: We have made this an independent section (3.4).
l1169: We have added the caveat in the conclusion suggested by the referee, taking into account a comment by John Miller:
L1086: “Fossil CO2 emissions are more straightforward to estimate than ecosystem fluxes due to extensive data collection around fuel production and trade, assuming that fuel statistics and accurate emission factors are available.”
For the technical corrections, we have: 1) corrected the font of “the notations” at l307; 2) put in bold “Forest land” at l591 to be consistent with following subsections (and removed the bold at l592, also for consistency); 3) removed the "old" results, and thus the faded panels, from all plots in response to this and a comment from John Miller; 4) doubled-checked the formatting of all section titles to be consistent.
Note that Figure 11 was removed in response to comments by John Miller, and thus this reviewer’s comment about the alignment of the caption is moot.
Citation: https://doi.org/10.5194/essd-2022-412-AC3 -
AC4: 'Response to John Miller', Matthew McGrath, 16 Jul 2023
We are happy that the reviewer finds our work to be “hugely impressive”, and that he has taken the time to go through the main text and make significant comments. It’s clear the reviewer has made an effort to understand the entire text, which helps improve the overall readability, accuracy, and consistency of our manuscript. We detail our responses below. We first respond to those comments requiring substantial amounts of work/reflection, which are open to interpretation, or to which we hold an alternative view. For straightforward comments (33 out of around 170 comments), we have made the proposed changes and noted them at the end of our response. Some of the comments in the text are covered by the general reviewer comments (1-5), and in those cases we group them together.
We apologize for any comments we may have missed.
Due to the length of this comment (32 pages), we have attached the entirety of the comment as a .pdf, in order to keep the formatting. We feel this helps clarity.
Status: closed
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CC1: 'Comment on essd-2022-412', Alex Vermeulen, 14 Feb 2023
This paper illustrates a general problem in attribution of data used in complex scientific analyses like discussed here. Especially when data is used from other studies (GCP, Eurocom etc) that are already published in the peer reviewed literature that again base on underlying data from observations. The risk is that in the end this observational data provision that forms the basis of the analyses completely gets out of sight. This is a threat for science, as more and more funding for these so essential observations hinges on data providers being able to show the stakeholders where and how much their data has been used, preferably through data citation tracking. As long as scientists do not apply proper data citation and data providers do not always provide proper data identifiers, publishers will not feel the need to demand and track data citation, so we need to solve this chicken and egg problem together as scientists and data providers by an effort to give some attention to the data citation issue.
I would argue that for this specific meta analysis it is necessary to provide at least acknowledgment and if possible also data citations for the observational data that were essential for the inversions and calibration of the vegetation models, especially when the references to the original analyses are in some cases already lacking the correct attribution. In this paper I only could track that the SOCAT product is mentioned as (aggregated) observation based data product, but the DOI citation did not make it to the references. As far as I know many atmospheric inversions make use of the NOAA Obspack data product, complemented with other aggregations like from ICOS atmosphere, and many vegetation models or machine learning data fusion products base on FLUXNET or other (ICOS) ecosystem flux datasets. So I would recommend some effort to extend the paper with better attribution to essential (observational) datasets (in)directly used in the analyses.
Citation: https://doi.org/10.5194/essd-2022-412-CC1 -
AC2: 'Response to Alex Vermeulen', Matthew McGrath, 16 Jul 2023
We fully agree on the importance of recognizing data providers when publishing complex scientific analysis. We have already made a significant improvement in this direction compared to previous studies through extensive engagement with data providers whose products appear in all figures and analysis used in this work, including those from publicly-available model intercomparison projects like TRENDY and the GCP. This has resulted in a manuscript with 65 co-authors, 49 pages of supplementary information (including more detailed model and data descriptions), and around 180 references.
In regards to the SOCAT product, we provided a reference of Bakker et al., 2016 (l2188). No DOI exists for the version of SOCAT used in this work. However, we have provided the DOI to the most recent version at the same line, and we urge data providers to include DOIs with all versions of their work released to the public to enable accurate and transparent citations. The following has been added to the References (complete author list included in the paper, but omitted here):
“Bakker, D. C. E., …: Surface Ocean CO2 Atlas Database Version 2022 (SOCATv2022) (NCEI Accession 0253659). Subset v2021. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/1h9f-nb73. Accessed 01 July 2021.”
