<p><strong>Abstract.</strong> Methane (<span class="inline-formula">CH₄</span>) 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 <span class="inline-formula">CH₄</span> fluxes with the eddy covariance (EC) technique, because this approach requires stationary signals from spatially homogeneous sources.

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... Episodic outbursts of <span class="inline-formula">CH₄</span> emissions, i.e. triggered by spontaneous outgassing of bubbles or venting of methane-rich air from lower levels due to shifts in atmospheric conditions, are particularly challenging to quantify. Such events typically last for only a few minutes, which is much shorter than the common averaging interval for EC (30&amp;thinsp;min). The steady-state assumption is jeopardised, which potentially leads to a non-negligible bias in the <span class="inline-formula">CH₄</span> flux. Based on data from Chersky, NE Siberia, 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&amp;thinsp;min. Statistics on meteorological conditions before, during, and after the detected events revealed that it is atmospheric mixing that triggered such events rather than <span class="inline-formula">CH₄</span> emission from the soil. By investigating individual events in more detail, we identified a potential influence of 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. The occurrence of extreme <span class="inline-formula">CH₄</span> flux events over the summer season followed a seasonal course with a maximum in early August, which is strongly correlated with the maximum soil temperature. Overall, our findings demonstrate that wavelet analysis is a powerful method for resolving highly variable flux events on the order of minutes, and can therefore support the evaluation of EC flux data quality under non-steady-state conditions.</p>