The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990-2019

Ana Maria Roxana Petrescu; Chunjing Qiu; Matthew J. McGrath; Philippe Peylin; Glen P. Peters; Philippe Ciais; Rona L. Thompson; Aki Tsuruta; Dominik Brunner; Matthias Kuhnert; Bradley Matthews; Paul I. Palmer; Oksana Tarasova; Pierre Regnier; Ronny Lauerwald; David Bastviken; Lena Höglund-Isaksson; Wilfried Winiwarter; Giuseppe Etiope; Tuula Aalto; Gianpaolo Balsamo; Vladislav Bastrikov; Antoine Berchet; Patrick Brockmann; Giancarlo Ciotoli; Giulia Conchedda; Monica Crippa; Frank Dentener; Christine D. Groot Zwaaftink; Diego Guizzardi; Dirk Günther; Jean Matthieu Haussaire; Sander Houweling; Greet Janssens-Maenhout; Massaer Kouyate; Adrian Leip; Antti Leppänen; Emanuele Lugato; Manon Maisonnier; Alistair J. Manning; Tiina Markkanen; Joe McNorton; Marilena Muntean; Gabriel D. Oreggioni; Prabir K. Patra; Lucia Perugini; Isabelle Pison; Maarit T. Raivonen; Marielle Saunois; Arjo J. Segers; Pete Smith; Efisio Solazzo; Hanqin Tian; Francesco N. Tubiello; Timo Vesala; Guido R. Van Der Werf; Chris Wilson; Sönke Zaehle

doi:10.5194/essd-15-1197-2023

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990-2019

Ana Maria Roxana Petrescu^*, Chunjing Qiu, Matthew J. McGrath, Philippe Peylin, Glen P. Peters, Philippe Ciais, Rona L. Thompson, Aki Tsuruta, Dominik Brunner, Matthias Kuhnert, Bradley Matthews, Paul I. Palmer, Oksana Tarasova, Pierre Regnier, Ronny Lauerwald, David Bastviken, Lena Höglund-Isaksson, Wilfried Winiwarter, Giuseppe Etiope, Tuula AaltoGianpaolo Balsamo, Vladislav Bastrikov, Antoine Berchet, Patrick Brockmann, Giancarlo Ciotoli, Giulia Conchedda, Monica Crippa, Frank Dentener, Christine D. Groot Zwaaftink, Diego Guizzardi, Dirk Günther, Jean Matthieu Haussaire, Sander Houweling, Greet Janssens-Maenhout, Massaer Kouyate, Adrian Leip, Antti Leppänen, Emanuele Lugato, Manon Maisonnier, Alistair J. Manning, Tiina Markkanen, Joe McNorton, Marilena Muntean, Gabriel D. Oreggioni, Prabir K. Patra, Lucia Perugini, Isabelle Pison, Maarit T. Raivonen, Marielle Saunois, Arjo J. Segers, Pete Smith, Efisio Solazzo, Hanqin Tian, Francesco N. Tubiello, Timo Vesala, Guido R. Van Der Werf, Chris Wilson, Sönke Zaehle

^*Corresponding author for this work

Aberdeen Centre For Environmental Sustainability

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27ĝ€¯+ĝ€¯UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990-2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015-2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1 (EDGARv6.0, last year 2018) and 18.4ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH-HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1. For N2O emissions, over the 2015-2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9ĝ€¯Tgĝ€¯N2Oĝ€¯yr-1, close to the NGHGI data (0.8±55ĝ€¯%ĝ€¯Tgĝ€¯N2Oĝ€¯yr-1). Over the same period, the mean of TD global and regional inversions was 1.4ĝ€¯Tgĝ€¯N2Oĝ€¯yr-1 (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27ĝ€¯+ĝ€¯UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-Annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-Annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at 10.5281/zenodo.7553800 (Petrescu et al., 2023).

