Keywords:methane, inverse analysis
Atmospheric methane (CH4) is the most important greenhouse gas after carbon dioxide. Because CH4 has a relatively short lifetime due to chemical losses in the atmosphere, it is expected that reducing CH4 emissions would mitigate global warming in a relatively short timeframe. However, sources of atmospheric CH4 are associated with a wide variety of processes such as fossil fuel production and consumption, agriculture, natural wetlands, and biomass burning, and our understanding of the full CH4 budget is limited. To better understand CH4 sources, an inverse analysis is one prominent methodology that estimates spatiotemporal variations of CH4 sources consistent with their prior estimates and atmospheric observations within specified uncertainties. In this study, we performed a long-term inverse analysis of CH4 fluxes with an inversion system named NICAM-TM 4D-Var (Niwa et al., 2017a,b). The inversion system is based on the atmospheric transport model NICAM-TM (Niwa et al., 2011), which has a homogeneous icosahedral grid system and mass conserving property. The horizontal model grid resolution was set to 223 km and the CH4 flux estimation was performed at the same resolution, though some spatial error correlations were introduced. The prior flux dataset includes wetland/rice cultivation emission and soil uptake estimated by the terrestrial ecosystem model VISIT (Ito and Inatomi, 2012). The other emission categories are provided from the ongoing Global Carbon Project (GCP)–CH4. In the inversion, several emission categories are separately estimated according to their seasonal and interannual variabilities. Compared with the prior estimates, the inverse analysis with ground-based station data estimated smaller emissions from East Asia and Europe, larger and smaller northern summer emissions from West Siberia and Hudson Bay Lowlands, respectively, and larger emissions from Bengal and Indochina areas. These changes estimated by the inversion are attributed to emissions from anthropogenic categories (mainly fossil fuel related), natural wetlands, and rice cultivation, respectively. The presentation will also address the reliability of the inversion estimates using independent aircraft data and examine the independence of each category emission estimate.
Niwa et al. (2011), Journal of the Meteorological Society of Japan. Ser. II, 89(3), 255–268.
Ito and Inatomi (2012), Biogeoscience, 9:759-773.
Niwa et al. (2017a), Geoscientific Model Development, 10(3), 1157–1174.
Niwa et al. (2017b), Geoscientific Model Development, 10(6), 2201–2219.
This study is supported by the Environment Research and Technology Development Fund (2-1701) of the Ministry of the Environment, Japan. The authors acknowledge the Global Carbon Project–CH4 for providing the prior flux dataset, some of which is made under the support of the RUDN project "5-100". The authors also acknowledge Doug Worthy of Environmental Canada, Juha Hatakka of Finnish Meteorological Institute, Camille Yver Kwok of Laboratoire des Sciences du Climat et de l’Environnement, Sebastien Conil of Andra, and Kazuyuki Saito and other members of Japan Meteorological Agency for providing observational data of atmospheric CH4. This study used observational data of Advanced Global Atmospheric Gases Experiment (AGAGE). Operations of the AGAGE network are principally supported by the NASA Upper Atmospheric Research Program in the US, by the Department for Business, Energy & Industrial Strategy (BEIS) in the UK, by the Commonwealth Scientific and Industrial Research Organization (CSIRO) and the Bureau of Meteorology (Australia) in Australia, with additional support from the National Oceanographic and Atmospheric Administration (NOAA) in the US. Yosuke Niwa sincerely thanks the members of the NICAM team for developing and maintaining the codes of NICAM.