Characterisation of short-term extreme methane fluxes related to non-turbulent mixing above an Arctic permafrost ecosystem
Methane ( CH 4 ) 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 CH 4 fluxes with the eddy covariance (EC) technique, becaus...
| Published in: | Atmospheric Chemistry and Physics |
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| Main Authors: | , , , |
| Format: | Other/Unknown Material |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/acp-19-4041-2019 https://www.atmos-chem-phys.net/19/4041/2019/ |
| Summary: | Methane ( CH 4 ) 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 CH 4 fluxes with the eddy covariance (EC) technique, because this approach requires stationary signals from spatially homogeneous sources. Episodic outbursts of CH 4 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 min). The steady-state assumption is jeopardised, which potentially leads to a non-negligible bias in the CH 4 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 min. Statistics on meteorological conditions before, during, and after the detected events revealed that it is atmospheric mixing that triggered such events rather than CH 4 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 CH 4 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. |
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