Original language	English
Pages (from-to)	1197-1268
Number of pages	72
Journal	Earth System Science Data
Volume	15
Issue number	3
DOIs	https://doi.org/10.5194/essd-15-1197-2023
Publication status	Published - 21 Mar 2023

Bibliographical note

Funding Information:
We thank Aurélie Paquirissamy, Géraud Moulas and the ARTTIC team for the great managerial support offered during the project. FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. Annual, gap-filled and harmonized NGHGI uncertainty estimates for the EU and its member states were provided by the EU GHG inventory team (European Environment Agency and its European Topic Centre on Climate change mitigation). Most top-down inverse simulations referred to in this paper rely for the derivation of optimized flux fields on observational data provided by surface stations that are part of networks like ICOS (datasets: 10.18160/P7E9-EKEA , Integrated Non-CO Observing System, 2018a, and 10.18160/B3Q6-JKA0 , Integrated Non-CO Observing System, 2018b), AGAGE, NOAA (Obspack Globalview CH: 10.25925/20221001 , Schuldt et al., 2017), CSIRO and/or WMO GAW. We thank all station PIs and their organizations for providing these valuable datasets. We acknowledge the work of other members of the EDGAR group (Edwin Schaaf, Jos Olivier) and the outstanding scientific contribution to the VERIFY project of Peter Bergamaschi. Timo Vesala thanks ICOS-Finland, University of Helsinki. The TM5-CAMS inversions are available from https://atmosphere.copernicus.eu (last access: June 2022); Arjo Segers acknowledges support from the Copernicus Atmosphere Monitoring Service, implemented by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission (grant no. CAMS2_55).

This research has been supported by the European Commission, Horizon 2020 Framework Programme (VERIFY, grant no. 776810).

Ronny Lauerwald received support from the CLand Convergence Institute. Prabir Patra received support from the Environment Research and Technology Development Fund (grant no. JPMEERF20182002) of the Environmental Restoration and Conservation Agency of Japan. Pierre Regnier received financial support from the H2020 project ESM2025 – Earth System Models for the Future (grant no. 101003536). David Basviken received support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (METLAKE, grant no. 725546). Greet Janssens-Maenhout received support from the European Union's Horizon 2020 research and innovation program (CoCO, grant no. 958927). Tuula Aalto received support from the Finnish Academy (grants nos. 351311 and 345531). Sönke Zhaele received support from the ERC consolidator grant QUINCY (grant no. 647204).

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Cite this

Petrescu, A. M. R., Qiu, C., McGrath, M. J., Peylin, P., Peters, G. P., Ciais, P., Thompson, R. L., Tsuruta, A., Brunner, D., Kuhnert, M., Matthews, B., Palmer, P. I., Tarasova, O., Regnier, P., Lauerwald, R., Bastviken, D., Höglund-Isaksson, L., Winiwarter, W., Etiope, G., ... Zaehle, S. (2023). The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990-2019. Earth System Science Data, 15(3), 1197-1268. https://doi.org/10.5194/essd-15-1197-2023

Petrescu, AMR, Qiu, C, McGrath, MJ, Peylin, P, Peters, GP, Ciais, P, Thompson, RL, Tsuruta, A, Brunner, D, Kuhnert, M, Matthews, B, Palmer, PI, Tarasova, O, Regnier, P, Lauerwald, R, Bastviken, D, Höglund-Isaksson, L, Winiwarter, W, Etiope, G, Aalto, T, Balsamo, G, Bastrikov, V, Berchet, A, Brockmann, P, Ciotoli, G, Conchedda, G, Crippa, M, Dentener, F, Groot Zwaaftink, CD, Guizzardi, D, Günther, D, Haussaire, JM, Houweling, S, Janssens-Maenhout, G, Kouyate, M, Leip, A, Leppänen, A, Lugato, E, Maisonnier, M, Manning, AJ, Markkanen, T, McNorton, J, Muntean, M, Oreggioni, GD, Patra, PK, Perugini, L, Pison, I, Raivonen, MT, Saunois, M, Segers, AJ, Smith, P, Solazzo, E, Tian, H, Tubiello, FN, Vesala, T, Van Der Werf, GR, Wilson, C & Zaehle, S 2023, 'The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990-2019', Earth System Science Data, vol. 15, no. 3, pp. 1197-1268. https://doi.org/10.5194/essd-15-1197-2023

