Articles | Volume 25, issue 23
https://doi.org/10.5194/acp-25-17187-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-17187-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Dec 2025
Research article |
| 01 Dec 2025
| 01 Dec 2025
German methane fluxes estimated top-down using ICON–ART – Part 2: Inversion results for 2021
Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany
Thomas Rösch
Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany
Diego Jiménez de la Cuesta Otero
Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany
Beatrice Ellerhoff
Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany
Buhalqem Mamtimin
Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany
Niklas Becker
Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany
Anne-Marlene Blechschmidt
Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany
Jochen Förstner
Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany
Andrea K. Kaiser-Weiss
CORRESPONDING AUTHOR
Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany
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Estimating emissions of greenhouse gases such as methane for individual countries is fundamental for climate mitigation policies. Numerical models can simulate how such emissions are transported to observation sites. Based on the observations, one can estimate the emissions. We describe how operational numerical weather prediction can help determine the model uncertainties and thereby improve the emission estimates. We test our system using simulated observations.
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Estimating emissions of greenhouse gases such as methane for individual countries is fundamental for climate mitigation policies. Numerical models can simulate how such emissions are transported to observation sites. Based on the observations, one can estimate the emissions. We describe how operational numerical weather prediction can help determine the model uncertainties and thereby improve the emission estimates. We test our system using simulated observations.
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A recent development in numerical simulations of the global atmosphere is the increase in horizontal resolution to grid spacings of a few kilometers. However, the vertical grid spacing of these models has not been reduced at the same rate as the horizontal grid spacing. Here, we assess the effects of much finer vertical grid spacings, in particular the impacts on cloud quantities and the atmospheric energy balance.
Axel Seifert, Vanessa Bachmann, Florian Filipitsch, Jochen Förstner, Christian M. Grams, Gholam Ali Hoshyaripour, Julian Quinting, Anika Rohde, Heike Vogel, Annette Wagner, and Bernhard Vogel
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We investigate how mineral dust can lead to the formation of cirrus clouds. Dusty cirrus clouds lead to a reduction in solar radiation at the surface and, hence, a reduced photovoltaic power generation. Current weather prediction systems are not able to predict this interaction between mineral dust and cirrus clouds. We have developed a new physical description of the formation of dusty cirrus clouds. Overall we can show a considerable improvement in the forecast quality of clouds and radiation.
Hengheng Zhang, Frank Wagner, Harald Saathoff, Heike Vogel, Gholam Ali Hoshyaripour, Vanessa Bachmann, Jochen Förstner, and Thomas Leisner
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-193, https://doi.org/10.5194/amt-2021-193, 2021
Revised manuscript not accepted
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The evolution and the properties of Saharan dust plume were characterized by LIDARs, a sun photometer, and a regional transport model. Comparison between LIDAR measurements, sun photometer and ICON-ART predictions shows a good agreement for dust arrival time, dust layer height, and dust structure but also that the model overestimates the backscatter coefficients by a factor of (2.2 ± 0.16) and underestimate aerosol optical depth by a factor of (1.5 ± 0.11).
