Application of the WRF-GHG model for the interpretation of ground-based observations of atmospheric greenhouse gas concentrations

The concentration of greenhouse gases (GHG) in the atmosphere, such as carbon dioxide (CO2) and methane (CH4), as well as the indirect greenhouse gas carbon monoxide (CO), varies greatly by location and over time, due to the complex interaction between emission sources, sinks and weather conditions. Understanding these fluctuations is crucial, as GHGs play a pivotal role in our climate system through the greenhouse effect, leading to substantial climate change. This PhD thesis analyzes observed time series at two distinct locations, which are part of international observing networks dedicated to long-term GHG monitoring: the remote tropical island of Réunion in the Indian Ocean and the urbanized county of Xianghe near Beijing, China. These ground-based measurements, collected through both in situ and remote sensing techniques, are simulated using the WRF-GHG atmospheric transport model.
The comparison between observations and model simulations allows for an extensive evaluation of the model's performance in these understudied regions, while also advancing our understanding of how human activities, natural processes, and weather patterns influence GHG concentrations at these sites. The model simulations reveal the most important source sectors contributing to the observed concentrations at both locations, taking into account the different characteristics of the in situ and remote sensing measurement techniques.
Remote sensing of column concentrations is generally more sensitive to distant sources and long-range transport, while the variability of in situ concentrations near the surface is strongly influenced by local wind patterns and nearby emissions. Although the WRF-GHG model proves to be a valuable tool for studying regional GHG dynamics by successfully capturing the temporal variation of the different time series at both sites, this study also highlights some limitations. It demonstrates the importance of high-quality input data for accurate model simulations and identifies the need to further advance our knowledge on the CH4 budget and its seasonality.
Overall, this research provides important insights into the complex interplay between GHG emissions, atmospheric transport, and measurement techniques, which can inform future efforts to monitor and mitigate climate change.