Greenhouse Gases

Analysis of GHG concentration and flux measurements
We work on the analysis of Greenhouse Gas (GHG) concentration and (Eddy Covariance) flux measurements at a Swiss tall tower site. Most flux towers provide observations at heights of several meters to tens of meters and therefore provide information about possible flux sources on a local spatial scale. Here we focus on one of the few European tall towers located close to Beromünster, Switzerland. The tower was initially set up as a CarboCount CH site — a dense GHG observation network run for four years (2012 – 2015) — and is continued since by the University of Bern. The highest measurements are taken at an altitude of 212 m above ground. This relatively high observation height results in a a flux footprint (field of view) of the tower of many kilometers, making its observations predestined to a source analysis on a much larger spatial scale than typical for flux towers. On the one hand, we use a flux footprint parameterization to estimate where the measured fluxes originate. Additionally, we analyse GHG concentration measurements by season, time of day, wind direction, and other criteria and perform various correlation analyses. In future work, the FLEXPART model will be used to further investigate the origin of the measured pollutants. This work is done in a close collaboration with the University of Bern.

Scientists:
Andreas Plach, Andreas Stohl

Inverse Modeling
Inverse modeling provides a powerful tool to verify national greenhouse gas (GHG) emission inventories by using atmospheric observations. We use the Lagrangian particle dispersion model FLEXPART in the backward mode to obtain a relationship between changes in atmospheric mixing ratios and GHG emissions. We then use this relationship to perform Bayesian inversions with the framework FLEXINVERT+, where a priori estimates of the GHG emissions are optimized to better fit atmospheric observations. 
On the one hand we use in-situ observations and satellite measurements to determine the emissions of different GHG species such as Sulfur Hexafluoride and Methane. On the other hand, we also investigate methodological aspects of inverse modeling to improve and further develop the method.

Scientists:
Martin Vojta, Rakesh Subramanian, Andreas Plach, Andreas Stohl