Cited articles
Agustí-Panareda, A., Diamantakis, M., Massart, S., Chevallier, F., Muñoz-Sabater, J., Barré, J., Curcoll, R., Engelen, R., Langerock, B., Law, R. M., Loh, Z., Morguí, J. A., Parrington, M., Peuch, V.-H., Ramonet, M., Roehl, C., Vermeulen, A. T., Warneke, T., and Wunch, D.: Modelling CO2 weather – why horizontal resolution matters, Atmos. Chem. Phys., 19, 7347–7376, https://doi.org/10.5194/acp-19-7347-2019, 2019. a
Basu, S., Lan, X., Dlugokencky, E., Michel, S., Schwietzke, S., Miller, J. B., Bruhwiler, L., Oh, Y., Tans, P. P., Apadula, F., Gatti, L. V., Jordan, A., Necki, J., Sasakawa, M., Morimoto, S., Di Iorio, T., Lee, H., Arduini, J., and Manca, G.: Estimating emissions of methane consistent with atmospheric measurements of methane and δ13C of methane, Atmos. Chem. Phys., 22, 15351–15377, https://doi.org/10.5194/acp-22-15351-2022, 2022. a
Bergamaschi, P., Krol, M., Meirink, J. F., Dentener, F., Segers, A., van Aardenne, J., Monni, S., Vermeulen, A. T., Schmidt, M., Ramonet, M., Yver, C., Meinhardt, F., Nisbet, E. G., Fisher, R. E., O'Doherty, S., and Dlugokencky, E. J.: Inverse modeling of European CH4 emissions 2001–2006, J. Geophys. Res.-Atmos., 115, https://doi.org/10.1029/2010JD014180, 2010. a
Bergamaschi, P., Houweling, S., Segers, A., Krol, M., Frankenberg, C., Scheepmaker, R. A., Dlugokencky, E., Wofsy, S. C., Kort, E. A., Sweeney, C., Schuck, T., Brenninkmeijer, C., Chen, H., Beck, V., and Gerbig, C.: Atmospheric CH4 in the first decade of the 21st century: Inverse modeling analysis using SCIAMACHY satellite retrievals and NOAA surface measurements, J. Geophys. Res.-Atmos., 118, 7350–7369, https://doi.org/10.1002/jgrd.50480, 2013. a
Bergamaschi, P., Karstens, U., Manning, A. J., Saunois, M., Tsuruta, A., Berchet, A., Vermeulen, A. T., Arnold, T., Janssens-Maenhout, G., Hammer, S., Levin, I., Schmidt, M., Ramonet, M., Lopez, M., Lavric, J., Aalto, T., Chen, H., Feist, D. G., Gerbig, C., Haszpra, L., Hermansen, O., Manca, G., Moncrieff, J., Meinhardt, F., Necki, J., Galkowski, M., O'Doherty, S., Paramonova, N., Scheeren, H. A., Steinbacher, M., and Dlugokencky, E.: Inverse modelling of European CH4 emissions during 2006–2012 using different inverse models and reassessed atmospheric observations, Atmos. Chem. Phys., 18, 901–920, https://doi.org/10.5194/acp-18-901-2018, 2018. a, b
Bergamaschi, P., Segers, A., Brunner, D., Haussaire, J.-M., Henne, S., Ramonet, M., Arnold, T., Biermann, T., Chen, H., Conil, S., Delmotte, M., Forster, G., Frumau, A., Kubistin, D., Lan, X., Leuenberger, M., Lindauer, M., Lopez, M., Manca, G., Müller-Williams, J., O'Doherty, S., Scheeren, B., Steinbacher, M., Trisolino, P., Vítková, G., and Yver Kwok, C.: High-resolution inverse modelling of European CH4 emissions using the novel FLEXPART-COSMO TM5 4DVAR inverse modelling system, Atmos. Chem. Phys., 22, 13243–13268, https://doi.org/10.5194/acp-22-13243-2022, 2022. a, b, c, d, e
Bessagnet, B., Pirovano, G., Mircea, M., Cuvelier, C., Aulinger, A., Calori, G., Ciarelli, G., Manders, A., Stern, R., Tsyro, S., García Vivanco, M., Thunis, P., Pay, M.-T., Colette, A., Couvidat, F., Meleux, F., Rouïl, L., Ung, A., Aksoyoglu, S., Baldasano, J. M., Bieser, J., Briganti, G., Cappelletti, A., D'Isidoro, M., Finardi, S., Kranenburg, R., Silibello, C., Carnevale, C., Aas, W., Dupont, J.-C., Fagerli, H., Gonzalez, L., Menut, L., Prévôt, A. S. H., Roberts, P., and White, L.: Presentation of the EURODELTA III intercomparison exercise – evaluation of the chemistry transport models' performance on criteria pollutants and joint analysis with meteorology, Atmos. Chem. Phys., 16, 12667–12701, https://doi.org/10.5194/acp-16-12667-2016, 2016. a
