Constraining the 2012-2014 growing season Alaskan methane budget using CARVE aircraft measurements

Publication information:

S. Hartery, R.Y-W. Chang, R. Commane, J. Lindaas, A. Karion, C. Sweeney, J. Henderson, M. Mountain, N. Steiner, K. McDonald, S. Dinardo, C. E. Miller, and S. C. Wofsy. 2017. “Constraining the 2012-2014 Growing Season Alaskan Methane Budget Using CARVE Aircraft Measurements ”. Atmospheric Chemistry and Physics Discussions, Pp. 1-27

Abstract

Methane (CH 4) is the second most important greenhouse gas but its emissions from northern regions is still poorly constrained. In this study, we analyze a subset of in situ CH 4 aircraft observations made over Alaska during the growing seasons of 2012–2014 as part of the Carbon in Arctic Reservoir Vulnerability Experiment (CARVE). Surface CH 4 fluxes are estimated using an atmospheric particle transport model which quantitatively links surface emissions from Alaska and the western Yukon with observations of enhanced CH 4 in the boundary layer. We estimate that between May and September, 2.1 ± 0.5 Tg, 1.7 ± 0.4 Tg and 2.0 ± 0.3 Tg of CH 4 were emitted from the region of interest for 2012–2014, respectively. The predominant sources of the CH 4 budget were two broadly classed eco-regions within our domain, with CH 4 from the tundra region accounting for over half of the overall budget, despite only representing 18 % of the total surface area. Boreal regions, which cover a large part of the study region, accounted for the remainder of the emissions. Simple multiple linear regression analysis revealed that overall, CH 4 flux were largely driven by soil temperature and elevation. In regions specifically dominated by wetlands, soil temperature and moisture at 10 cm depth were important explanatory variables while in regions that were not wetlands, soil temperature and moisture at 40 cm depth were more important, reflecting the depth at which methanogenesis occurs. Although similar variables have been found in the past to control CH 4 emissions at local scales, this study shows that they can be used to generate a statistical model to estimate the regional scale CH 4 budget.