A WRF-Chem study of the greenhouse gas column and in situ surface concentrations observed in Xianghe, China – Part 1: Methane (CH4)

Abstract. This study is the first of two companion papers which investigate the temporal variability of CO2, CH4 and additionally CO concentrations measured at the Xianghe observation site near Beijing in China using the Weather Research and Forecast model coupled with Chemistry (WRF-Chem), aiming to understand the contributions from different emission sectors and the influence of meteorological processes. Simulations of the in situ (Picarro) and remote sensing (TCCON-affiliated) measurements are produced by the model's greenhouse gas option, called WRF-GHG, from September 2018 until September 2019. The present study discusses the results for CH4. The model shows good performance, after correcting for biases in boundary conditions, achieving correlation coefficients up to 0.66 for near-surface concentrations and 0.65 for column-averaged data. The simulations use separate tracers for different source sectors and revealed that energy, residential heating, waste management and agriculture are the primary contributors to the CH4 concentrations, with the energy sector having a greater impact on column measurements than surface concentrations. Monthly variability is linked to both emission patterns and meteorological influences, with advection of either clean or polluted air masses from the North China Plain playing a significant role. The diurnal variation of the in situ concentrations due to planetary boundary layer dynamics is quite well captured by WRF-GHG. Despite capturing the key variability of the CH4 observations, the model displays a seasonal bias, likely originating from an incorrect seasonality in the emissions from agricultural and/or waste management activities. Our findings highlight the value of WRF-GHG to interpret both surface and column observations in Xianghe, offering source sector attribution and insights into the link with local and large-scale winds based on the simultaneously computed meteorological fields. However, they also highlight the need to improve the knowledge on the seasonal CH4 cycle in northern China to obtain more accurate emission data and boundary conditions for high-resolution modeling.