Bruch, V., Rösch, T., Jiménez de la Cuesta Otero, D., Ellerhoff, B., Mamtimin, B., Becker, N., Blechschmidt, A.-M., Förstner, J., and Kaiser-Weiss, A. K.: German methane fluxes estimated top-down using ICON–ART – Part 1: Ensemble-enhanced scaling inversion, Atmos. Chem. Phys., 25, 17159–17185, https://doi.org/10.5194/acp-25-17159-2025, 2025a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Bruch, V., Rösch, T., Jiménez de la Cuesta Otero, D., Ellerhoff, B., Mamtimin, B., Becker, N., Blechschmidt, A.-M., Förstner, J., and Kaiser-Weiss, A. K.: German methane fluxes in 2021 estimated with an ensemble-enhanced scaling inversion based on the ICON–ART model, Zenodo [data set], https://doi.org/10.5281/zenodo.15083479, 2025b. a
Canepa, E. and Builtjes, P. J. H.: Thoughts on Earth System Modeling: From global to regional scale, Earth-Sci. Rev., 171, 456–462, https://doi.org/10.1016/j.earscirev.2017.06.017, 2017. a
Chandra, N., Patra, P. K., Fujita, R., Höglund-Isaksson, L., Umezawa, T., Goto, D., Morimoto, S., Vaughn, B. H., and Röckmann, T.: Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020, Commun. Earth Environ., 5, 1–15, https://doi.org/10.1038/s43247-024-01286-x, 2024. a
Chen, H. W., Zhang, F., Lauvaux, T., Davis, K. J., Feng, S., Butler, M. P., and Alley, R. B.: Characterization of Regional-Scale CO2 Transport Uncertainties in an Ensemble with Flow-Dependent Transport Errors, Geophys. Res. Lett., 46, 4049–4058, https://doi.org/10.1029/2018GL081341, 2019. a
Citepa: Format Secten, https://www.citepa.org/fr/secten/, (last access: 18 March 2025), 2024. a
Cusworth, D. H., Bloom, A. A., Ma, S., Miller, C. E., Bowman, K., Yin, Y., Maasakkers, J. D., Zhang, Y., Scarpelli, T. R., Qu, Z., Jacob, D. J., and Worden, J. R.: A Bayesian framework for deriving sector-based methane emissions from top-down fluxes, Commun. Earth Environ., 2, 1–8, https://doi.org/10.1038/s43247-021-00312-6, 2021. a
Deng, Z., Ciais, P., Tzompa-Sosa, Z. A., Saunois, M., Qiu, C., Tan, C., Sun, T., Ke, P., Cui, Y., Tanaka, K., Lin, X., Thompson, R. L., Tian, H., Yao, Y., Huang, Y., Lauerwald, R., Jain, A. K., Xu, X., Bastos, A., Sitch, S., Palmer, P. I., Lauvaux, T., d'Aspremont, A., Giron, C., Benoit, A., Poulter, B., Chang, J., Petrescu, A. M. R., Davis, S. J., Liu, Z., Grassi, G., Albergel, C., Tubiello, F. N., Perugini, L., Peters, W., and Chevallier, F.: Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions, Earth Syst. Sci. Data, 14, 1639–1675, https://doi.org/10.5194/essd-14-1639-2022, 2022. a
Department for Energy Security and Net Zero: Final UK greenhouse gas emissions national statistics: 1990 to 2022, https://www.gov.uk/government/statistics/final-uk-greenhouse-gas-emissions-national-statistics-1990-to-2022, (last access: 17 January 2025), 2024. a
East, J. D., Jacob, D. J., Balasus, N., Bloom, A. A., Bruhwiler, L., Chen, Z., Kaplan, J. O., Mickley, L. J., Mooring, T. A., Penn, E., Poulter, B., Sulprizio, M. P., Worden, J. R., Yantosca, R. M., and Zhang, Z.: Interpreting the Seasonality of Atmospheric Methane, Geophys. Res. Lett., 51, e2024GL108494, https://doi.org/10.1029/2024GL108494, 2024. a
