Investigating ecosystem respiration CO2 signals using night-time ICOS tower observations

The biosphere-atmosphere CO₂ exchange is the largest carbon flux in the global carbon cycle, yet substantial uncertainties remain in quantifying gross primary production (GPP) and ecosystem respiration (Re). Top-down atmospheric inversion modeling provides a powerful approach to reduce the uncertainties in surface fluxes through a combination of atmospheric observations and transport modeling. However, as during nighttime mixing process of the atmosphere is weakly developed and hard to simulate in atmospheric transport models, atmospheric inversions typically rely on afternoon observations when both GPP and Re occur simultaneously, making it challenging to isolate their individual contributions.  In order to disentangle the respiration signals and simultaneously utilize previously unused observational data, we established a novel algorithm for the identification of night-time mixing height, based on the temporal variation of virtual potential temperature from ICOS tower measurements. The method is validated using profile information on greenhouse gases. We then integrated CO₂ signals below the diagnosed mixing height and incorporated these partial column increment as observational operators in CarboScope-Regional (CSR), a Bayesian inverse modeling framework. This enhanced inversion scheme enables improved quantification of ecosystem respiration (and, by extension, GPP), bringing about a better understanding and constrains on the the role of biological fluxes in European carbon budgets.