As noted by the commenter, some datasets are essential to atmospheric inversions, in particular the station-level observations of atmospheric carbon dioxide concentrations as a function of time. For the EUROCOM simulations, Monteil et al. (2020) and Thompson et al. (2020), already cited in our manuscript, provide good traceability for the particular observational dataset used, with several paragraphs of text and acknowledgements. We have added the citation of the atmospheric mole fraction with the following sentence at L2096: “The observational dataset used for the EUROCOM drought ensemble is accessible on the ICOS Carbon Portal (Drought 2018 Team and ICOS Atmosphere Thematic Centre, 2020).” We have also added the reference in the References:
“Drought 2018 Team, ICOS Atmosphere Thematic Centre: Drought-2018 atmospheric CO2 Mole Fraction product for 48 stations (96 sample heights), Integrated Carbon Observation System [data set], https://doi.org/10.18160/ERE9-9D85, 2020.”
We did something similar for the inversions used in the Global Carbon Budget, whose information is reported by Friedlingstein et al. (2022) in Table A4, adding explicit data references in our section GCP 2021.
L2080: “For details see Friedlingstein et al. (2022), in particular Table A4. Atmospheric observations for most model systems are taken from Cox et al. (2021) and Di Sarra et al. (2021). Note that one of the ensemble”
with the following references (full author list included in the manuscript):
“Cox, A… Multi-laboratory compilation of atmospheric carbon dioxide data for the period 1957–2019; obspack_CO2_1_GLOBALVIEWplus_v6.1_2021-03-01, NOAA Global Monitoring Laboratory [data set], https://doi.org/10.25925/20201204, 2021.
“Di Sarra, A. G.,…Multi-laboratory compilation of atmospheric carbon dioxide data for the years 2020–2021; obspack_CO2_1_NRT_v6.1.1_2021-05-17, NOAA Global Monitoring Laboratory [data set], https://doi.org/10.25925/20210517, 2021.”
In addition, we have made explicit data usage for inversions performed for the VERIFY project and not reported elsewhere by incorporating text and references as possible into the Appendix on regional inversions:
L1923: “For the regional inversions, atmospheric observations of CO2 were taken from multiple sources. For CarboScopeRegional, atmospheric observations were taken from the ICOS 2021.1 ATC (ICOS RI, 2021) and the GlobalViewPlus 6.1 product (Schuldt et al., 2021a). For the CIF-CHIMERE inversions, atmospheric observations of CO2 for the period 2005-2020 were taken from the ICOS 2021.1 ATC (ICOS RI, 2021) and SNO_SIFA L2 (SNO-IFA, 2023) releases, along with data distributed through the GlobalViewPlus 6.1 product (Schuldt et al., 2021a). For LUMIA inversions, atmospheric observations of CO2 for the period 2006-2018 were taken from the dataset prepared for the 2018 drought task force initiative (Thompson et al., 2020). For the more recent years, data were used from the ICOS 2021.1 ATC release (ICOS RI, 2021), along with data distributed through the GlobalViewPlus 7.0 product (Schuldt et al., 2021b), and, for four sites, data distributed through the World Data Center for Greenhouse Gases. “
The situation for bottom-up models is slightly different. We do not report results from machine-learning upscaled Fluxnet products (such as Fluxcom) in the current work. While evaluation against eddy covariance sites (such as FLUXNET and ICOS) is likely the norm for dynamic global vegetation models used in the TRENDY ensemble, it’s unclear how many models rely on such results for parameterization of various processes, nor how important these processes are for the overall model behavior compared to those which use other data streams. In other words, even if evaluations are run, it’s unclear to what extent models are changed in response to results. As such, we prefer to trust original citations of the models to resolve these questions, and we defer to editorial policies of the journal for additional citation standards.
Citation: https://doi.org/10.5194/essd-2022-412-AC2
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AC2: 'Response to Alex Vermeulen', Matthew McGrath, 16 Jul 2023
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RC1: 'Comment on essd-2022-412', Anonymous Referee #1, 13 Mar 2023
Overall, this paper is well written and would be helpful, particularly for those communities working closely with the datasets used in the paper. The dataset itself may not be something unique, instead, this paper could support the future GHG emissions inventory synthesis work in Europe and potentially other Annex I parties in the world. However, possibly by the nature of this type of dataset, the text seems to be a bit lengthy and tedious, especially the result section. Although the authors described the details of the data sources and models used in this work, the dataset itself may not be easy to use/understand for users new to this field. A brief description of targeted users, potential data usage/application, and a list of key take-home messages (in the intro or conclusion) may help the reader to grasp the paper's contents (and narrow down sections relevant to the readers). Plese see the attached documents for more detail comments.