Engelen, R. J., Denning, A. S., and Gurney, K. R.: On error estimation in atmospheric CO2 inversions, J. Geophys. Res.-Atmos., 107, ACL10-1–ACL10-13, https://doi.org/10.1029/2002JD002195, 2002. a
Estrada, L. A., Varon, D. J., Sulprizio, M., Nesser, H., Chen, Z., Balasus, N., Hancock, S. E., He, M., East, J. D., Mooring, T. A., Oort Alonso, A., Maasakkers, J. D., Aben, I., Baray, S., Bowman, K. W., Worden, J. R., Cardoso-Saldaña, F. J., Reidy, E., and Jacob, D. J.: Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations, Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, 2025. a, b
Ganesan, A. L., Manning, A. J., Grant, A., Young, D., Oram, D. E., Sturges, W. T., Moncrieff, J. B., and O'Doherty, S.: Quantifying methane and nitrous oxide emissions from the UK and Ireland using a national-scale monitoring network, Atmos. Chem. Phys., 15, 6393–6406, https://doi.org/10.5194/acp-15-6393-2015, 2015. a
Gerbig, C., Körner, S., and Lin, J. C.: Vertical mixing in atmospheric tracer transport models: error characterization and propagation, Atmos. Chem. Phys., 8, 591–602, https://doi.org/10.5194/acp-8-591-2008, 2008. a
Henne, S., Brunner, D., Oney, B., Leuenberger, M., Eugster, W., Bamberger, I., Meinhardt, F., Steinbacher, M., and Emmenegger, L.: Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling, Atmos. Chem. Phys., 16, 3683–3710, https://doi.org/10.5194/acp-16-3683-2016, 2016. a, b
ICOS RI: ICOS Handbook 2024, ICOS ERIC, https://doi.org/10.18160/28AV-80QR, 2024. a
ICOS RI, Bergamaschi, P., Colomb, A., De Mazière, M., Emmenegger, L., Kubistin, D., Lehner, I., Lehtinen, K., Leuenberger, M., Lund Myhre, C., Marek, M. V., O'Doherty, S., Platt, S. M., Plaß-Dülmer, C., Apadula, F., Arnold, S., Blanc, P.-E., Brunner, D., Chen, H., Chmura, L., Chmura, Ł., Conil, S., Couret, C., Cristofanelli, P., Forster, G., Frumau, A., Gerbig, C., Gheusi, F., Hammer, S., Haszpra, L., Hatakka, J., Heliasz, M., Henne, S., Hensen, A., Hoheisel, A., Kneuer, T., Larmanou, E., Laurila, T., Leskinen, A., Levin, I., Lindauer, M., Lopez, M., Mammarella, I., Manca, G., Manning, A., Martin, D., Meinhardt, F., Mölder, M., Müller-Williams, J., Noe, S. M., Nęcki, J., Ottosson-Löfvenius, M., Philippon, C., Pitt, J., Ramonet, M., Rivas-Soriano, P., Scheeren, B., Schumacher, M., Sha, M. K., Spain, G., Steinbacher, M., Sørensen, L. L., Vermeulen, A., Vítková, G., Xueref-Remy, I., di Sarra, A., Conen, F., Kazan, V., Roulet, Y.-A., Biermann, T., Delmotte, M., Heltai, D., Hermansen, O., Komínková, K., Laurent, O., Levula, J., Lunder, C., Marklund, P., Morguí, J.-A., Pichon, J.-M., Schmidt, M., Sferlazzo, D., Smith, P., Stanley, K., Trisolino, P., Zazzeri, G., ICOS Carbon Portal, ICOS Atmosphere Thematic Centre, ICOS Flask And Calibration Laboratory, and ICOS Central Radiocarbon Laboratory: European Obspack compilation of atmospheric methane data from ICOS and non-ICOS European stations for the period 1984–2024;
obspack_ch4_466_GVeu_v9.2_20240502, ICOS Data Portal [data set], https://doi.org/10.18160/9B66-SQM1, 2024. a
IPCC, Calvo Buendia, E., Tanabe, K., Kranjc, A., Baasansuren, J., Fukuda, M., Ngarize, S., Osako, A., Pyrozhenko, Y., Shermanau, P., and Federici, S., eds.: 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, vol. 1, The Intergovernmental Panel on Climate Change (IPCC), https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html (last access: 19 November 2025), 2019. a
