Spatiotemporal dynamics of atmospheric CO₂ across China revealed by long-term, high-resolution satellite-derived data

Abstract. Understanding the spatiotemporal dynamics of atmospheric carbon dioxide (CO₂) is fundamental for advancing climate change research and designing effective mitigation strategies. Yet current analyses are constrained by two key limitations: sparse observations that hinder intra-urban assessment and relatively short monitoring periods that limit long-term consistency. To overcome these challenges, we developed a long-term atmospheric CO₂ hindcast modeling framework that generates daily 1-km column-averaged dry-air mole fraction of CO₂ (XCO₂) across China for 2000–2020. The framework adapts the proven PM_2.5 hindcast approach to CO₂ estimation by training an Extremely Randomized Trees model on the residuals between OCO-2 observations and CarbonTracker simulations. The model integrates a comprehensive set of physically interpretable predictors—including MAIAC aerosol optical depth, NO₂, peroxyacetyl nitrate, meteorological variables, and land-use indicators—linking CO₂ variability to co-emitted tracers and boundary-layer processes. Rigorous evaluation demonstrated high reliability (cross-validation R² = 0.94–0.97, RMSE = 0.82–1.29 ppm; independent validation R² = 0.82–0.97). The resulting long-term, high-resolution dataset reveals distinct carbon hotspots and their evolution: the North China Plain remained persistently elevated with rapid increases during 2000–2010, while southern China exhibited accelerated growth after 2010. Enhancement analyses identified consistent intra-regional hotspots in southeastern Beijing-Tianjin-Hebei and northern Zhejiang, with emissions declining after 2012 and rebounding after 2018. During the Wuhan COVID-19 lockdown, urban cores showed sharper reductions than suburban areas. The proposed XCO₂ hindcast modeling framework and the resulting dataset provide a valuable foundation for advancing carbon-neutrality assessments and guiding climate policy across multiple spatial scales.