资源环境科技发展态势分析平台

Continuous efforts have been made to monitor atmospheric CO2 mole fractions as it is one of the most influential greenhouse gases in Earth's atmosphere. The atmospheric CO2 mole fractions are mostly determined by CO2 exchanges at the Earth's surface (i.e., surface CO2 flux). Inverse modeling, which is a method to estimate the CO2 exchanges at the Earth's surface, derives surface CO2 fluxes using modeled and observed atmospheric CO2 mole fraction data. Although observation data are crucial for successful modeling, comparatively fewer in situ observation sites are located in Asia compared to Europe or North America. Based on the importance of the terrestrial ecosystem of Asia for global carbon exchanges, more observation stations and an effective observation network design are required. In this paper, several observation network experiments were conducted to optimize the surface CO2 flux of Asia using CarbonTracker and observation system simulation experiments (OSSEs). The impacts of the redistribution of and additions to the existing observation network of Asia were evaluated using hypothetical in situ observation sites. In the case of the addition experiments, 10 observation stations, which is a practical number for real implementation, were added through three strategies: random addition, the influence matrix (i.e., self-sensitivity), and ecoregion information within the model. The simulated surface CO2 flux in Asia in summer can be improved by redistributing the existing observation network. The addition experiments revealed that considering both the distribution of normalized self-sensitivity and ecoregion information can yield better simulated surface CO2 fluxes compared to random addition, regardless of the season. This study provides a diagnosis of the existing ob- servation network and useful information for future observation network design in Asia to estimate the surface CO2 flux and also suggests the use of an influence matrix for designing CO2 observation networks. Unlike other previous observation network studies with many numerical experiments for optimization, comparatively fewer experiments were required in this study. Thus, the methodology used in this study may be used for designing observation networks for monitoring greenhouse gases at both continental and global scales.