A WRF-Chem study on the variability of CO₂, CH₄ and CO concentrations at Xianghe, China supported by ground-based observations and TROPOMI

Abstract. The temporal variability of both surface concentrations and column abundances of CO₂, CH₄ and CO at the Xianghe site in China are analyzed with the Weather Research and Forecast model coupled with Chemistry (WRF-Chem). Simulations of these in situ (PICARRO) and remote sensing (TCCON-affiliated) measurements are produced by the model's passive tracer option, called WRF-GHG, from September 2018 until September 2019. Our analysis found a good model performance with correlation coefficients between observations and simulations up to 0.85 for CO₂ and 0.69 for CO. Key source sectors for every gas are revealed by tracking the anthropogenic fluxes in separate tracer fields. While there are slight variations in the relative impacts of these source sectors between surface and column observations, owing to differences in the sensitivity footprint of each observation type, the primary sectors influencing the various species are evident. For CO₂ the industry, energy and biosphere sectors are found to be the primary contributors to the total simulated concentration, whereas CH₄ concentrations are predominantly attributed to the energy, agriculture and residential & waste sectors. For CO, industry is the largest contributing sector at Xianghe, followed by residential and transportation sources. Differences among the various observation types were particularly visible in the contributions of the biosphere to CO₂ and the energy sector to CH₄, as their largest sources are located further away from Xianghe. Further, the influence of meteorological factors on the variability observed in the different time series was analyzed. We found that southwest winds typically bring polluted air masses from the North China Plain to the site, while northern winds are associated with cleaner conditions. Variability in surface measurements is primarily driven by the daily cycle of accumulation and atmospheric mixing linked with the planetary boundary layer height. Furthermore, the study demonstrates the ability to detect strong regional sources at Xianghe depending on wind direction. To address inconsistencies between the simulations and observations of CH₄, we looked at TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. We found that the model underestimation of CH₄ in summer and overestimation in winter may result from a combination of a similar bias in the lateral boundary conditions and an incorrect monthly variation of the CH₄ emissions in the agriculture and/or waste sectors of the CAMS-GLOB-ANT inventory over north China. Additionally, WRF-GHG simulations indicated a possible overestimation of coal mine emissions nearby Tangshan, which could not be confirmed nor contradicted by the TROPOMI observations. In summary, our findings highlight the value of WRF-GHG to interpret both surface and column observations at Xianghe, offering source sector attribution and insights in the link with local and large-scale winds based on the simultaneously computed meteorological fields. However, given the long lifetime of the considered species and the fact that WRF-GHG is a regional model, accurate initial and lateral boundary conditions remain crucial. The dependence on precise input emission data on the other hand, can be used to evaluate the existing bottom-up inventories.