Measurements of the Vertical Structure of Carbon Dioxide in the Atmospheric Boundary Layer and the Atmospheric Surface Layer Using UAS

Carbon dioxide (CO₂) has been systematically measured using ground-based observing stations, aircraft, and satellites for many years with a focus on better understanding the global carbon cycle. For instance, studies have demonstrated that the amplitude of seasonal variations of CO₂ concentrations are strongest in the atmospheric boundary layer (ABL) (15-30 ppm) compared to those in the free troposphere (10-15 ppm). These changes are primarily driven by photosynthetic and respiratory processes in the biosphere coupled with fossil fuel combustion emissions from urban locations. While these techniques have been useful in resolving CO₂ sources and sinks on a global scale, a lack of vertically resolved measurements in the ABL has caused large uncertainties in the carbon cycle to remain on a local and regional level.

Recent advances in differential GPS coupled with gas sensing technologies for unmanned aerial systems (UAS) have made it possible to collect spatially and temporally resolved CO₂ profiles within the lowest kilometer of the atmosphere. We propose that measurements made using this approach will greatly improve our ability to sample CO₂ within the ABL and expand our understanding of how it is distributed.

This presentation begins with a brief outline of how we sample CO₂ using UAS and then show results from our field experiments. The presented results come from observations made at the University of Oklahoma’s Kessler Atmospheric and Ecological Research Station (KAEFS) located near Washington, Oklahoma during Summer and Fall 2018. This study examines changes in the vertical structure from diurnal and seasonal cycles as well as estimates daytime CO₂ fluxes. Observations of CO₂ obtained using UAS are referenced against measurements from ground-stations. Profiles will also be assessed to examine the impact of temperature, humidity, soil moisture, vegetation type, and solar radiation on variability.