Articles | Volume 19, issue 6
https://doi.org/10.5194/acp-19-4041-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-19-4041-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Apr 2019
Research article |
| 01 Apr 2019
| 01 Apr 2019
Characterisation of short-term extreme methane fluxes related to non-turbulent mixing above an Arctic permafrost ecosystem
Carsten Schaller
Department of Micrometeorology, University of Bayreuth, 95440 Bayreuth, Germany
Max-Planck-Institute for Biogeochemistry, 07745 Jena, Germany
now at: University of Münster, Institute of Landscape Ecology, Climatology Research Group, Heisenbergstr. 2, 48149 Münster, Germany
Fanny Kittler
Max-Planck-Institute for Biogeochemistry, 07745 Jena, Germany
Thomas Foken
Department of Micrometeorology, University of Bayreuth, 95440 Bayreuth, Germany
University of Bayreuth, Bayreuth Center of Ecology and Environmental Research (BayCEER), 95440 Bayreuth, Germany
Mathias Göckede
CORRESPONDING AUTHOR
Max-Planck-Institute for Biogeochemistry, 07745 Jena, Germany
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Cited
14 citations as recorded by crossref.
- Quantitative Evaluation of Wavelet Analysis Method for Turbulent Flux Calculation of Non‐Stationary Series Y. Li et al. 10.1029/2022GL101591
- Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales S. Knox et al. 10.1111/gcb.15661
- Sensitivity of Analytical Flux Footprint Models in Diverse Source‐Receptor Configurations: A Field Experimental Study S. Kumari et al. 10.1029/2020JG005694
- Forecasting of some greenhouse gases content trend in the air of the Russian Arctic region E. Baglaeva et al. 10.1016/j.apr.2020.10.009
- Estimating immediate post-fire carbon fluxes using the eddy-covariance technique B. Oliveira et al. 10.5194/bg-18-285-2021
- Quantifying the impact of emission outbursts and non-stationary flow on eddy-covariance CH<sub>4</sub> flux measurements using wavelet techniques M. Göckede et al. 10.5194/bg-16-3113-2019
- Interannual, summer, and diel variability of CH4and CO2effluxes from Toolik Lake, Alaska, during the ice-free periods 2010–2015 W. Eugster et al. 10.1039/D0EM00125B
- Methane Emission from a Small Lake after Artificially Created Ebullition J. Forner et al. 10.1007/s13157-023-01685-4
- A permutation approach to evaluating the performance of a forecasting model of methane content in the atmospheric surface layer of arctic region A. Sergeev et al. 10.1016/j.apr.2023.102000
- Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions M. Ueyama et al. 10.1111/gcb.16594
- Detecting Hot Spots of Methane Flux Using Footprint‐Weighted Flux Maps C. Rey‐Sanchez et al. 10.1029/2022JG006977
- Intermittent Surface Renewals and Methane Hotspots in Natural Peatlands E. Zorzetto et al. 10.1007/s10546-021-00637-x
- Studying boundary layer methane isotopy and vertical mixing processes at a rewetted peatland site using an unmanned aircraft system A. Lampert et al. 10.5194/amt-13-1937-2020
- High Levels of CO2 Exchange During Synoptic‐Scale Events Introduce Large Uncertainty Into the Arctic Carbon Budget K. Jentzsch et al. 10.1029/2020GL092256
13 citations as recorded by crossref.
- Quantitative Evaluation of Wavelet Analysis Method for Turbulent Flux Calculation of Non‐Stationary Series Y. Li et al. 10.1029/2022GL101591
- Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales S. Knox et al. 10.1111/gcb.15661
- Sensitivity of Analytical Flux Footprint Models in Diverse Source‐Receptor Configurations: A Field Experimental Study S. Kumari et al. 10.1029/2020JG005694
- Forecasting of some greenhouse gases content trend in the air of the Russian Arctic region E. Baglaeva et al. 10.1016/j.apr.2020.10.009
- Estimating immediate post-fire carbon fluxes using the eddy-covariance technique B. Oliveira et al. 10.5194/bg-18-285-2021
- Quantifying the impact of emission outbursts and non-stationary flow on eddy-covariance CH<sub>4</sub> flux measurements using wavelet techniques M. Göckede et al. 10.5194/bg-16-3113-2019
- Interannual, summer, and diel variability of CH4and CO2effluxes from Toolik Lake, Alaska, during the ice-free periods 2010–2015 W. Eugster et al. 10.1039/D0EM00125B
- Methane Emission from a Small Lake after Artificially Created Ebullition J. Forner et al. 10.1007/s13157-023-01685-4
- A permutation approach to evaluating the performance of a forecasting model of methane content in the atmospheric surface layer of arctic region A. Sergeev et al. 10.1016/j.apr.2023.102000
- Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions M. Ueyama et al. 10.1111/gcb.16594
- Detecting Hot Spots of Methane Flux Using Footprint‐Weighted Flux Maps C. Rey‐Sanchez et al. 10.1029/2022JG006977
- Intermittent Surface Renewals and Methane Hotspots in Natural Peatlands E. Zorzetto et al. 10.1007/s10546-021-00637-x
- Studying boundary layer methane isotopy and vertical mixing processes at a rewetted peatland site using an unmanned aircraft system A. Lampert et al. 10.5194/amt-13-1937-2020
Latest update: 23 Nov 2024
Short summary
Methane emissions from biogenic sources, e.g. Arctic permafrost ecosystems, are associated with uncertainties due to the high variability of fluxes in both space and time. Besides the traditional eddy covariance method, we evaluated a method based on wavelet analysis, which does not require a stationary time series, to calculate fluxes. The occurrence of extreme methane flux events was strongly correlated with the soil temperature. They were triggered by atmospheric non-turbulent mixing.
Methane emissions from biogenic sources, e.g. Arctic permafrost ecosystems, are associated with...
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