Mapping Global Atmospheric CO₂ Concentration at High Spatiotemporal Resolution

Satellite measurements of the spatiotemporal distributions of atmospheric CO₂ concentrations are a key component for better understanding global carbon cycle characteristics. Currently, several satellite instruments such as the Greenhouse gases Observing SATellite (GOSAT), SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY), and Orbiting Carbon Observatory-2 can be used to measure CO₂ column-averaged dry air mole fractions. However, because of cloud effects, a single satellite can only provide limited CO₂ data, resulting in significant uncertainty in the characterization of the spatiotemporal distribution of atmospheric CO₂ concentrations. In this study, a new physical data fusion technique is proposed to combine the GOSAT and SCIAMACHY measurements. On the basis of the fused dataset, a gap-filling method developed by modeling the spatial correlation structures of CO₂ concentrations is presented with the goal of generating global land CO₂ distribution maps with high spatiotemporal resolution. The results show that, compared with the single satellite dataset (i.e., GOSAT or SCIAMACHY), the global spatial coverage of the fused dataset is significantly increased (reaching up to approximately 20%), and the temporal resolution is improved by two or three times. The spatial coverage and monthly variations of the generated global CO₂ distributions are also investigated. Comparisons with ground-based Total Carbon Column Observing Network (TCCON) measurements reveal that CO₂ distributions based on the gap-filling method show good agreement with TCCON records despite some biases. These results demonstrate that the fused dataset as well as the gap-filling method are rather effective to generate global CO₂ distribution with high accuracies and high spatiotemporal resolution.