[A-CG37] Global Carbon Cycle Observation and Analysis

[ACG37-P01] Multi-species inversion for better constraining Asian GHG emissions

*Yosuke Niwa¹ (1.National Institute for Environmental Studies)

Keywords:greenhouse gas, inverse analysis, atmospheric observation

The Asian region is the largest greenhouse gas (GHG) emitter in the world. Top-down inversion analsyis, which is based on atmospheric observations, is one prominent method to estimate spatio-temporal variations of GHG fluxes at the earth surface. In recent years, a number of atmospheric observations are becoming available around Asia and a variety of atmospheric constituents are measured as well as GHG. It is expected that those multi-species observations could provide additional information of each source/sink component contribution. In fact, previous studies based on atmospheric observations made use of those multiple-species observations in estimation of GHG fluxes; however, incorporating those data in a more systematic faction, i.e., using a model, is not a trivial task due to complicated modeling and inversion techniques are required. In this study, we try to develop a multi-species inversion system to efficiently incorporate such multiple observational constraints in estimation of GHG emissions. The inversion system is constructed based on NICAM-based Inverse Simulation for Monitoring carbon dioxde (CO₂) and methane (CH₄) (NISMON-CO₂/CH₄) by incorporating additional atmospheric constituents such as carbon monoxide and ethane. In the analysis of NISMON, the four-dimensional variational method is used to exploit observational information as much as possible and estimate high-resolution fluxes. The additional constraints are introduced by sharing a common factor applied to each shared flux component (e.g., fossil fuel use, biomass burnings etc.) in the variational method framework. In this study, an inversion experiment is performed with pseudo-observations to investigate performance of the developed system in estimation of Asian GHG emissions, especially focusing on its utility in separating contributions of different source/sink components.


Acknowledgement:
The inverse analyses and system development were performed on the supercomputer systems of National Institute for Environmental Studies (NEC SX-Aurora TSUBASA). This study is supported by the Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency of Japan (JPMEERF21S20810).