Estimating Random Uncertainty in Airborne Flux Measurements Over Alaskan Tundra: Update on the Flux Fragment Method

Airborne turbulence measurement gives a spatial distribution of air-surface fluxes that networks of fixed surface sites typically can not capture. Much work has improved the accuracy of such measurements and the estimation of the uncertainty peculiar to streams of turbulence data measured from the air. A particularly significant challenge and opportunity is to distinguish fluxes from different surface types, especially those occurring in patches smaller than the necessary averaging length (Lenschow et al. 1994). Kirby et al. (2008) proposed the Flux-Fragment Method (FFM), a conditional-sampling variant of eddy covariance in the space/time domain. They demonstrated its ability to segregate the mean flux density (CO₂, H₂O, sensible heat) in maize from that in soybeans over the patchwork farmland of Illinois, USA. This was an ideal surface for the method, and the random-error estimate used a relatively rudimentary bootstrap resampling. The present paper describes an upgraded random-error estimate that accounts for the serial correlation of the time/space series and the heterogeneity of the signal. Results are presented from the Alaskan Tundra (Sayres et al. 2017). Though recognized as important, systematic error estimates are not covered in this paper. Some discussion is offered on the relation of the FFM to other approaches similarly motivated, particularly those using wavelets. Successful measurement of the variation of air-surface exchange over heterogeneous surfaces has value for developing and improving process models relating surface flux to remotely sensible quantities such as the vegetative land-cover type and its condition.