We show that forward simulations of global CO2 using an atmospheric transport model (ATM) at 0.5°×0.625° and 4°×5° resolutions differ significantly in vertical and meridional distribution. Comparing two observing simulation system experiments at 4°×5° resolution that assimilate pseudo observations sampled from the two forward simulations, we isolated the impact of coarse-resolution ATM error on regional flux estimates that a significant amount of annual carbon uptake from the ocean and tropics is improperly redistributed to the land and extratropics, respectively. In addition, this error leads to an underestimated seasonal amplitude in the northern extratropical land and a reversed seasonal phase in the northern extratropical ocean. The reversed seasonal phase has also been shown in a real data assimilation experiment and state-of-the-art inversions, suggesting that ocean glint retrieval error may not be as significant as previously thought and reasonable ocean flux estimates depend strongly on the accuracy of ATM.

The outbreak of coronavirus disease 2019 (COVID-19) has caused tremendous loss to human life and economic decline in China. A timely assessment of COVID-19’s impact on provincial CO emission reductions is crucial for accurately understanding the degree of reduction and its implications for mitigation measures. Here, we used gross domestic product (GDP) and an inventory (CEADs) to estimate the reductions in the first quarter (Q1) of 2020. We find a reduction of -257.7 Mt CO (-11.0%) over 2019 Q1. Secondary industry contributed 72.5% of the total reduction, due largely to lower coal consumption and cement reduction. At the provincial level, Hubei contributed the most to reductions. Transportation reduction also made a significant contribution. One policy implication is advocating working from home and holding teleconferences to reduce traffic emissions. We provide provincial reductions as spatial constraints for modeling studies and further support for both the carbon cycling scientists and policy makers.