Using atmospheric inverse model CIF-FLEXPART to estimate the capability to monitor CO2 and CH4 emission changes in Finland

In our changing climate and the green transition movement, it is important to verify national greenhouse gas emission inventories. By using prior emission estimates from inventories and process models as well as atmospheric greenhouse gas observations, we are able to assess emissions and their changes from different sources with inversion modeling. However, the scarcity of measuring stations affect the uncertainty of the inversion models.In this study, we assess our ability to monitor the green transition in Finland. Currently atmospheric greenhouse gas concentrations are measured in 6 locations in Finland. Including new observation towers would fill existing gaps in the observation network. We aim to see how new atmospheric concentration measurement towers with different sensor accuracies could improve greenhouse gas detection in the area. We carry out an observing system simulation experiment (OSSE), using sensitivity tests with the Community Inversion Framework (CIF) using the transport model FLEXPART with a 0.1º x 0.1º spatial resolution on a nested domain, and observing how the model reacts to changes in the prior emissions and synthetic observations.Addition of new stations in Finland could improve greenhouse gas detection and emission inventory assessment, and using lower accuracy sensors could help improve detection with a lower cost. Development of the OSSE experiment is still in progress and emissions from different source sectors, like wetlands and anthropogenic emissions will be optimized for multiple years. Our final inversion products will give improved estimates of inversion model sensitivity and show how effective a new observing system in Finland will be at detecting emissions.