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AC3: 'Response to Anonymous Referee #1', Matthew McGrath, 16 Jul 2023
Thank you for the positive comments.
For the general comments, we take note of the length of the text and have made modifications to make it more accessible for new readers to the field.
1) We have added Table A1 in the Appendix summarizing terminology and acronyms. We have included a reference to this table at several places in the text, also responding to a comment from John Miller.
2) l175-188 (the final paragraph in the Introduction) provide a general overview of the focus of the paper. We have added a new paragraph following this one which contains a brief description of targeted users, potential data usage/application, and a list of key take-home messages in order to help the reader narrow down sections relevant to their interests.
L201: “The dataset assembled in this paper (McGrath et al., 2023) provides annual values of carbon dioxide emissions and sinks in fossil and LULUCF sectors for the EU27+UK across a range of data products based on different methodologies. This enables, for example, researchers producing datasets based on new methods a source of evaluation in the form of a best-estimate range of values. Decision makers may also find the results useful for targeting mitigation efforts in the EU27+UK by providing a more complete subsectorial breakdown. While NGHGIs already provide detailed data-based disaggregation based on activities, the dataset here adds additional constraints from independent data and models used outside of the inventory community. In addition, this paper outlines a methodology by which users of country-level CO2 emission data can compare datasets against NGHGIs and identify where agreement occurs for the right (and wrong) reasons. Section 3.1 highlights the extreme difference between current fossil emissions and uptake by the land surface. Section 3.2 looks at an ensemble of bottom-up estimates of fossil CO2 emissions, in addition to a preliminary inversion using atmospheric NO2 observations as a constraint. Sections 3.3.2 and 3.3.3 show that better agreement between the NGHGI and other models occurs when the models are driven strongly be category-specific data in forestry, grasslands, and croplands, as opposed to more generalized models created to couple to atmospheric models in global climate projections. Section 3.3.4 highlights the challenges currently facing comparison of atmospheric inversion models with NGHGIs, while simultaneously showing improvement by accounting for net emissions for lateral transfer of carbon between countries. Section 3.4 provides more discussion around uncertainties in both top-down and bottom-up estimates.”
3) In an effort to both reduce the length of the results section and to balance discussion around CO2 fossil and LULUCF emissions, we have removed text from Section 3.3 (LULUCF results), in particular a large section of 3.3.4 which is summarized elsewhere in the literature, and Section 3.3.2 (in response as well to concerns over the understandability of Figure 3). We have kept Figure 3 in the Appendix in case it is useful for readers familiar with the previous paper, and we reference it once in the main text. This eliminates almost 100 lines and one figure from the LULUCF Results section. Comments from John Miller led to additional shortening, which served to further balance presentation of CO2 fossil and LULUCF fluxes.
For the specific comments,
l148-163: Modifications made in response to the general comments above have reduced the number of detailed comparisons in the LULUCF results, and consequently address the comment here.
l390-395: We have added references to specific figures, taking into account additional text in response to John Miller:
L481: “An overview of all CO2 fossil and land datasets in this work (Fig. 1) leads to a series of conclusions: 1) Regardless of the method used (NGHGI, bottom-up models, top-down models), the timeseries of annual fluxes from fossil CO2 emissions rest almost one order of magnitude higher than removals from CO2 uptake/removal by the land surface and well outside uncertainty estimates (Figs. 1a-c); 2) Uncertainties are much larger in the LULUCF estimates than in the fossil CO2 estimates, regardless if one represents uncertainty by internal random error (i.e., the NGHGI totals in Fig. 1a, and the sub-sector LULUCF fluxes in Fig. 1d) or ensemble spread (i.e., bottom-up models in Fig. 1b, and the sub-sector LULUCF fluxes in Fig. 1e); 3) Interannual variability (IAV) is much more present in non-NGHGI LULUCF datasets (colored lines in Figs. 1b,c,e) than in NGHGI LULUCF datasets (Figs. 1a,d) or any of the fossil datasets (black lines in all subplots)."
