Structural divergence between satellite and surface CO observations across China, North America, and Europe (2015–2024)

Abstract. Satellite and surface observations of atmospheric carbon monoxide (CO) yield divergent emission estimates, yet the underlying causes of this discrepancy remain poorly understood. Here we present a multi-region analysis spanning 2015–2024 over China, North America, and Europe, assimilating MOPITT satellite retrievals and ground-based CO measurements from over 3300 monitoring stations. Our results reveal that the systematic underestimation of modeled surface CO is a common feature across all three continents, with median observation-to-model ratios of 1.82 (China), 1.65 (North America), and 2.25 (Europe). This bias progressively diminishes from urban to rural sites, by 44 % in North America and 58 % in Europe, and almost disappears at remote sites, demonstrating that representation error is an important driver of the surface-model mismatch. In contrast, comparisons of column CO between MOPITT and model show no systematic underestimation. Assimilation experiments further illuminate the structural nature of this divergence: while surface-data assimilation greatly improves agreement with in situ observations, it introduces a positive column bias relative to MOPITT. This bias is most pronounced over China and Europe but remains modest over North America, and accordingly the decadal trends from surface CO show a sharp decline in China (-5.92 % yr^-1) vs. plateaus after 2018 and 2016 in North America and Europe, while satellite-derived column trends are substantially weaker. Our multi-region analysis demonstrates that the satellite-surface discrepancy is a structurally embedded feature of the observing system, and advocates for joint utilization of both observation platforms in future inversion studies to achieve robust and self-consistent CO emission estimates.