Preprints
https://doi.org/10.5194/essd-2022-287
https://doi.org/10.5194/essd-2022-287
 
02 Sep 2022
02 Sep 2022
Status: this preprint is currently under review for the journal ESSD.

The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK: 1990–2020

Ana Maria Roxana Petrescu1, Chunjing Qiu2, Matthew J. McGrath2, Philippe Peylin2, Glen P. Peters3, Philippe Ciais2, Rona L. Thompson4, Aki Tsuruta5, Dominik Brunner6, Matthias Kuhnert7, Bradley Matthews8, Paul I. Palmer9, Oksana Tarasova10, Pierre Regnier11, Ronny Lauerwald12, David Bastviken13, Lena Höglund-Isaksson14, Wilfried Winiwarter14,15, Giuseppe Etiope16, Tuula Aalto5, Gianpaolo Balsamo17, Vladislav Bastrikov18, Antoine Berchet2, Patrick Brockmann2, Giancarlo Ciotoli19, Giulia Conchedda20, Monica Crippa21, Frank Dentener21, Christine D. Groot Zwaaftink4, Diego Guizzardi21, Dirk Günther22, Jean-Matthieu Haussaire6, Sander Houweling1, Greet Janssens-Maenhout21, Massaer Kouyate2, Adrian Leip21,a, Antti Leppänen23, Emanuele Lugato21, Manon Maisonnier11, Alistair J. Manning24, Tiina Markkanen5, Joe McNorton17, Marilena Muntean21, Gabriel D. Oreggionib, Prabir K. Patra25, Lucia Perugini26, Isabelle Pison2, Maarit T. Raivonen23, Marielle Saunois2, Arjo J. Segers27, Pete Smith7, Efisio Solazzob, Hanqin Tian28, Francesco N. Tubiello20, Timo Vesala23,29, Chris Wilson30,31, and Sönke Zaehle32 Ana Maria Roxana Petrescu et al.
  • 1Department of Earth Sciences, Vrije Universiteit Amsterdam, 1081HV, Amsterdam, the Netherlands
  • 2Laboratoire des Sciences du Climat et de l’Environnement, 91190 Gif-sur-Yvette, France
  • 3CICERO Center for International Climate Research, Oslo, Norway
  • 4Norwegian Institute for Air Research (NILU), Kjeller, Norway
  • 5Finnish Meteorological Institute, P. O. Box 503, FI-00101 Helsinki, Finland
  • 6Empa, Swiss Federal Laboratories for Materials Science and Technology, 8600 Dübendorf, Switzerland
  • 7Institute of Biological and Environmental Sciences, University of Aberdeen, 23 St Machar Drive, Aberdeen, AB24 3UU, UK
  • 8Umweltbundesamt GmbH, Climate change mitigation & emission inventories, 1090, Vienna, Austria
  • 9School of GeoSciences, University of Edinburgh, Edinburgh, UK
  • 10Science and Innovation Department, World Meteorological Organization (WMO), Geneva, Switzerland
  • 11Biogeochemistry and Modeling of the Earth System, Université Libre de Bruxelles, 1050 Bruxelles, Belgium
  • 12Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, Thiverval-Grignon, France
  • 13Department of Thematic Studies – Environmental Change, Linköping University, Sweden
  • 14International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
  • 15Institute of Environmental Engineering, University of Zielona Góra, Zielona Góra, 65-417, Poland
  • 16Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma 2, via V. Murata 605, Roma, Italy
  • 17European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, RG2 9AX, UK
  • 18Science Partners, 75010 Paris, France
  • 19Consiglio Nazionale delle Ricerche, Istituto di Geologia Ambientale e Geoingegneria, Via Salaria km 29300, 00015 Monterotondo, Rome, Italy
  • 20Food and Agriculture Organization of the United Nations, Statistics Division, 00153 Rome, Italy
  • 21European Commission, Joint Research Centre, 21027 Ispra (Va), Italy
  • 22Umweltbundesamt (UBA), 14193 Berlin, Germany
  • 23University of Helsinki, Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, 00560 Helsinki, Finland
  • 24Hadley Centre, Met Office, Exeter, EX1 3PB, UK
  • 25Research Institute for Global Change, JAMSTEC, Yokohama 2360001, Japan
  • 26Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Viterbo, Italy
  • 27Department of Climate, Air and Sustainability, TNO, Princetonlaan 6, 3584 CB Utrecht, the Netherlands
  • 28International Centre for Climate and Global Change, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849, USA
  • 29Institute for Atmospheric and Earth System Research,/Forest Sciences, Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland
  • 30Institute for Climate and Atmospheric Science, University of Leeds, Leeds, UK
  • 31National Centre for Earth Observation, University of Leeds, Leeds, UK
  • 32Max Planck Institute for Biogeochemistry (MPI-BGC), Jena, Germany
  • anot at: European Commission, DG Research and Innovation, 1050 Brussels, Belgium
  • bformerly at: European Commission, Joint Research Centre, 21027 Ispra (Va), Italy

Abstract. Knowledge of the spatial distribution of the fluxes of greenhouse gases 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 (EU27+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, inverse modelling estimates, and extends the previous period 1990–2017 to 2020. BU and TD products are compared with European National GHG Inventories (NGHGI) reported by Parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. The uncertainties of NGHGIs were evaluated using the standard deviation obtained by varying parameters of inventory calculations, 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), 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 20.5 Tg CH4 yr−1 (EDGAR v5v6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, 2015), close to the NGHGI estimates of 17.5 ± 2.1 Tg CH4 yr−1. TD inversions 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 34 Tg CH4 yr−1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 Tg CH4 yr−1 and 24 Tg CH4 yr−1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soils emissions from the JSBACH-HIMMELI model, natural rivers, lakes and reservoirs emissions, geological sources and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH4 yr−1. For N2O emissions, over the 2015–2019 period, both BU products (EDGAR v5v6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N2O yr−1, close to the NGHGI data (0.8 ± 55 % Tg N2O yr−1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N2O 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 EU27+UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, of great importance for CH4 and N2O, which may help identify sector contributions to divergence between prior and posterior estimates at the annual/inter-annual scale. Even if currently comparison between CH4 and N2O inversions 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 modelling and observations, as well as modelling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emissions inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.6992472 (Petrescu et al., 2022).

Ana Maria Roxana Petrescu et al.

Status: open (until 28 Oct 2022)

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Ana Maria Roxana Petrescu et al.

Data sets

The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK: 1990-2020 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, Chris Wilson, and Sönke Zaehle https://doi.org/10.5281/zenodo.6992472

Ana Maria Roxana Petrescu et al.

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Short summary
This study updates the state-of-the-art scientific overview of data availability from bottom-up and top-down CH4 and N2O emissions in the EU27 and UK synthesised in Petrescu et al. (2021a). The data integrates the most recent emission inventories with process-based model data and regional/global inversions for the European domain, aiming at reconciling them with official country-level UNFCCC national GHG inventories in support to policy and to facilitate real-time verification procedures.