Janssens-Maenhout, G., Pinty, B., Dowell, M., Zunker, H., Andersson, E., Balsamo, G., Bézy, J.-L., Brunhes, T., Bösch, H., Bojkov, B., Brunner, D., Buchwitz, M., Crisp, D., Ciais, P., Counet, P., Dee, D., van der Gon, H. D., Dolman, H., Drinkwater, M. R., Dubovik, O., Engelen, R., Fehr, T., Fernandez, V., Heimann, M., Holmlund, K., Houweling, S., Husband, R., Juvyns, O., Kentarchos, A., Landgraf, J., Lang, R., Löscher, A., Marshall, J., Meijer, Y., Nakajima, M., Palmer, P. I., Peylin, P., Rayner, P., Scholze, M., Sierk, B., Tamminen, J., and Veefkind, P.: Toward an Operational Anthropogenic CO2 Emissions Monitoring and Verification Support Capacity, Bull. Am. Meteorol. Soc., 101, E1439–E1451, https://doi.org/10.1175/BAMS-D-19-0017.1, 2020. a
Kaminski, T., Rayner, P. J., Heimann, M., and Enting, I. G.: On aggregation errors in atmospheric transport inversions, J. Geophys. Res.-Atmos., 106, 4703–4715, https://doi.org/10.1029/2000JD900581, 2001. a, b
Kuenen, J., Dellaert, S., Visschedijk, A., Jalkanen, J.-P., Super, I., and Denier van der Gon, H.: Copernicus Atmosphere Monitoring Service regional emissions version 4.2 (CAMS-REG-v4.2), Copernicus Atmosphere Monitoring Service (CAMS) [data set], https://doi.org/10.24380/0vzb-a387, 2021. a
Kuenen, J., Dellaert, S., Visschedijk, A., Jalkanen, J.-P., Super, I., and Denier van der Gon, H.: CAMS-REG-v4: a state-of-the-art high-resolution European emission inventory for air quality modelling, Earth Syst. Sci. Data, 14, 491–515, https://doi.org/10.5194/essd-14-491-2022, 2022. a
Logan, J. A., Prather, M. J., Wofsy, S. C., and McElroy, M. B.: Tropospheric chemistry: A global perspective, J. Geophys. Res.-Oceans, 86, 7210–7254, https://doi.org/10.1029/JC086iC08p07210, 1981. a
Manning, A. J., O'Doherty, S., Jones, A. R., Simmonds, P. G., and Derwent, R. G.: Estimating UK methane and nitrous oxide emissions from 1990 to 2007 using an inversion modeling approach, J. Geophys. Res.-Atmos., 116, https://doi.org/10.1029/2010JD014763, 2011. a
Mead, G. J., Herman, D. I., Giorgetta, F. R., Malarich, N. A., Baumann, E., Washburn, B. R., Newbury, N. R., Coddington, I., and Cossel, K. C.: Apportionment and Inventory Optimization of Agriculture and Energy Sector Methane Emissions Using Multi-Month Trace Gas Measurements in Northern Colorado, Geophys. Res. Lett., 51, e2023GL105973, https://doi.org/10.1029/2023GL105973, 2024. a
Meirink, J. F., Bergamaschi, P., Frankenberg, C., D'Amelio, M. T. S., Dlugokencky, E. J., Gatti, L. V., Houweling, S., Miller, J. B., Röckmann, T., Villani, M. G., and Krol, M. C.: Four-dimensional variational data assimilation for inverse modeling of atmospheric methane emissions: Analysis of SCIAMACHY observations, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD009740, 2008a. a
Meirink, J. F., Bergamaschi, P., and Krol, M. C.: Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: method and comparison with synthesis inversion, Atmos. Chem. Phys., 8, 6341–6353, https://doi.org/10.5194/acp-8-6341-2008, 2008b. a
Munassar, S., Monteil, G., Scholze, M., Karstens, U., Rödenbeck, C., Koch, F.-T., Totsche, K. U., and Gerbig, C.: Why do inverse models disagree? A case study with two European CO2 inversions, Atmos. Chem. Phys., 23, 2813–2828, https://doi.org/10.5194/acp-23-2813-2023, 2023. a
