Atmospheric Remote Sensing
Airborne trace gas measurements and mesoscale modelling
Inverse data-driven estimation
Integrating surface-atmosphere Exchange Processes Across Scales - Modeling and Monitoring
Tall Tower Atmospheric Gas Measurements
Carbon Cycle Data Assimilation
Satellite-based remote sensing of greenhouse gases
More on my scientific interests
Atmospheric CO2 is increasing rapidly since the industrialization due to constantly rising emissions of CO2 associated with the combustion of fossil fuels. In fact modern humans are in the process of releasing most of the carbon contained in fossil fuel reservoirs into the atmosphere in only a few hundred years. These same reservoirs took nature hundreds of millions of years to build up.
Currently about half of the emitted CO2 stays in the atmosphere (and causes the increase), and the other half disappears in reservoirs such as the global oceans and the biosphere. These reservoirs are sensitive to environmental conditions, for example increased temperatures can lead to higher probabilities of fires, causing the carbon stored in biomass to be released as CO2 in a very short time. A feedback of the opposite sign is the potentially accelerated growth of plants due to increased atmospheric levels of CO2, a fertilization effect. In climate models such feedback mechanisms have a crucial impact on predicted future climate. Therefore we need to better understand the underlying processes rather soon to allow better predictions.
One way of investigating and quantifying such processes is to use atmospheric measurements of CO2 in combination with transport models, and to derive surface-atmosphere exchange by solving the inverse problem (i.e. find the surface fluxes that satisfy the measurements, given the transport model). On global scales such inversions have been done mainly using measurements made at remote locations (often coastal stations and islands), far away from strong (and variable) surface fluxes.
However, in order to better quantify biosphere-atmosphere fluxes on regional scales (scales of land-use patterns, of disturbances, and of political boundaries), measurements have to be utilized that are made over continental areas, in closer proximity of the processes that matter. Since increasing complexity of the fluxes (higher spatial variability) and meteorology (mesoscale circulations such caused by non-ideal terrain) strongly reduces the ability of existing inversion models to represent the measurements of CO2, new modeling tools have to be developed.
As experimentalist who has collected data from airplanes, the many details of the atmosphere that can complicate interpretation of these data in a model attract my attention: clouds, sometimes indicating massive convective transport on small horizontal scales; a mixed layer with a sometimes well defined top, eddies that shake the airplane but that also transport and mix pollutants (or signales from surface sources and sinks) within the mixed layer. Such observations influenced my decision to work at the interface between experiment (atmospheric trace gas measurements) and theory (transport modeling).
The final goal is a model-data fusion framework that can utilize the wealth of information provided by measurements from many different experiments such as: tall tower trace gas measurements, airborne tracer measurement, remote sensing of global trace gas distribution, eddy flux measurements, remote sensing of land and ocean, land cover/land use, inventory data, etc..