Eddy covariance carbonyl sulfide flux measurements with a quantum cascade laser absorption spectrometer

Abstract

The trace gas carbonyl sulfide (COS) has lately received growing interest from the eddy covariance (EC) community due to its potential to serve as an independent approach for constraining gross primary production and canopy stomatal conductance. Thanks to recent developments of fast-response high-precision trace gas analysers (e.g. quantum cascade laser absorption spectrometers, QCLAS), a handful of EC COS flux measurements have been published since 2013. To date, however, a thorough methodological characterisation of QCLAS with regard to the requirements of the EC technique and the necessary processing steps has not been conducted. The objective of this study is to present a detailed characterisation of the COS measurement with the Aerodyne QCLAS in the context of the EC technique and to recommend best EC processing practices for those measurements. Data were collected from May to October 2015 at a temperate mountain grassland in Tyrol, Austria. Analysis of the Allan variance of high-frequency concentration measurements revealed the occurrence of sensor drift under field conditions after an averaging time of around 50g s. We thus explored the use of two high-pass filtering approaches (linear detrending and recursive filtering) as opposed to block averaging and linear interpolation of regular background measurements for covariance computation. Experimental low-pass filtering correction factors were derived from a detailed cospectral analysis. The CO2 and H2O flux measurements obtained with the QCLAS were compared with those obtained with a closed-path infrared gas analyser. Overall, our results suggest small, but systematic differences between the various high-pass filtering scenarios with regard to the fraction of data retained in the quality control and flux magnitudes. When COS and CO2 fluxes are combined in the ecosystem relative uptake rate, systematic differences between the high-pass filtering scenarios largely cancel out, suggesting that this relative metric represents a robust key parameter comparable between studies relying on different post-processing schemes.