Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations

Rajesh Janardanan , Shamil Maksyutov (1) , Aki Tsuruta , Fenjuan Wang , Yogesh K Tiwari , Vinu Valsala (2) , Akihiko Ito (1) , Yukio Yoshida (1) , Johannes Kaiser (3) , Greet Janssens-Maenhout (4) , Mikhail Arshinov (5) , Motoki Sasakawa (1) , Yasunori Tohjima (1) , Douglas E J Worthy , Edward J Dlugokencky (6) , Michel Ramonet (7, 8) , Jgor Arduini , Jost V Lavric , Salvatore Piacentino (9) , Paul B Krummel , Ray Langenfelds (10) , Ivan Mammarella (11) , Tsuneo Matsunaga

1 NIES - National Institute for Environmental Studies
2 CCCR - Centre for Climate Change Research [Pune]
3 MPIC - Max Planck Institute for Chemistry
4 IES - JRC Institute for Environment and Sustainability
5 LTS - Laboratory of Theoretical Spectroscopy [Tomsk]
6 ESRL - NOAA Earth System Research Laboratory
7 LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette]
8 ICOS-RAMCES - ICOS-RAMCES
9 ENEA - Italian National agency for new technologies, Energy and sustainable economic development [Frascati]
10 Oceans and Atmosphere
11 Helsingin yliopisto = Helsingfors universitet = University of Helsinki

We employed a global high-resolution inverse model to optimize the CH 4 emission using Greenhouse gas Observing Satellite (GOSAT) and surface observation data for a period from 2011-2017 for the two main source categories of anthropogenic and natural emissions. We used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission and scaled them by country to match the national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using the Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass Remote Sens. 2020, 12, 375 2 of 24 burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimated a global total anthropogenic and natural methane emissions of 340.9 Tg CH 4 yr −1 and 232.5 Tg CH 4 yr −1 , respectively. Country-scale analysis of the estimated anthropogenic emissions showed that all the top-emitting countries showed differences with their respective inventories to be within the uncertainty range of the inventories, confirming that the posterior anthropogenic emissions did not deviate from nationally reported values. Large countries, such as China, Russia, and the United States, had the mean estimated emission of 45.7 ± 8.6, 31.9 ± 7.8, and 29.8 ± 7.8 Tg CH 4 yr −1 , respectively. For natural wetland emissions, we estimated large emissions for Brazil (39.8 ± 12.4 Tg CH 4 yr −1), the United States (25.9 ± 8.3 Tg CH 4 yr −1), Russia (13.2 ± 9.3 Tg CH 4 yr −1), India (12.3 ± 6.4 Tg CH 4 yr −1), and Canada (12.2 ± 5.1 Tg CH 4 yr −1). In both emission categories, the major emitting countries all had the model corrections to emissions within the uncertainty range of inventories. The advantages of the approach used in this study were: (1) use of high-resolution transport, useful for simulations near emission hotspots, (2) prior anthropogenic emissions adjusted to the UNFCCC reports, (3) combining surface and satellite observations, which improves the estimation of both natural and anthropogenic methane emissions over spatial scale of countries.

Mots clés: inverse model; GOSAT; methane emission; anthropogenic; UNFCCC; wetland

Domaines: Sciences de l'environnement; Océan, Atmosphère

Rajesh Janardanan, Shamil Maksyutov, Aki Tsuruta, Fenjuan Wang, Yogesh K Tiwari, et al.. Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations. Remote Sensing, 2020, 12 (3), pp.375. ⟨10.3390/rs12030375⟩. ⟨hal-02897101⟩