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Abstract

Methane (CH4) emissions from biogenic sources, such as Arctic permafrost wetlands, are associated with large
uncertainties because of the high variability of fluxes in both space and time. This variability poses a challenge to monitoring
CH4 fluxes with the eddy covariance (EC) technique, because this approach requires stationary signals from spatially homogeneous
sources. Episodic outbursts of CH4 emissions, i.e. outgassing in the form of bubbles from oversaturated groundwater
or surfacewater, are 5 particularly challenging to quantify. Such events typically last for only a few minutes, which is much
shorter than the common averaging interval for eddy covariance (30 minutes). The steady state assumption is jeopardized,
which potentially leads to a non-negligible bias in the CH4 flux. We tested and evaluated a flux calculation method based on
wavelet analysis, which, in contrast to regular EC data processing, does not require steady-state conditions and is allowed to
obtain fluxes over averaging periods as short as 1 minute. We demonstrate that the occurrence of extreme CH4 flux events
10 over the summer season followed a seasonal course with a maximum in early August, which is strongly correlated with the
maximum soil temperature. Statistics on meteorological conditions before, during, and after the detected events revealed that it
is atmospheric mixing that triggered such events rather than CH4 emission from the soil. By investigating individual events in
more detail, we identified various mesoscale processes like gravity waves, low-level jets, weather fronts passing the site, and
cold-air advection from a nearby mountain ridge as the dominating processes. Overall, our findings demonstrate that wavelet
15 analysis is a powerful method for resolving highly variable flux events on the order of minutes. It is a reliable reference to
evaluate the quality of EC fluxes under non-steady-state conditions.