l413-415: The caption text reads, “Panels (d) and (e) include a breakdown of the LULUCF flux”. Top-down fluxes are not included in panels (d) and (e). For clarity, we have changed this text to explicitly mention bottom-up fluxes: “include a breakdown of the [bottom-up] LULUCF flux”. In addition, the “CO2” in l414 has been changed so that the “2” is a subscript.
l484-1024: We have modified the beginning of Section 3.3 (l485-487) to help interpretation of the differences between the results in this work and those from Petrescu et al. (2021b). In particular, large changes can be seen when significant processes are added to a model (e.g., with ORCHIDEE), as this case is no longer simply a result of adding additional years of forcing data and re-running the previous model. We have also added this information to Table 2. Note that the actual graphs showing the "old" results have been removed, but we still think this text is relevant.
L594: “The following graphs occasionally show large differences compared to previously-reported values. This may happen when the model has undergone substantial changes since the work of Petrescu et al. (2021b), such as the case with ORCHIDEE and the addition of a dynamic nitrogen cycle coupled to the carbon cycle. Such cases are both identified in the text as appropriate and as well as in Table 2.”
l552: In response to the concerns over length; the LULUCF focus of the manuscript; and the understandability of Figure 3, we have removed Figure 3 and Section 3.3.2 as it was not essential and focused only on NGHGIs without connecting solidly to the other datasets. We have placed the figure in the Appendix with a reference from the text in the section on total bottom-up and top-down LULUCF estimates.
l1025: We have made this an independent section (3.4).
l1169: We have added the caveat in the conclusion suggested by the referee, taking into account a comment by John Miller:
L1086: “Fossil CO2 emissions are more straightforward to estimate than ecosystem fluxes due to extensive data collection around fuel production and trade, assuming that fuel statistics and accurate emission factors are available.”
For the technical corrections, we have: 1) corrected the font of “the notations” at l307; 2) put in bold “Forest land” at l591 to be consistent with following subsections (and removed the bold at l592, also for consistency); 3) removed the "old" results, and thus the faded panels, from all plots in response to this and a comment from John Miller; 4) doubled-checked the formatting of all section titles to be consistent.
Note that Figure 11 was removed in response to comments by John Miller, and thus this reviewer’s comment about the alignment of the caption is moot.
Citation: https://doi.org/10.5194/essd-2022-412-AC3
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AC3: 'Response to Anonymous Referee #1', Matthew McGrath, 16 Jul 2023
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RC2: 'Comment on essd-2022-412', John Miller, 09 Jun 2023
Please see attached .pdf, part 1 with general comments and part 2 with specific comments within th annotated manuscript. Please also note that the low score for completeness reflects the absence of datailed "data" availability for gridded model fluxes, etc., as described in the general comments.
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AC4: 'Response to John Miller', Matthew McGrath, 16 Jul 2023
We are happy that the reviewer finds our work to be “hugely impressive”, and that he has taken the time to go through the main text and make significant comments. It’s clear the reviewer has made an effort to understand the entire text, which helps improve the overall readability, accuracy, and consistency of our manuscript. We detail our responses below. We first respond to those comments requiring substantial amounts of work/reflection, which are open to interpretation, or to which we hold an alternative view. For straightforward comments (33 out of around 170 comments), we have made the proposed changes and noted them at the end of our response. Some of the comments in the text are covered by the general reviewer comments (1-5), and in those cases we group them together.
We apologize for any comments we may have missed.
Due to the length of this comment (32 pages), we have attached the entirety of the comment as a .pdf, in order to keep the formatting. We feel this helps clarity.
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AC4: 'Response to John Miller', Matthew McGrath, 16 Jul 2023
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AC1: 'Comment on essd-2022-412', Matthew McGrath, 16 Jul 2023
We have made more than 1000 modifications to the manuscript in response to reviewer and editorial comments. None of these change the main conclusions. Most of them aim to improve readability or serve as typographical corrections; a small number deal with technical oversights, which we have corrected. We thank all three reviewers for their time. We have added a line to the Acknowledgments: “MJM also thanks the three reviewers, in particular John Miller, for insightful comments which improved the quality of the final manuscript.”
Throughout our responses, we refer to line numbers in the original manuscript with a lowercase “l”, while line numbers in the final submitted manuscript (without track changes, i.e., the clean version) begin with a capital “L”.