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., Aalto, T., Balsamo, G., Bastrikov, V., Berchet, A., Brockmann, P., Ciotoli, G., Conchedda, G., Crippa, M., Dentener, F., Groot Zwaaftink, C. D., Guizzardi, D., Günther, D., Haussaire, J.-M., Houweling, S., Janssens-Maenhout, G., Kouyate, M., Leip, A., Leppänen, A., Lugato, E., Maisonnier, M., Manning, A. J., Markkanen, T., McNorton, J., Muntean, M., Oreggioni, G. D., Patra, P. K., Perugini, L., Pison, I., Raivonen, M. T., Saunois, M., Segers, A. J., Smith, P., Solazzo, E., Tian, H., Tubiello, F. N., Vesala, T., van der Werf, G. R., Wilson, C., and Zaehle, S.: The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019, Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, 2023. a, b, c, d
Petrescu, A. M. R., Peters, G. P., Engelen, R., Houweling, S., Brunner, D., Tsuruta, A., Matthews, B., Patra, P. K., Belikov, D., Thompson, R. L., Höglund-Isaksson, L., Zhang, W., Segers, A. J., Etiope, G., Ciotoli, G., Peylin, P., Chevallier, F., Aalto, T., Andrew, R. M., Bastviken, D., Berchet, A., Broquet, G., Conchedda, G., Dellaert, S. N. C., Denier van der Gon, H., Gütschow, J., Haussaire, J.-M., Lauerwald, R., Markkanen, T., van Peet, J. C. A., Pison, I., Regnier, P., Solum, E., Scholze, M., Tenkanen, M., Tubiello, F. N., van der Werf, G. R., and Worden, J. R.: Comparison of observation- and inventory-based methane emissions for eight large global emitters, Earth Syst. Sci. Data, 16, 4325–4350, https://doi.org/10.5194/essd-16-4325-2024, 2024. a
Ramsden, A. E., Ganesan, A. L., Western, L. M., Rigby, M., Manning, A. J., Foulds, A., France, J. L., Barker, P., Levy, P., Say, D., Wisher, A., Arnold, T., Rennick, C., Stanley, K. M., Young, D., and O'Doherty, S.: Quantifying fossil fuel methane emissions using observations of atmospheric ethane and an uncertain emission ratio, Atmos. Chem. Phys., 22, 3911–3929, https://doi.org/10.5194/acp-22-3911-2022, 2022. a
Rieger, D., Bangert, M., Bischoff-Gauss, I., Förstner, J., Lundgren, K., Reinert, D., Schröter, J., Vogel, H., Zängl, G., Ruhnke, R., and Vogel, B.: ICON–ART 1.0 – a new online-coupled model system from the global to regional scale, Geosci. Model Dev., 8, 1659–1676, https://doi.org/10.5194/gmd-8-1659-2015, 2015. a, b
Rocher-Ros, G., Stanley, E. H., Loken, L. C., Casson, N. J., Raymond, P. A., Liu, S., Amatulli, G., and Sponseller, R. A.: Global methane emissions from rivers and streams, Nature, 621, 530–535, https://doi.org/10.1038/s41586-023-06344-6, 2023. a
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding, vol. 2 of Series on Atmospheric, Oceanic and Planetary Physics, World Scientific Publishing Company, Singapore, ISBN 978-981-02-2740-1, https://doi.org/10.1142/3171, 2000. a, b
Rödenbeck, C., Houweling, S., Gloor, M., and Heimann, M.: CO2 flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport, Atmos. Chem. Phys., 3, 1919–1964, https://doi.org/10.5194/acp-3-1919-2003, 2003. a
Schraff, C., Reich, H., Rhodin, A., Schomburg, A., Stephan, K., Periáñez, A., and Potthast, R.: Kilometre-scale ensemble data assimilation for the COSMO model (KENDA), Q. J. R. Meteorolog. Soc., 142, 1453–1472, https://doi.org/10.1002/qj.2748, 2016. a
Schröter, J., Rieger, D., Stassen, C., Vogel, H., Weimer, M., Werchner, S., Förstner, J., Prill, F., Reinert, D., Zängl, G., Giorgetta, M., Ruhnke, R., Vogel, B., and Braesicke, P.: ICON-ART 2.1: a flexible tracer framework and its application for composition studies in numerical weather forecasting and climate simulations, Geosci. Model Dev., 11, 4043–4068, https://doi.org/10.5194/gmd-11-4043-2018, 2018. a, b
Segers, A. and Houweling, S.: CAMS global inversion-optimised greenhouse gas fluxes and concentrations, v22r2, Copernicus Atmosphere Monitoring Service [data set], https://doi.org/10.24381/ed2851d2, (last access: 18 April 2024), 2020. a, b