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title = "The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990-2019",

abstract = "Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27ĝ€¯+ĝ€¯UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990-2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015-2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1 (EDGARv6.0, last year 2018) and 18.4ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH-HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1. For N2O emissions, over the 2015-2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9ĝ€¯Tgĝ€¯N2Oĝ€¯yr-1, close to the NGHGI data (0.8±55ĝ€¯%ĝ€¯Tgĝ€¯N2Oĝ€¯yr-1). Over the same period, the mean of TD global and regional inversions was 1.4ĝ€¯Tgĝ€¯N2Oĝ€¯yr-1 (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27ĝ€¯+ĝ€¯UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-Annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-Annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at 10.5281/zenodo.7553800 (Petrescu et al., 2023).",

author = "Petrescu, {Ana Maria Roxana} and Chunjing Qiu and McGrath, {Matthew J.} and Philippe Peylin and Peters, {Glen P.} and Philippe Ciais and Thompson, {Rona L.} and Aki Tsuruta and Dominik Brunner and Matthias Kuhnert and Bradley Matthews and Palmer, {Paul I.} and Oksana Tarasova and Pierre Regnier and Ronny Lauerwald and David Bastviken and Lena H{\"o}glund-Isaksson and Wilfried Winiwarter and Giuseppe Etiope and Tuula Aalto and Gianpaolo Balsamo and Vladislav Bastrikov and Antoine Berchet and Patrick Brockmann and Giancarlo Ciotoli and Giulia Conchedda and Monica Crippa and Frank Dentener and {Groot Zwaaftink}, {Christine D.} and Diego Guizzardi and Dirk G{\"u}nther and Haussaire, {Jean Matthieu} and Sander Houweling and Greet Janssens-Maenhout and Massaer Kouyate and Adrian Leip and Antti Lepp{\"a}nen and Emanuele Lugato and Manon Maisonnier and Manning, {Alistair J.} and Tiina Markkanen and Joe McNorton and Marilena Muntean and Oreggioni, {Gabriel D.} and Patra, {Prabir K.} and Lucia Perugini and Isabelle Pison and Raivonen, {Maarit T.} and Marielle Saunois and Segers, {Arjo J.} and Pete Smith and Efisio Solazzo and Hanqin Tian and Tubiello, {Francesco N.} and Timo Vesala and {Van Der Werf}, {Guido R.} and Chris Wilson and S{\"o}nke Zaehle",