We have taken seriously comments by two reviewers on the length and balance between the different parts. We summarize our changes in the following table (bold values indicate major changes):
We have heard and responded to reviewer concerns about length and balance between fossil and land fluxes. We initially removed two figures and almost 100 lines of text (placing one in the Appendix). Despite this, the length of the manuscript and the major sections were not very different, due to additional clarifications requested by reviewers. We therefore merged six other figures together into three, moving one of the original six to the Appendix as it illustrated a point we refer to in the main text, and combined the sections with those figures (Cropland and Grassland became a single section, as did bottom-up and top-down comparisons to LULUCF NGHGIs). This resulted in modest gains in the Forest land section, significant gains in the new combined Grassland/Cropland (25%, targeted for reduction due to the emissions being considerably lower than those on Forest land), and significant gains in the new combined LULUCF BU/TD (25%, while avoiding further reductions as this section is relatively more important to the overall picture). The Results section is now more balanced between fossil and land fluxes (3.8:1 ratio of land to other, down from 6.1:1 in the initial version). This is all shown in the attached table.
In addition to changes made to reviewer comments, we have made the following modifications.
The author “Fortems, A.” was changed to “Fortems-Cheiney, A.”.
The affiliation “UniSystems Company, Milan, Italy” has been added for authors M. Crippa and E. Solazzo. The JRC affiliation has been retained for M. Crippa, but dropped for E. Solazzo. The second affiliation “Universita degli Studi di Milano, Milano, Italy” has been added for M. Vizzarri.
Some minor typographical and formatting errors were corrected while responding to comments below.
We added some subsection numbers into the appendix, as the length made it difficult to quickly reference just by the name of the subsection. We also harmonized cross-referencing and section/subsection title formats, using the styles "Heading 1", "Heading 2", "Heading 3", and "Heading 4", and avoiding all colored font.
A new version of the data has been uploaded to Zenodo (https://doi.org/10.5281/zenodo.8148461). The primary change was the uncertainty of the NGHGI GL estimates, which was greatly reduced after an error was discovered. None of the conclusions were altered. We have replaced the old DOI address in the paper by this one.
Text in the appendix related to an inland water dataset was removed, as the dataset was neither updated nor used in this work (a related, but different dataset is described in the section on lateral fluxes).
In addition, five modifications were requested by ESSD upon initial manuscript submission. We have included those changes here:
- The citation (McGrath et al., 2023) was added to the DOI https://doi.org/10.5281/zenodo.8148461 in the abstract (note the update for the dataset version and year, as well)
- The Grassi et al (2022b) reference has finished review and been published. We have updated the citation to reflect this, and updated the citation to Grassi et al. (2023) due to the change in year. No other works are listed “in review”. We have removed two references listed as “in prep”.
- We have mentioned that one of the co-authors is a member of the editorial board of ESSD under the headline "Competing interests".
- We removed blue text in multiple places due to automatic hyperlinking and sectioning (changes not always tracked).
- “Sweden” was added to affiliation #15.
Note that figure and section numbers have been modified as a result of efforts to shorten the main text.
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AC2: 'Response to Alex Vermeulen', Matthew McGrath, 16 Jul 2023
We fully agree on the importance of recognizing data providers when publishing complex scientific analysis. We have already made a significant improvement in this direction compared to previous studies through extensive engagement with data providers whose products appear in all figures and analysis used in this work, including those from publicly-available model intercomparison projects like TRENDY and the GCP. This has resulted in a manuscript with 65 co-authors, 49 pages of supplementary information (including more detailed model and data descriptions), and around 180 references.
In regards to the SOCAT product, we provided a reference of Bakker et al., 2016 (l2188). No DOI exists for the version of SOCAT used in this work. However, we have provided the DOI to the most recent version at the same line, and we urge data providers to include DOIs with all versions of their work released to the public to enable accurate and transparent citations. The following has been added to the References (complete author list included in the paper, but omitted here):
“Bakker, D. C. E., …: Surface Ocean CO2 Atlas Database Version 2022 (SOCATv2022) (NCEI Accession 0253659). Subset v2021. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/1h9f-nb73. Accessed 01 July 2021.”
