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: open (until 13 Apr 2023)
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CC1: 'Comment on essd-2022-412', Alex Vermeulen, 14 Feb 2023
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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 -
RC1: 'Comment on essd-2022-412', Anonymous Referee #1, 13 Mar 2023
reply
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.
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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