Is the biosphere-atmosphere exchange of total reactive nitrogen above forest driven by the same factors as carbon dioxide? An analysis using artificial neural networks

Phase and amplitude of ecosystem-atmosphere fluxes of reactive nitrogen compounds are poorly understood due to a lack of suitable observation methods. Understanding the biophysical controls of the surface nitrogen exchange is essential for the parameterization of process-based and chemical transport models that can be used for the determination of regional or national nitrogen budgets. In this study, we investigated similarities in time series of net total reactive nitrogen (ΣN_r) and carbon dioxide (CO₂) fluxes above forest with regard to their variability and driving factors. We found corresponding shapes of the mean diurnal summertime patterns of ΣN_r and CO₂. While ecosystem respiration leads to a net CO₂ release at night, ΣN_r was on average deposited throughout the entire observation period. Using artificial neural network analysis, global radiation (Rg) was identified to be the main control for both ΣN_r and CO₂. While the concentration of ΣN_r substantially improved the coefficient of determination for ΣN_r fluxes when used as a secondary driver, only minor improvements of 2–3% were found for CO₂ fluxes when using for example temperature or vapour pressure deficit (VPD) as secondary driver. Considering two dominant drivers, 41 and 66% of the variability in ΣN_r and CO₂ fluxes, respectively, could be explained. Further data stratification for ΣN_r revealed that higher concentrations, higher temperature, and higher VPD as well as dry leaf surfaces tend to favour higher deposition of ΣN_r, whereas lower concentrations, lower temperature, and lower VPD as well as wet leaf surfaces mainly correspond to situations when less ΣN_r was deposited or even emitted. Our results support the understanding of biosphere-atmosphere interactions, their driving factors, and establish a link between ΣN_r and CO₂ exchange, which may be beneficial for future developments in state-of-the-art exchange modelling.