As noted by the commenter, some datasets are essential to atmospheric inversions, in particular the station-level observations of atmospheric carbon dioxide concentrations as a function of time. For the EUROCOM simulations, Monteil et al. (2020) and Thompson et al. (2020), already cited in our manuscript, provide good traceability for the particular observational dataset used, with several paragraphs of text and acknowledgements. We have added the citation of the atmospheric mole fraction with the following sentence at L2096: “The observational dataset used for the EUROCOM drought ensemble is accessible on the ICOS Carbon Portal (Drought 2018 Team and ICOS Atmosphere Thematic Centre, 2020).” We have also added the reference in the References:
“Drought 2018 Team, ICOS Atmosphere Thematic Centre: Drought-2018 atmospheric CO2 Mole Fraction product for 48 stations (96 sample heights), Integrated Carbon Observation System [data set], https://doi.org/10.18160/ERE9-9D85, 2020.”
We did something similar for the inversions used in the Global Carbon Budget, whose information is reported by Friedlingstein et al. (2022) in Table A4, adding explicit data references in our section GCP 2021.
L2080: “For details see Friedlingstein et al. (2022), in particular Table A4. Atmospheric observations for most model systems are taken from Cox et al. (2021) and Di Sarra et al. (2021). Note that one of the ensemble”
with the following references (full author list included in the manuscript):
“Cox, A… Multi-laboratory compilation of atmospheric carbon dioxide data for the period 1957–2019; obspack_CO2_1_GLOBALVIEWplus_v6.1_2021-03-01, NOAA Global Monitoring Laboratory [data set], https://doi.org/10.25925/20201204, 2021.
“Di Sarra, A. G.,…Multi-laboratory compilation of atmospheric carbon dioxide data for the years 2020–2021; obspack_CO2_1_NRT_v6.1.1_2021-05-17, NOAA Global Monitoring Laboratory [data set], https://doi.org/10.25925/20210517, 2021.”
In addition, we have made explicit data usage for inversions performed for the VERIFY project and not reported elsewhere by incorporating text and references as possible into the Appendix on regional inversions:
L1923: “For the regional inversions, atmospheric observations of CO2 were taken from multiple sources. For CarboScopeRegional, atmospheric observations were taken from the ICOS 2021.1 ATC (ICOS RI, 2021) and the GlobalViewPlus 6.1 product (Schuldt et al., 2021a). For the CIF-CHIMERE inversions, atmospheric observations of CO2 for the period 2005-2020 were taken from the ICOS 2021.1 ATC (ICOS RI, 2021) and SNO_SIFA L2 (SNO-IFA, 2023) releases, along with data distributed through the GlobalViewPlus 6.1 product (Schuldt et al., 2021a). For LUMIA inversions, atmospheric observations of CO2 for the period 2006-2018 were taken from the dataset prepared for the 2018 drought task force initiative (Thompson et al., 2020). For the more recent years, data were used from the ICOS 2021.1 ATC release (ICOS RI, 2021), along with data distributed through the GlobalViewPlus 7.0 product (Schuldt et al., 2021b), and, for four sites, data distributed through the World Data Center for Greenhouse Gases. “
The situation for bottom-up models is slightly different. We do not report results from machine-learning upscaled Fluxnet products (such as Fluxcom) in the current work. While evaluation against eddy covariance sites (such as FLUXNET and ICOS) is likely the norm for dynamic global vegetation models used in the TRENDY ensemble, it’s unclear how many models rely on such results for parameterization of various processes, nor how important these processes are for the overall model behavior compared to those which use other data streams. In other words, even if evaluations are run, it’s unclear to what extent models are changed in response to results. As such, we prefer to trust original citations of the models to resolve these questions, and we defer to editorial policies of the journal for additional citation standards.
Citation: https://doi.org/10.5194/essd-2022-412-AC2 -
AC3: 'Response to Anonymous Referee #1', Matthew McGrath, 16 Jul 2023
Thank you for the positive comments.
For the general comments, we take note of the length of the text and have made modifications to make it more accessible for new readers to the field.
1) We have added Table A1 in the Appendix summarizing terminology and acronyms. We have included a reference to this table at several places in the text, also responding to a comment from John Miller.
2) l175-188 (the final paragraph in the Introduction) provide a general overview of the focus of the paper. We have added a new paragraph following this one which contains a brief description of targeted users, potential data usage/application, and a list of key take-home messages in order to help the reader narrow down sections relevant to their interests.
