15:30 〜 15:45
[ACG39-07] Advancing Methane Flux Estimation: Integrating Isotopic, Satellite, and Modeling Approaches to Support Global CH4 Monitoring
キーワード：methane, climate change, GOSAT
The Global Methane Pledge (GMP), launched at COP26 in 2021 and signed by 158 countries, aims to reduce global CH4 emissions by at least 30% below 2020 levels by 2030. Achieving this goal is essential to meeting the goals of the Paris Agreement. The Global Carbon Project (GCP) provides updates on the global CH4 budget at multi-year intervals, the most recent of which covers 2010-2019. However, more frequent updates are needed to accurately monitor CH4 emissions and sinks. The World Meteorological Organization (WMO) Global Greenhouse Gas Watch (G3W) program has called for high-resolution CH4 flux estimates with minimal latency.
This study addresses these challenges by developing an advanced system for accurate and timely estimation of CH4 fluxes that integrates multiple isotopic observations, satellite datasets, and atmospheric models. The isotopic composition of CH4 provides valuable insight into its atmospheric sources, as different emission sectors (microbial, thermogenic, biomass burning) exhibit distinct stable carbon and hydrogen isotopic signatures. By incorporating relatively sparse but highly accurate isotopic measurements, the proposed system enhances the attribution of CH4 emissions to specific sources, improving sector-specific emission tracking and trend analysis. Satellites equipped with advanced sensors offer unprecedented advantages for global CH4 monitoring, including continuous coverage, high temporal resolution, and minimal data latency compared to traditional ground-based methods. The atmospheric transport model MIROC4-ACTM is used to efficiently assimilate and analyze large amounts of observational data. This high-performance computing framework enables rapid testing of different satellite data, ensuring timely flux estimates to support global monitoring initiatives under G3W and GCP. Long-term variations derived from an inversion based on isotopic composition, while short-term updates based on the CH4 burden approach using satellite xCH4 data.
In this analysis, we have focused on the period since 2010, using GOSAT xCH4 data from both bias-corrected and bias-corrected products. The bias uncorrected product provides a slightly larger spatial coverage, while the bias corrected product reduces the discrepancy between model and observation. Although the mean discrepancy is quite reasonable, there are several outliers that could bias the analysis. Outlier correction is an important point to improve flux estimation in the proposed method. The proposed results are compared with the methane budget from the GCP 2024 report.
This study addresses these challenges by developing an advanced system for accurate and timely estimation of CH4 fluxes that integrates multiple isotopic observations, satellite datasets, and atmospheric models. The isotopic composition of CH4 provides valuable insight into its atmospheric sources, as different emission sectors (microbial, thermogenic, biomass burning) exhibit distinct stable carbon and hydrogen isotopic signatures. By incorporating relatively sparse but highly accurate isotopic measurements, the proposed system enhances the attribution of CH4 emissions to specific sources, improving sector-specific emission tracking and trend analysis. Satellites equipped with advanced sensors offer unprecedented advantages for global CH4 monitoring, including continuous coverage, high temporal resolution, and minimal data latency compared to traditional ground-based methods. The atmospheric transport model MIROC4-ACTM is used to efficiently assimilate and analyze large amounts of observational data. This high-performance computing framework enables rapid testing of different satellite data, ensuring timely flux estimates to support global monitoring initiatives under G3W and GCP. Long-term variations derived from an inversion based on isotopic composition, while short-term updates based on the CH4 burden approach using satellite xCH4 data.
In this analysis, we have focused on the period since 2010, using GOSAT xCH4 data from both bias-corrected and bias-corrected products. The bias uncorrected product provides a slightly larger spatial coverage, while the bias corrected product reduces the discrepancy between model and observation. Although the mean discrepancy is quite reasonable, there are several outliers that could bias the analysis. Outlier correction is an important point to improve flux estimation in the proposed method. The proposed results are compared with the methane budget from the GCP 2024 report.