note = "Funding Information: We thank Aur{\'e}lie Paquirissamy, G{\'e}raud Moulas and the ARTTIC team for the great managerial support offered during the project. FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. Annual, gap-filled and harmonized NGHGI uncertainty estimates for the EU and its member states were provided by the EU GHG inventory team (European Environment Agency and its European Topic Centre on Climate change mitigation). Most top-down inverse simulations referred to in this paper rely for the derivation of optimized flux fields on observational data provided by surface stations that are part of networks like ICOS (datasets: 10.18160/P7E9-EKEA , Integrated Non-CO Observing System, 2018a, and 10.18160/B3Q6-JKA0 , Integrated Non-CO Observing System, 2018b), AGAGE, NOAA (Obspack Globalview CH: 10.25925/20221001 , Schuldt et al., 2017), CSIRO and/or WMO GAW. We thank all station PIs and their organizations for providing these valuable datasets. We acknowledge the work of other members of the EDGAR group (Edwin Schaaf, Jos Olivier) and the outstanding scientific contribution to the VERIFY project of Peter Bergamaschi. Timo Vesala thanks ICOS-Finland, University of Helsinki. The TM5-CAMS inversions are available from https://atmosphere.copernicus.eu (last access: June 2022); Arjo Segers acknowledges support from the Copernicus Atmosphere Monitoring Service, implemented by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission (grant no. CAMS2_55). This research has been supported by the European Commission, Horizon 2020 Framework Programme (VERIFY, grant no. 776810). Ronny Lauerwald received support from the CLand Convergence Institute. Prabir Patra received support from the Environment Research and Technology Development Fund (grant no. JPMEERF20182002) of the Environmental Restoration and Conservation Agency of Japan. Pierre Regnier received financial support from the H2020 project ESM2025 – Earth System Models for the Future (grant no. 101003536). David Basviken received support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (METLAKE, grant no. 725546). Greet Janssens-Maenhout received support from the European Union's Horizon 2020 research and innovation program (CoCO, grant no. 958927). Tuula Aalto received support from the Finnish Academy (grants nos. 351311 and 345531). S{\"o}nke Zhaele received support from the ERC consolidator grant QUINCY (grant no. 647204). ",

year = "2023",

month = mar,

day = "21",

doi = "10.5194/essd-15-1197-2023",

language = "English",

volume = "15",

pages = "1197--1268",

journal = "Earth System Science Data",

issn = "1866-3508",

publisher = "Copernicus Publications",

number = "3",

}

TY - JOUR

T1 - The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom

T2 - 1990-2019

AU - Petrescu, Ana Maria Roxana

AU - Qiu, Chunjing

AU - McGrath, Matthew J.

AU - Peylin, Philippe

AU - Peters, Glen P.

AU - Ciais, Philippe

AU - Thompson, Rona L.

AU - Tsuruta, Aki

AU - Brunner, Dominik

AU - Kuhnert, Matthias

AU - Matthews, Bradley

AU - Palmer, Paul I.

AU - Tarasova, Oksana

AU - Regnier, Pierre

AU - Lauerwald, Ronny

AU - Bastviken, David

AU - Höglund-Isaksson, Lena

AU - Winiwarter, Wilfried

AU - Etiope, Giuseppe

AU - Aalto, Tuula

AU - Balsamo, Gianpaolo

AU - Bastrikov, Vladislav

AU - Berchet, Antoine

AU - Brockmann, Patrick

AU - Ciotoli, Giancarlo

AU - Conchedda, Giulia

AU - Crippa, Monica

AU - Dentener, Frank

AU - Groot Zwaaftink, Christine D.

AU - Guizzardi, Diego

AU - Günther, Dirk

AU - Haussaire, Jean Matthieu

AU - Houweling, Sander

AU - Janssens-Maenhout, Greet

AU - Kouyate, Massaer

AU - Leip, Adrian

AU - Leppänen, Antti

AU - Lugato, Emanuele

AU - Maisonnier, Manon

AU - Manning, Alistair J.

AU - Markkanen, Tiina

AU - McNorton, Joe

AU - Muntean, Marilena

AU - Oreggioni, Gabriel D.

AU - Patra, Prabir K.

AU - Perugini, Lucia

AU - Pison, Isabelle

AU - Raivonen, Maarit T.

AU - Saunois, Marielle

AU - Segers, Arjo J.

AU - Smith, Pete

AU - Solazzo, Efisio

AU - Tian, Hanqin

AU - Tubiello, Francesco N.

AU - Vesala, Timo

AU - Van Der Werf, Guido R.

