Arctic Regional Methane Fluxes by Ecotope as Derived Using Eddy Covariance from a Low-Flying Aircraft

Sayres, D. S., R. Dobosy, C. Healy, E. Dumas, J. Kochendorfer, J. Munster, J. Wilkerson, B. Baker, and J. G. Anderson (2017), Arctic regional methane fluxes by ecotope as derived using eddy covariance from a low-flying aircraft, Atmos. Chem. Phys. 17(13): 8619-8633; doi: 10.5194/acp-17-8619-2017.

The Arctic terrestrial and sub-sea permafrost region contains approximately 30 % of the global carbon stock, and therefore understanding Arctic methane emissions and how they might change with a changing climate is important for quantifying the global methane budget and understanding its growth in the atmosphere. Here we present measurements from a new in situ flux observation system designed for use on a small, low-flying aircraft that was deployed over the North Slope of Alaska during August 2013. The system combines a small methane instrument based on integrated cavity output spectroscopy (ICOS) with an air turbulence probe to calculate methane fluxes based on eddy covariance. We group surface fluxes by land class using a map based on LandSat Thematic Mapper (TM) data with 30 m resolution. We find that wet sedge areas dominate the methane fluxes with a mean flux of 2.1 µg m−2 s−1 during the first part of August. Methane emissions from the Sagavanirktok River have the second highest at almost 1 µg m−2 s−1. During the second half of August, after soil temperatures had cooled by 7 °C, methane emissions fell to between 0 and 0.5 µg m−2 s−1 for all areas measured. We compare the aircraft measurements with an eddy covariance flux tower located in a wet sedge area and show that the two measurements agree quantitatively when the footprints of both overlap. However, fluxes from sedge vary at times by a factor of 2 or more even within a few kilometers of the tower demonstrating the importance of making regional measurements to map out methane emissions spatial heterogeneity. Aircraft measurements of surface flux can play an important role in bridging the gap between ground-based measurements and regional measurements from remote sensing instruments and models.