Using OCO-2 Observations to Constrain Regional CO2 Fluxes Estimated with the Vegetation, Photosynthesis and Respiration Model - Igor B. Konovalov; Nikolai A. Golovushkin; Evgeny A. Mareev (Remote Sensing)
A good quantitative knowledge of regional sources and sinks of atmospheric carbon dioxide (CO2) is essential for understanding the global carbon cycle. It is also a key prerequisite for elaborating cost-effective national strategies to achieve the goals of the Paris Agreement. However, available estimates of CO2 fluxes for many regions of the world remain uncertain, despite significant recent progress in the remote sensing of terrestrial vegetation and atmospheric CO2. In this study, we investigate the feasibility of inferring reliable regional estimates of the net ecosystem exchange (NEE) using column-averaged dry-air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations as constraints on parameters of the widely used Vegetation Photosynthesis and Respiration model (VPRM), which predicts ecosystem fluxes based on vegetation indices derived from multispectral satellite imagery. We developed a regional-scale inverse modeling system that applies a Bayesian variational optimization algorithm to optimize parameters of VPRM coupled to the CHIMERE chemistry transport model and which involves a preliminary transformation of the input XCO2 data that reduces the impact of the CHIMERE boundary conditions on inversion results. We investigated the potential of our inversion system by applying it to a European region (that includes, in particular, the EU countries and the UK) for the warm season (May–September) of 2021. The inversion of the OCO-2 observations resulted in a major (more than threefold) reduction of the prior uncertainty in the regional NEE estimate. The posterior NEE estimate agrees with independent estimates provided by the CarbonTracker Europe High-Resolution (CTE-HR) system and the ensemble of the v10 OCO-2 model intercomparison (MIP) global inversions. We also found that the inversion improves the agreement of our simulations of XCO2 with retrievals from the Total Carbon Column Observing Network (TCCON). Our sensitivity test experiments using synthetic XCO2 data indicate that the posterior NEE estimate would remain reliable even if the actual regional CO2 fluxes drastically differed from their prior values. Furthermore, the posterior NEE estimate is found to be robust to strong biases and random uncertainties in the CHIMERE boundary conditions. Overall, this study suggests that our approach offers a reliable and relatively simple way to derive robust estimates of CO2 ecosystem fluxes from satellite XCO2 observations while enhancing the applicability of VPRM in regions where eddy covariance measurements of CO2 fluxes are scarce.