Global CO2 flux estimation using NISMON-CO2 and GOSAT for carbon cycle analysis improvement

Accurate estimation of carbon dioxide (CO₂) flux is essential for better understanding of the global carbon budget and its impact on climate changes, which would further suggest strategies for emission reduction. Bottom-up approaches, while fundamental, often face challenges in capturing the complexities of CO₂ fluxes due to uncertainties in emission inventories and limitations in representing spatio-temporal variability of CO₂ flux across diverse regions. In contrast, top-down methods, which combine simulations and observations with inverse modeling approach, offer powerful tools for dynamically constraining CO₂ flux estimates. In comaparison to limited in-situ observations, satellite provides broader spatial coverage and therefore it is expected to enhance the flux estimation. In this study, we apply an integrated flux inversion framework NISMON-CO₂ to a CO₂ inversion with column-averaged dry air mole fraction of CO₂ (XCO₂), stored in NIES Level 2 product from the Greenhouse gases Observing SATellite (GOSAT) measurements and assess general features of the inversion results by comparing with an already established surface in-situ data inversion. NISMON-CO₂ incorporates NICAM-TM (Nonhydrostatic ICosahedral Atmospheric Model-based Transport Model) for forward simulation, coupled with a 4DVar (four dimensional variational) data assimilation system for inverse computations. The 4DVar leverages the adjoint of NICAM-TM alongside the quasi-Newtonian optimization scheme. GOSAT, a Japanese satellite launched in 2009 by Japan Aerospace Exploration Agency (JAXA) in collaboration with the Ministry of the Environment (MOE) and the National Institute for Environmental Studies (NIES), provides high quality greenhouse gas mesurements from space to study their global distribution.

The prior flux data include four fluxes: fossil fuel emissions from GridFED (Gridded Fossil Emission Dataset), biomass burning emissions from Global Fire Emission Database (GFED), biospheric fluxes (gross primary production, respiration and land use change) from the Vegetation Integrative SImulator for Trace gases (VISIT) and air-sea exchange flux data from Japan Meteorological Agency (JMA). In this study, meteorological data that drive NICAM-TM is updated to the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) from JRA-55. Several numerical experiments are conducted for the period since April, 2009 till date to understand the performance of the inversion by analyzing the consistency of the resultant flux and concentration. This study illustrates the power of integrating satellite-derived products to provide comprehensive CO₂ flux estimates, significantly enhancing our understanding of CO₂ dynamics at global and regional scales.

Keywords: CO₂ flux estimation, GOSAT, XCO₂, 4DVar, NICAM, transport model, satellite data assimilation.