Atmospheric modeling of airborne GHG observations over Europe using a regional transport model : towards quantitative inversions using multiple species
Long-term observations of greenhouse gases are necessary to improve our understanding of sources and sinks of GHGs and their interaction with a changing climate. Such observations are used in combination with inverse atmospheric transport models to estimate surface-atmosphere exchange fluxes. Most of these observations are nowadays collected by ground-based networks of tall towers or satellites in low orbit. However, in the last decade, a new stream of data is gaining momentum: regularly collected airborne data. Airborne data provide an interesting alternative because by collecting observations along the vertical path of an aircraft it is possible to better understand the vertical structure of the atmosphere. Originally limited by the cost of rental aircrafts, this new source of data can now provide in-situ measurements on a regular basis thanks to strategic partnerships between academia and airlines all over the world. A clever way to reduce costs is in-fact to exploit platforms that are naturally bound to fly as much as possible like commercial airliners. In Europe, the leading project making use of this technique is MOZAIC/IAGOS (Measurements of Ozone by Airbus In-service airCraft / In-service Aircraft for a Global Observing System). The modeling framework used in this thesis combines a regional Lagrangian transport model (STILT, the Stochastic Time Inverted Lagrangian Transport model) with simulated fluxes from anthropogenic emissions for three trace gases (CO2, CO and CH4), biogenic emissions for CO2, and lateral boundary conditions from global models. We chose this framework because it ensures a fairly good representation of trace gas distribution, it allows for inverse modeling to retrieve regional fluxes, and is flexible enough to assess sources of mismatch between simulated and observed trace gas distributions.