Automated chamber systems are advancing our fundamental understanding of the biogenic greenhouse gas (GHG) fluxes, carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), in terrestrial and aquatic ecosystems. Chamber based flux data is often used to derive emission factors for GHG accounting, for example for agricultural systems, which are central for climate mitigation actions.
New technologies are developing fast and produce large, high-frequency datasets consisting of thousands of flux measurements, enabling new insights into key biogeochemical cycles and their temporal and spatial regulation. However, the increased amount of data also creates a need for new methodologies for raw data processing, data curation, and data analysis to harness the complexity in these data sets. Examples of analytical challenges include: how to objectively quality control flux data? how to develop robust methods for selecting the appropriate time window for flux calculations during a chamber closure period? how to interpret large datasets? We seek abstracts on field and laboratory studies utilising automated systems for measuring surface-atmosphere GHG exchange, novel processing and analytical approaches for automated chamber data, and modelling studies based on automated chamber data.