[A-CG47] Global Carbon Cycle Observation and Analysis

[ACG47-P03] Toward a long-term global inversion of atmospheric CO₂ for elucidating seasonal and interannual variations of natural carbon fluxes

*丹羽 洋介¹ (1.国立環境研究所)

キーワード：炭素循環、二酸化炭素、逆解析

A number of ground-based stations have been performing atmospheric measurements of CO₂ for several decades and have observed notable interannual variations of the growth rate of the atmospheric CO₂, which suggests that the natural CO₂ flux at the earth surface have been varying in conjunction with some climate key parameters (e.g., El Niño Southern Oscillation index). Furthermore, distinct seasonal variations of atmospheric CO₂ observed by those measurements give evidence of dynamic photosynthesis and respiration activities of terrestrial biospheres. However, the mechanisms underlying those natural CO₂ flux variations are not fully understood and that gives significant uncertainties in the global warming prediction by earth system models equipped with carbon cycle feedbacks. An atmospheric observation-based estimate of surface CO₂ fluxes, i.e., so-called top-down analysis, would improve our understanding of the surface CO₂ flux mechanisms. An atmospheric inverse analysis in which a transport model is used to connect surface fluxes with atmospheric mole fractions is a prominent method in that it gives spatiotemporal variations of surface fluxes. In order to give implications of surface flux mechanisms, we should conduct a long-term inverse analysis with a high spatial resolution. In this study, we first use a simple box model that emulates integration of the global atmospheric CO₂ and apply it to a long-term inverse analysis with a variational method. In that experiment, we investigate how surface flux estimation is optimized along iterative calculations. Furthermore, we extend the inverse analysis to a more realistic problem with a sophisticated inverse system name NICAM-TM 4D-Var (Niwa et al., 2017a,b), in which a global three-dimensional model is employed. By using synthetic data with a current observational network, we elucidate uncertainties behind coming inversion results from NICAM-TM 4D-Var with real observations.



Niwa, Y., H. Tomita, M. Satoh, R. Imasu, Y. Sawa, K. Tsuboi, H. Matsueda, T. Machida, M. Sasakawa, B. Belan, and N. Saigusa: A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 1: Offline forward and adjoint transport models, Geosci. Model Dev., 10, 1157–1174, doi:10.5194/gmd-10-1157-2017, 2017a.

Niwa, Y., Y. Fujii, Y. Sawa, Y. Iida, A. Ito, M. Satoh, R. Imasu, K. Tsuboi, H. Matsueda, and N. Saigusa: A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 2: Optimization scheme and identical twin experiment of atmospheric CO₂ inversion, Geosci. Model Dev., 10, 2201-2219, doi:10.5194/gmd-10-2201-2017, 2017b.