Abstract Accurate simulation of regional carbon dioxide (CO 2 ) concentrations is critical for urban carbon monitoring, inverse modeling and mitigation. However, large uncertainties persist due to differences in anthropogenic emission inventories in China. Focusing on Jiangsu Province in the Yangtze River Delta, where the mean inter‐inventory spread in annual city‐level total emissions exceeds 60% and spatial discrepancies are substantial, we assessed how six widely used inventories affect modeled CO 2 fields. Using a 3‐km WRF‐Chem‐VPRM framework, we performed simulations for July and December 2022. To isolate the impact of inventory‐related uncertainties, we designed sensitivity experiments that separately perturbed inventory selection, emission magnitude, spatial distribution, and temporal and vertical allocation used in the inventory‐to‐model matching. All experiments were driven by identical meteorological conditions to ensure comparability. Model outputs showed good consistency with meteorological observations, CarbonTracker near‐surface CO 2 , and OCO‐2 XCO 2 ( R ≈ 0.83), while also capturing physically reasonable near‐surface diurnal behavior. Inventory selection led to a nighttime urban domain‐averaged standard deviation of 8.2 ppm. Sensitivity results indicated that spatial allocation differences contributed more to modeled CO 2 variability than total emission magnitude. Under stable boundary‐layer conditions, vertical allocation emerged as a key uncertainty source, producing 25–50 ppm differences in surface CO 2 . These results demonstrate that inventory‐to‐model matching, especially vertical allocation, can exceed the impact of inventory selection for high‐resolution urban CO 2 simulations under stable nighttime conditions. This study provides a quantitative basis for diagnosing inventory‐induced variability and supports the development of fine‐resolution, vertically resolved inventories for robust urban CO 2 modeling and future inversion efforts.
Feng et al. (Mon,) studied this question.