Optimal design of surface CO2 observation network to constrain China's land carbon sink

Accurate estimate of the size of land carbon sink is essential for guiding climate mitigation actions to ful-fill China's net-zero ambitions before 2060. The atmospheric inversion is an effective approach to provide spatially explicit estimate of surface CO2 fluxes that are optimally consistent with atmospheric CO2 mea-surements. But atmospheric inversion of China's land carbon sink has enormous uncertainties, with one major source arising from the poor coverage of CO2 observation stations. Here we use a regional atmo-spheric inversion framework to design an observation network that could minimize uncertainties in inverted estimate of China's land carbon sink. Compared with the large spread of inverted sink (-1 Pg C a-1) from state-of-the-art inversions using existing CO2 observations, the uncertainty is con-strained within 0.3 Pg C a-1 when a total of 30 stations were deployed, and is further reduced to approx-imately 0.2 Pg C a-1 when 60 stations were deployed. The proposed stations are mostly distributed over areas with high biosphere productivity during the growing season, such as Southeast China, Northeast China, North China, and the Tibetan Plateau. Moreover, the proposed stations can cover areas where existing satellites have limited coverage due to cloud shadowing in the monsoon season or over complex topography. Such ground-based observation network will be a critical component in the future integrated observing system for monitoring China's land carbon fluxes.& COPY; 2023 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved.

Automated summary

Accurate estimation of China's land carbon sink is crucial for achieving net-zero goals before 2060. The atmospheric inversion method has uncertainties in estimating China's land carbon sink, largely due to inadequate CO2 observation station coverage. In this study, a regional atmospheric inversion framework is used to design an observation network that minimizes uncertainties in estimating China's land carbon sink.