Published January 25, 2018
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Inverse modelling of European CH<sub>4</sub> emissions during 2006–2012 using different inverse models and reassessed atmospheric observations
Creators
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Bergamaschi, P.1
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Karstens, U.2
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Karstens, U.3
- Manning, A. J.4
- Saunois, M.5
- Tsuruta, A.6
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Berchet, A.5
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Berchet, A.7
- Vermeulen, A. T.3
- Vermeulen, A. T.8
- Arnold, T.4
- Arnold, T.9
- Arnold, T.10
- Janssens-Maenhout, G.1
- Hammer, S.11
- Levin, I.11
- Schmidt, M.11
- Ramonet, M.5
- Lopez, M.5
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Lavric, J.2
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Aalto, T.6
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Chen, H.12
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Chen, H.13
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Feist, D. G.2
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Gerbig, C.2
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Haszpra, L.14
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Haszpra, L.15
- Hermansen, O.16
- Manca, G.1
- Moncrieff, J.10
- Meinhardt, F.17
- Necki, J.18
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Galkowski, M.18
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O'Doherty, S.19
- Paramonova, N.20
- Scheeren, H. A.12
- Steinbacher, M.7
- Dlugokencky, E.21
- 1. European Commission Joint Research Centre, Ispra (Va), Italy
- 2. Max Planck Institute for Biogeochemistry, Jena, Germany
- 3. ICOS Carbon Portal, ICOS ERIC, University of Lund, Lund, Sweden
- 4. Met Office Exeter, Devon, UK
- 5. Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL), CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
- 6. Finnish Meteorological Institute (FMI), Helsinki, Finland
- 7. Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
- 8. Energy research Centre of the Netherlands (ECN), Petten, the Netherlands
- 9. National Physical Laboratory, Teddington, Middlesex, TW11 0LW, UK
- 10. School of GeoSciences, The University of Edinburgh, Edinburgh, EH9 3FF, UK
- 11. Institut für Umweltphysik, Heidelberg University, Heidelberg, Germany
- 12. Center for Isotope Research (CIO), University of Groningen, Groningen, the Netherlands
- 13. Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO, USA
- 14. Hungarian Meteorological Service, Budapest, Hungary
- 15. Research Centre for Astronomy and Earth Sciences, Geodetic and Geophysical Institute, Sopron, Hungary
- 16. Norwegian Institute for Air Research (NILU), Kjeller, Norway
- 17. Umweltbundesamt, Messstelle Schauinsland, Kirchzarten, Germany
- 18. AGH University of Science and Technology, Krakow, Poland
- 19. Atmospheric Chemistry Research Group, University of Bristol, Bristol, UK
- 20. Voeikov Main Geophysical Observatory, St. Petersburg, Russia
- 21. NOAA Earth System Research Laboratory, Global Monitoring Division, Boulder, CO, USA
Description
We present inverse modelling (top down) estimates of European methane
(CH4) emissions for 2006–2012 based on a new quality-controlled and
harmonised in situ data set from 18 European atmospheric monitoring stations.
We applied an ensemble of seven inverse models and performed four inversion
experiments, investigating the impact of different sets of stations and the
use of a priori information on emissions.
The inverse models infer total CH4 emissions of 26.8 (20.2–29.7) Tg CH4 yr−1 (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006–2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom up, based on statistical data and emissions factors) amount to only 21.3 Tg CH4 yr−1 (2006) to 18.8 Tg CH4 yr−1 (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), total wetland emissions of 4.3 (2.3–8.2) Tg CH4 yr−1 from the EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Taking into account the wetland emissions from the WETCHIMP ensemble, the top-down estimates are broadly consistent with the sum of anthropogenic and natural bottom-up inventories. However, the contribution of natural sources and their regional distribution remain rather uncertain.
Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon Cycle (IMECC) aircraft campaign. We present a novel approach to estimate the biases in the derived emissions, based on the comparison of simulated and measured enhancements of CH4 compared to the background, integrated over the entire boundary layer and over the lower troposphere. The estimated average regional biases range between −40 and 20 % at the aircraft profile sites in France, Hungary and Poland.
The inverse models infer total CH4 emissions of 26.8 (20.2–29.7) Tg CH4 yr−1 (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006–2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom up, based on statistical data and emissions factors) amount to only 21.3 Tg CH4 yr−1 (2006) to 18.8 Tg CH4 yr−1 (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), total wetland emissions of 4.3 (2.3–8.2) Tg CH4 yr−1 from the EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Taking into account the wetland emissions from the WETCHIMP ensemble, the top-down estimates are broadly consistent with the sum of anthropogenic and natural bottom-up inventories. However, the contribution of natural sources and their regional distribution remain rather uncertain.
Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon Cycle (IMECC) aircraft campaign. We present a novel approach to estimate the biases in the derived emissions, based on the comparison of simulated and measured enhancements of CH4 compared to the background, integrated over the entire boundary layer and over the lower troposphere. The estimated average regional biases range between −40 and 20 % at the aircraft profile sites in France, Hungary and Poland.
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