Improving the Accuracy of CO₂ Emission Estimates over South Korea Using a Top-down Inversion Framework

The international community has continuously monitored carbon emissions by publishing National Inventory Reports (NIRs) under the Paris Agreement adopted in 2015 to address the climate crisis. However, current emission estimation methods predominantly rely on bottom-up approaches based on statistical information, which are subject to limitations, including the potential omission of emission sources and the long time required for emission compilation. To overcome these limitations, top-down approaches that estimate emissions using meteorological models and observed atmospheric greenhouse gas concentrations have recently gained increasing attention. This approach has been adopted as a scientific methodology of the Integrated Global Greenhouse Gas Information System (IG³IS), developed under the auspices of the World Meteorological Organization (WMO), and is regarded as a complementary alternative to conventional emission inventories. In this study, carbon dioxide (CO₂) emissions over South Korea were estimated using a top-down approach based on the Stochastic Time-Inverted Lagrangian Transport Model (STILT) and observations from WMO/Global Atmosphere Watch (GAW) stations, and their accuracy was evaluated. The STILT-based inversion results indicate that anthropogenic CO₂ emissions in South Korea for 2019 amount to 589.7 Mt yr⁻¹, which is 83.6 Mt yr⁻¹ lower than the estimate reported in the existing NIR. The downward correction is primarily concentrated in Seoul and the surrounding metropolitan region. Furthermore, to account for the spatial characteristics of CO₂ emission distributions, high-resolution and realistic emission estimates were derived for regions with dense point-source emissions using the Weather Research and Forecasting (WRF) model. The application of top-down approaches for greenhouse gas emission estimation in East Asian countries, together with continuous technological advancement, is expected to provide a scientific foundation for improving the reliability of emission estimates and supporting future climate crisis response strategies.