JpGU-AGU Joint Meeting 2020

Presentation information

[E] Poster

A (Atmospheric and Hydrospheric Sciences ) » A-CG Complex & General

[A-CG47] Global Carbon Cycle Observation and Analysis

convener:Kazuhito Ichii(Chiba University), Prabir Patra(Research Institute for Global Change, JAMSTEC), Forrest M. Hoffman(Oak Ridge National Laboratory), Makoto Saito(National Institute of Environmental Studies)

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

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

Keywords:carbon cycle, CO2, inverse analysis

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.