AU - Wilson, Chris

AU - Zaehle, Sönke

N1 - Funding Information: We thank Aurélie Paquirissamy, Géraud Moulas and the ARTTIC team for the great managerial support offered during the project. FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. Annual, gap-filled and harmonized NGHGI uncertainty estimates for the EU and its member states were provided by the EU GHG inventory team (European Environment Agency and its European Topic Centre on Climate change mitigation). Most top-down inverse simulations referred to in this paper rely for the derivation of optimized flux fields on observational data provided by surface stations that are part of networks like ICOS (datasets: 10.18160/P7E9-EKEA , Integrated Non-CO Observing System, 2018a, and 10.18160/B3Q6-JKA0 , Integrated Non-CO Observing System, 2018b), AGAGE, NOAA (Obspack Globalview CH: 10.25925/20221001 , Schuldt et al., 2017), CSIRO and/or WMO GAW. We thank all station PIs and their organizations for providing these valuable datasets. We acknowledge the work of other members of the EDGAR group (Edwin Schaaf, Jos Olivier) and the outstanding scientific contribution to the VERIFY project of Peter Bergamaschi. Timo Vesala thanks ICOS-Finland, University of Helsinki. The TM5-CAMS inversions are available from https://atmosphere.copernicus.eu (last access: June 2022); Arjo Segers acknowledges support from the Copernicus Atmosphere Monitoring Service, implemented by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission (grant no. CAMS2_55). This research has been supported by the European Commission, Horizon 2020 Framework Programme (VERIFY, grant no. 776810). Ronny Lauerwald received support from the CLand Convergence Institute. Prabir Patra received support from the Environment Research and Technology Development Fund (grant no. JPMEERF20182002) of the Environmental Restoration and Conservation Agency of Japan. Pierre Regnier received financial support from the H2020 project ESM2025 – Earth System Models for the Future (grant no. 101003536). David Basviken received support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (METLAKE, grant no. 725546). Greet Janssens-Maenhout received support from the European Union's Horizon 2020 research and innovation program (CoCO, grant no. 958927). Tuula Aalto received support from the Finnish Academy (grants nos. 351311 and 345531). Sönke Zhaele received support from the ERC consolidator grant QUINCY (grant no. 647204).

PY - 2023/3/21

Y1 - 2023/3/21

N2 - Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27ĝ€¯+ĝ€¯UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990-2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015-2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1 (EDGARv6.0, last year 2018) and 18.4ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH-HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1. For N2O emissions, over the 2015-2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9ĝ€¯Tgĝ€¯N2Oĝ€¯yr-1, close to the NGHGI data (0.8±55ĝ€¯%ĝ€¯Tgĝ€¯N2Oĝ€¯yr-1). Over the same period, the mean of TD global and regional inversions was 1.4ĝ€¯Tgĝ€¯N2Oĝ€¯yr-1 (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27ĝ€¯+ĝ€¯UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-Annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-Annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at 10.5281/zenodo.7553800 (Petrescu et al., 2023).

AB - Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27ĝ€¯+ĝ€¯UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990-2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015-2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1 (EDGARv6.0, last year 2018) and 18.4ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH-HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8ĝ€¯Tgĝ€¯CH4ĝ€¯yr-1. For N2O emissions, over the 2015-2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9ĝ€¯Tgĝ€¯N2Oĝ€¯yr-1, close to the NGHGI data (0.8±55ĝ€¯%ĝ€¯Tgĝ€¯N2Oĝ€¯yr-1). Over the same period, the mean of TD global and regional inversions was 1.4ĝ€¯Tgĝ€¯N2Oĝ€¯yr-1 (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27ĝ€¯+ĝ€¯UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-Annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-Annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at 10.5281/zenodo.7553800 (Petrescu et al., 2023).

UR - http://www.scopus.com/inward/record.url?scp=85151406498&partnerID=8YFLogxK

U2 - 10.5194/essd-15-1197-2023

DO - 10.5194/essd-15-1197-2023

M3 - Article

AN - SCOPUS:85151406498

SN - 1866-3508

VL - 15

SP - 1197

EP - 1268

JO - Earth System Science Data

JF - Earth System Science Data

IS - 3

ER -

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990-2019

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