[A-AS04] Atmospheric Chemistry

[AAS04-P28] Global inverse analysis of CH₄ fluxes using NICAM-TM 4D-Var

*Yosuke Niwa^1,2, Akihiko Ito¹, Toshinobu Machida¹, Motoki Sasakawa¹, Yasunori Tohjima¹, Taku Umezawa¹, Shinji Morimoto³, Shuji Aoki³, Yousuke Sawa², Kazuhiro Tsuboi², Hidekazu Matsueda², Ed Dlugokencky⁴, Christina Harth⁵, Paul B Krummel⁶, Ray L Langenfelds⁶, Zoe M Loh⁶, Jens Mühle⁵, Simon O’Doherty⁷, Ronald G Prinn⁸, Michel Ramonet⁹, Peter Salameh⁵, Colm Sweeney⁴, Ray Weiss⁵, Dickon Young⁷, Simona Castaldi^10,11, Sergio Noce¹², Marielle Saunois⁹, Ann R Stavert¹³ (1.National Institute for Environmental Studies, 2.Meteorological Research Institute, 3.Tohoku University, 4.Earth System Research Laboratory, National Oceanic and Atmospheric Administration, 5.Scripps Institution of Oceanography, University of California, 6.Climate Science Centre, CSIRO Oceans and Atmosphere, 7.School of Chemistry, University of Bristol, 8.Massachusetts Institute of Technology, 9.Laboratoire des Sciences du Climat et de l’Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), 10.DISTABIF, Università degli studi della Campania Luigi Vanvitelli, 11.Department Landscape Design & Sustainable Ecosystems RUDN University, 12.IAFES, Euro-Mediterranean Center on Climate Change, 13.Global Carbon Project, CSIRO Oceans and Atmosphere)

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.


References
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.

Acknowledgement
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.