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Atmospheric inverse modelling is a method for estimating surface-to-atmosphere fluxes of greenhouse gases (GHG) based on atmospheric observations and is being increasingly used to assess and improve inventories of emissions at national and sub-national scales. The inversion method is also referred to in the 2006 IPCC Guidelines on national reporting (and in the 2019 refinement) as a way to independently verify national emission inventories. Atmospheric observations have rapidly expanded in the last decade with new satellite missions and improved remote sensing as well as with new ground-based networks and higher precision instrumentation. However, atmospheric inversions are technically complex and can be prone to large uncertainties, especially when they are not well implemented. We present an overview of the state-of-the-art in atmospheric inverse modelling in the context of constraining national GHG emission inventories, using both global and regional models. We also discuss the current challenges, especially the sources of systematic uncertainties, which are generally not reported and remain poorly quantified, and suggest ways to better quantify these uncertainties. We also discuss the need to adjust atmospheric inversion results to make them comparable to the inventories, which report only anthropogenic emissions. Although many of the challenges apply independently of GHG species, we will focus on and give examples for CO2 fluxes from the land-biosphere, CH4 and N2O. Lastly, we present our plans to develop a “best practice” for atmospheric inverse modelling within the new Horizon Europe project, EYE-CLIMA. This will include practical guidelines on how to set-up inversion frameworks, e.g. the selection of observations, inversion domain, prior and observation uncertainties, as well as on how to evaluate and report atmospheric inversion results. The best practice guidelines will support the definition of a standard in atmospheric inverse modelling and quality control.
Topic : Theme 2: State of play in integrated approaches for advanced GHG emission estimates and the way forward to operational services.
Reference : T2-B8
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