Revisiting Energy Balance Non-Closure through Multivariate Atmospheric Regime Analysis

To better understand the global carbon budget, terrestrial greenhouse gas (GHG) emissions can be quantified using the eddy covariance (EC) technique. However, this method sill suffers from the well-known energy balance closure (EBC) problem observed at most EC stations. Mesoscale organized atmospheric structures have been proposed as the main hypothesis to explain this persistent lack of EBC. Several studies suggest that, since turbulent transport mechanisms affect both energy and scalar fluxes, systematic energy imbalance may indicate regime-dependent biases in GHG emissions estimates. This could lead to underestimated long-term budgets, which is why improving our understanding of the mechanisms underlying energy balance non-closure is essential.

The ICOS station in Lonzée (BE-Lon, cropland) has one of the longest and most complete data series on cropland in Europe, offering more than 20 years fluxes measurements. Our study proposes a new approach based on multivariate analysis and temporal segmentation to identify and characterize coherent atmospheric episodes. These episodes are subsequently grouped using clustering techniques into statistically distinct regimes, whose EBC characteristics are then quantified and compared. First results show that, within each atmospheric stability condition, several regimes are identified, displaying contrasted mean EBC levels. However, transitions between these configurations remain gradual, suggesting a continuum rather than sharply separated states. In addition, remote sensing products are used to quantify landscape heterogeneity and assess its influence on the occurrence of the identified atmospheric regimes.