Seidel, D. J., Zhang, Y., Beljaars, A., Golaz, J.-C., Jacobson, A. R., and Medeiros, B.: Climatology of the planetary boundary layer over the continental United States and Europe, J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2012JD018143, 2012. a
Steiner, M., Cantarello, L., Henne, S., and Brunner, D.: Flow-dependent observation errors for greenhouse gas inversions in an ensemble Kalman smoother, Atmos. Chem. Phys., 24, 12447–12463, https://doi.org/10.5194/acp-24-12447-2024, 2024a. a
Tenkanen, M. K., Tsuruta, A., Denier van der Gon, H., Höglund-Isaksson, L., Leppänen, A., Markkanen, T., Petrescu, A. M. R., Raivonen, M., Aaltonen, H., and Aalto, T.: Partitioning anthropogenic and natural methane emissions in Finland during 2000–2021 by combining bottom-up and top-down estimates, Atmos. Chem. Phys., 25, 2181–2206, https://doi.org/10.5194/acp-25-2181-2025, 2025. a, b
Thanwerdas, J., Saunois, M., Berchet, A., Pison, I., and Bousquet, P.: Investigation of the renewed methane growth post-2007 with high-resolution 3-D variational inverse modeling and isotopic constraints, Atmos. Chem. Phys., 24, 2129–2167, https://doi.org/10.5194/acp-24-2129-2024, 2024. a
Thompson, R. L., Krishnankutty, N., Pisso, I., Schneider, P., Stebel, K., Sasakawa, M., Stohl, A., and Platt, S. M.: Efficient use of a Lagrangian particle dispersion model for atmospheric inversions using satellite observations of column mixing ratios, Atmos. Chem. Phys., 25, 12737–12751, https://doi.org/10.5194/acp-25-12737-2025, 2025. a
UNFCCC: National Inventory Submissions 2024, https://unfccc.int/ghg-inventories-annex-i-parties/2024, (last access: 18 March 2025), 2024. a
van der Laan-Luijkx, I. T., van der Velde, I. R., van der Veen, E., Tsuruta, A., Stanislawska, K., Babenhauserheide, A., Zhang, H. F., Liu, Y., He, W., Chen, H., Masarie, K. A., Krol, M. C., and Peters, W.: The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: implementation and global carbon balance 2001–2015, Geosci. Model Dev., 10, 2785–2800, https://doi.org/10.5194/gmd-10-2785-2017, 2017. a
Varon, D. J., Jacob, D. J., Sulprizio, M., Estrada, L. A., Downs, W. B., Shen, L., Hancock, S. E., Nesser, H., Qu, Z., Penn, E., Chen, Z., Lu, X., Lorente, A., Tewari, A., and Randles, C. A.: Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations, Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, 2022. a, b
Weber, T., Wiseman, N. A., and Kock, A.: Global ocean methane emissions dominated by shallow coastal waters, Nat. Commun., 10, 1–10, https://doi.org/10.1038/s41467-019-12541-7, 2019. a
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core, Quart. J. Roy. Meteorol. Soc., 141, 563–579, https://doi.org/10.1002/qj.2378, 2015. a, b
Short summary
Estimating emissions of greenhouse gases such as methane for individual countries is fundamental for climate mitigation policies. We use a numerical weather model to simulate how methane emissions are transported to observation sites. Based on the observations, we estimate the emissions in Central Europe in 2021. For Germany and the Benelux, we find higher emissions than expected from the national emission reporting. We provide sector-specific estimates to support national emission reporting.
Estimating emissions of greenhouse gases such as methane for individual countries is fundamental...
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