Airborne measurements of methane fluxes in permafrost landscapes (AirMeth)

One of the most pressing questions with regard to climate feedback processes in a warming Arctic is the regional-scale greenhouse gas release from Arctic permafrost areas. The Airborne Measurements of Methane Fluxes (AIRMETH) campaigns are designed to quantitatively and spatially explicitly address...

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Bibliographic Details
Main Authors: Sachs, Torsten, Serafimovich, Andrei, Metzger, Stefan, Wieneke, Sebastian, Kohnert, Katrin, Hartmann, Jörg
Format: Conference Object
Language:unknown
Published: EUCOP 4 2014
Subjects:
Online Access:https://epic.awi.de/id/eprint/39542/
https://epic.awi.de/id/eprint/39542/1/2014_EUCOP4_Book_of_Abstracts.pdf
http://www.eucop4.org/documents.html
https://hdl.handle.net/10013/epic.46694
https://hdl.handle.net/10013/epic.46694.d001
Description
Summary:One of the most pressing questions with regard to climate feedback processes in a warming Arctic is the regional-scale greenhouse gas release from Arctic permafrost areas. The Airborne Measurements of Methane Fluxes (AIRMETH) campaigns are designed to quantitatively and spatially explicitly address this question. Ground-based eddy covariance (EC) measurements provide continuous in-situ observations of the surface-atmosphere exchange of energy and matter. However, these observations are rare in the Arctic permafrost zone and site selection is bound by logistical constraints among others. Consequently, these observations cover only small areas that are not necessarily representative of the region of interest. Airborne measurements can overcome this limitation by covering distances of hundreds of kilometers over time periods of a few hours. During the AIRMETH-2012 and AIRMETH-2013 campaigns aboard the research aircraft POLAR 5 we measured turbulent exchange fluxes of energy, methane, and (in 2013) carbon dioxide along thousand of kilometers covering the North Slope of Alaska and the Mackenzie Delta, Canada. Time-frequency (wavelet) analysis, footprint modeling, and machine learning techniques are used to extract spatially resolved turbulence statistics and fluxes, spatially resolved contributions of land cover and biophysical surface properties to each flux observation, and regionally valid functional relationships between environmental drivers and observed fluxes that can explain spatial flux patterns and – if available in temporal resolution – allow for spatio-temporal scaling of the observations. This presentation we will focus on 2012 methane fluxes on the North Slope of Alaska and the relevant processes on the regional scale.