Exploring atmospheric CH4 monitoring network expansion in Italy using inverse modelling

Abstract

Top-down approaches using inverse modelling provide valuable complementary information to national methane emission inventories, which are primarily based on bottom-up methods. Here, we focus on Italy, where methane is currently monitored at five stations that belong to the Integrated Carbon Observation System (ICOS). Top-down estimates show substantial discrepancies over this country, suggesting that the current network provides weak observational constraints on methane fluxes. In this study, we assess the potential expansion of Italy's ICOS network using Observation System Simulation Experiments (OSSEs) and inverse modelling. Eight candidate sites were identified, selected either from existing non-ICOS monitoring stations or from proposed future locations. To conduct the OSSEs, we use the ICON-ART model coupled with the Community Inversion Framework (CIF). We design a set of network expansion scenarios to evaluate the potential of each candidate station to improve emission constraints and include four additional scenarios to quantify the contribution of existing and idealized networks. To reduce the influence of randomness, multiple emission scenarios are constructed. Among all candidates, Chieti (CHI; 42.2° N, 14.7° E) and Mount Venda (VND; 45.3° N, 11.7° E) emerge as the most effective additions, with Chieti showing a slight overall advantage. Chieti enhances constraints mainly in Central and Southern Italy, while Mount Venda is particularly effective in Northern Italy, where most anthropogenic methane emissions originate. The framework developed here can be readily applied to other countries aiming to optimize their atmospheric measurement networks and to improve constraints on greenhouse gas emissions.