Characterisation of short-term extreme methane fluxes related to non-turbulent mixing above an Arctic permafrost ecosystem

Abstract

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. 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 CH4 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 CH4 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 CH4 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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“…For example, in wetlands with permanent inundation, the seasonal variation of CH 4 exchange is predominantly controlled by temperature and plant phenology (Chu et al 2014;Sturtevant et al 2016). Ecosystem CH 4 exchange also varies considerably at both longer (e.g., interannual; Knox et al 2016;Rinne et al 2018) and shorter (e.g., weeks, days, or hours; Koebsch et al 2015;Hatala et al 2012;Schaller et al 2018) time scales. Wavelet decomposition is a particularly useful tool for investigating scale in geophysical and ecological analysis (Cazelles et al 2008;Torrence and Compo 1998), because it can characterize both the time scale and location of patterns and perturbations in the data.…”

“…Wavelet decomposition is a particularly useful tool for investigating scale in geophysical and ecological analysis (Cazelles et al 2008;Torrence and Compo 1998), because it can characterize both the time scale and location of patterns and perturbations in the data. Partitioning variability across temporal scales can help to isolate and characterize important processes (Schaller et al 2018).…”

“…Reliable EC measurements of CO 2 and water vapor f luxes have been conducted at hundreds of sites across broad regional networks , and substantial efforts have focused on developing best practices and harmonizing approaches across sites to ensure consistent, high-quality flux measurements (Aubinet et al 1999;Reichstein et al 2005;Moffat et al 2007). CH 4 fluxes are often characterized by small fluxes with episodic spikes, and additional research is needed to ensure reliable measurements (Peltola et al 2014(Peltola et al , 2013, and refine and standardize methods and routines for data processing and quality checking (Nemitz et al 2018;Schaller et al 2018). Recent efforts provided guidance on instrument selection, setup and maintenance, and data processing for EC CH 4 flux measurements (Nemitz et al 2018).…”

Quantifying the impact of emission outbursts and non-stationary flow on eddy-covariance CH<sub>4</sub> flux measurements using wavelet techniques

“…The second flux processing method (Schaller et al, 2017(Schaller et al, , 2019 is based on wavelet analysis and uses the sinusoidal and complex-valued Morlet wavelet transform for flux quantification. The Morlet wavelet provides an excellent resolution in the frequency domain and can be used to analyze atmospheric turbulence (e.g., Strunin and Hiyama, 2004;Thomas and Foken, 2005). Since this study focused on comparing eddycovariance-derived and wavelet-derived fluxes, the temporal integration of the wavelet method was chosen to closely match the eddy-covariance method (30 min); however, due to the decomposition in time and frequency domain the averaging intervals could not match perfectly, and an averaging interval of 33 min for the wavelet method was used.…”

Quantifying the impact of emission outbursts and non-stationary flow on eddy covariance CH<sub>4</sub> flux measurements using wavelet techniques

“…In case of methane fluxes, particularly a potential violation of the required steady-state conditions linked to episodic outbursts from wetland sources (Schaller et al, 2018) may lead to low flux data quality, and therefore can substantially increase the gap fraction in quality filtered EC time series. One potential mechanism for such high methane emission events is so-called ebullition (e.g.…”