L201: “The dataset assembled in this paper (McGrath et al., 2023) provides annual values of carbon dioxide emissions and sinks in fossil and LULUCF sectors for the EU27+UK across a range of data products based on different methodologies. This enables, for example, researchers producing datasets based on new methods a source of evaluation in the form of a best-estimate range of values. Decision makers may also find the results useful for targeting mitigation efforts in the EU27+UK by providing a more complete subsectorial breakdown. While NGHGIs already provide detailed data-based disaggregation based on activities, the dataset here adds additional constraints from independent data and models used outside of the inventory community. In addition, this paper outlines a methodology by which users of country-level CO2 emission data can compare datasets against NGHGIs and identify where agreement occurs for the right (and wrong) reasons. Section 3.1 highlights the extreme difference between current fossil emissions and uptake by the land surface. Section 3.2 looks at an ensemble of bottom-up estimates of fossil CO2 emissions, in addition to a preliminary inversion using atmospheric NO2 observations as a constraint. Sections 3.3.2 and 3.3.3 show that better agreement between the NGHGI and other models occurs when the models are driven strongly be category-specific data in forestry, grasslands, and croplands, as opposed to more generalized models created to couple to atmospheric models in global climate projections. Section 3.3.4 highlights the challenges currently facing comparison of atmospheric inversion models with NGHGIs, while simultaneously showing improvement by accounting for net emissions for lateral transfer of carbon between countries. Section 3.4 provides more discussion around uncertainties in both top-down and bottom-up estimates.”
3) In an effort to both reduce the length of the results section and to balance discussion around CO2 fossil and LULUCF emissions, we have removed text from Section 3.3 (LULUCF results), in particular a large section of 3.3.4 which is summarized elsewhere in the literature, and Section 3.3.2 (in response as well to concerns over the understandability of Figure 3). We have kept Figure 3 in the Appendix in case it is useful for readers familiar with the previous paper, and we reference it once in the main text. This eliminates almost 100 lines and one figure from the LULUCF Results section. Comments from John Miller led to additional shortening, which served to further balance presentation of CO2 fossil and LULUCF fluxes.
For the specific comments,
l148-163: Modifications made in response to the general comments above have reduced the number of detailed comparisons in the LULUCF results, and consequently address the comment here.
l390-395: We have added references to specific figures, taking into account additional text in response to John Miller:
L481: “An overview of all CO2 fossil and land datasets in this work (Fig. 1) leads to a series of conclusions: 1) Regardless of the method used (NGHGI, bottom-up models, top-down models), the timeseries of annual fluxes from fossil CO2 emissions rest almost one order of magnitude higher than removals from CO2 uptake/removal by the land surface and well outside uncertainty estimates (Figs. 1a-c); 2) Uncertainties are much larger in the LULUCF estimates than in the fossil CO2 estimates, regardless if one represents uncertainty by internal random error (i.e., the NGHGI totals in Fig. 1a, and the sub-sector LULUCF fluxes in Fig. 1d) or ensemble spread (i.e., bottom-up models in Fig. 1b, and the sub-sector LULUCF fluxes in Fig. 1e); 3) Interannual variability (IAV) is much more present in non-NGHGI LULUCF datasets (colored lines in Figs. 1b,c,e) than in NGHGI LULUCF datasets (Figs. 1a,d) or any of the fossil datasets (black lines in all subplots)."
l413-415: The caption text reads, “Panels (d) and (e) include a breakdown of the LULUCF flux”. Top-down fluxes are not included in panels (d) and (e). For clarity, we have changed this text to explicitly mention bottom-up fluxes: “include a breakdown of the [bottom-up] LULUCF flux”. In addition, the “CO2” in l414 has been changed so that the “2” is a subscript.
l484-1024: We have modified the beginning of Section 3.3 (l485-487) to help interpretation of the differences between the results in this work and those from Petrescu et al. (2021b). In particular, large changes can be seen when significant processes are added to a model (e.g., with ORCHIDEE), as this case is no longer simply a result of adding additional years of forcing data and re-running the previous model. We have also added this information to Table 2. Note that the actual graphs showing the "old" results have been removed, but we still think this text is relevant.
L594: “The following graphs occasionally show large differences compared to previously-reported values. This may happen when the model has undergone substantial changes since the work of Petrescu et al. (2021b), such as the case with ORCHIDEE and the addition of a dynamic nitrogen cycle coupled to the carbon cycle. Such cases are both identified in the text as appropriate and as well as in Table 2.”
l552: In response to the concerns over length; the LULUCF focus of the manuscript; and the understandability of Figure 3, we have removed Figure 3 and Section 3.3.2 as it was not essential and focused only on NGHGIs without connecting solidly to the other datasets. We have placed the figure in the Appendix with a reference from the text in the section on total bottom-up and top-down LULUCF estimates.
l1025: We have made this an independent section (3.4).
l1169: We have added the caveat in the conclusion suggested by the referee, taking into account a comment by John Miller:
L1086: “Fossil CO2 emissions are more straightforward to estimate than ecosystem fluxes due to extensive data collection around fuel production and trade, assuming that fuel statistics and accurate emission factors are available.”
For the technical corrections, we have: 1) corrected the font of “the notations” at l307; 2) put in bold “Forest land” at l591 to be consistent with following subsections (and removed the bold at l592, also for consistency); 3) removed the "old" results, and thus the faded panels, from all plots in response to this and a comment from John Miller; 4) doubled-checked the formatting of all section titles to be consistent.
Note that Figure 11 was removed in response to comments by John Miller, and thus this reviewer’s comment about the alignment of the caption is moot.
Citation: https://doi.org/10.5194/essd-2022-412-AC3 -
AC4: 'Response to John Miller', Matthew McGrath, 16 Jul 2023
We are happy that the reviewer finds our work to be “hugely impressive”, and that he has taken the time to go through the main text and make significant comments. It’s clear the reviewer has made an effort to understand the entire text, which helps improve the overall readability, accuracy, and consistency of our manuscript. We detail our responses below. We first respond to those comments requiring substantial amounts of work/reflection, which are open to interpretation, or to which we hold an alternative view. For straightforward comments (33 out of around 170 comments), we have made the proposed changes and noted them at the end of our response. Some of the comments in the text are covered by the general reviewer comments (1-5), and in those cases we group them together.
We apologize for any comments we may have missed.
Due to the length of this comment (32 pages), we have attached the entirety of the comment as a .pdf, in order to keep the formatting. We feel this helps clarity.
Matthew Joseph McGrath et al.
Data sets
Data for the consolidated European synthesis of CO2 emissions and removals for EU27 and UK: 1990-2020 McGrath, Matthew Joseph; Petrescu, Ana Maria Roxana; Peylin, Philippe; Andrew, Robbie M.; Matthews, Bradley; Dentener, Frank; Balkovič, Juraj; Bastrikov, Vladislav; Becker, Meike; Broquet, Gregoire; Ciais, Philippe; Fortems, Audrey; Ganzenmüller, Raphael; Grassi, Giacomo; Harris, Ian; Jones, Matthew; Knauer, Juergen; Kuhnert, Matthias; Monteil, Guillaume; Munassar, Saqr; Palmer, Paul I.; Peters, Glen P.; Qiu, Chunjing; Schelhaas, Mart-Jan; Tarasova, Oksana; Vizzarri, Matteo; Winkler, Karina; Balsamo, Gianpaolo; Berchet, Antoine; Briggs, Peter; Brockmann, Patrick; Chevallier, Frédéric; Conchedda, Giulia; Crippa, Monica; Dellaert, Stijn; Denier van der Gon, Hugo A. C.; Filipek, Sara; Friedlingstein, Pierre; Fuchs, Richard; Gauss, Michael; Gerbig, Christoph; Guizzardi, Diego; Günther, Dirk; Houghton, Richard A.; Janssens-Maenhout, Greet; Lauerwald, Ronny; Lerink, Bas; Luijkx, Ingrid T.; Moulas, Géraud; Muntean, Marilena; Nabuurs, Gert-Jan; Paquirissamy, Aurélie; Perugini, Lucia; Peters, Wouter; Pilli, Roberto; Pongratz, Julia; Regnier, Pierre; Scholze, Marko; Serengil, Yusuf; Smith, Pete; Solazzo, Efisio; Thompson, Rona L.; Tubiello, Francesco N.; Vesala, Timo; Walther, Sophia https://doi.org/10.5281/zenodo.7365863
Matthew Joseph McGrath et al.
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