B22D-04
An underestimated methane sink in Arctic mineral soils
Tuesday, 15 December 2015: 11:05
2008 (Moscone West)
Youmi Oh1, David Medvigy2, Brandon T Stackhouse3, Maggie Lau1, Tullis C Onstott3, Christian Juncher Jørgensen4, Bo Elberling5, Craig A. Emmerton6 and Vincent L St.Louis7, (1)Princeton University, Princeton, NJ, United States, (2)Princeton University, Geosciences, Princeton, NJ, United States, (3)Princeton Univ, Princeton, NJ, United States, (4)University of Copenhagen, Copenhagen, Denmark, (5)University of Copenhagen, Department for Geosciences and Natural Resource Management, Copenhagen, Denmark, (6)Organization Not Listed, Washington, DC, United States, (7)University of Alberta, Edmonton, AB, Canada
Abstract:
Atmospheric methane has more than doubled since the industrial revolution, yet the sources and sinks are still poorly constrained. Though soil methane oxidation is the largest terrestrial methane sink, it is inadequately represented in current models. We have conducted laboratory analysis of mineral cryosol soils from Axel Heiberg Island in the Canadian high arctic. Microcosm experiments were carried out under varying environmental conditions and used to parameterize methane oxidation models. One-meter long intact soil cores were also obtained from Axel Heiberg Island and analyzed in the laboratory. A controlled core thawing experiment was carried out, and observed methane fluxes were compared to modeled methane fluxes. We find that accurate model simulation of methane fluxes needs to satisfy two requirements:(1) microbial biomass needs to be dynamically simulated, and (2) high-affinity methanotrophs need to be represented. With these 2 features, our model is able to reproduce observed temperature and soil moisture sensitivities of high affinity methanotrophs, which are twice as sensitive to temperature than the low affinity methanotrophs and are active under saturated moisture conditions. The model is also able to accurately reproduce the time rate of change of microbial oxidation of atmospheric methane. Finally, we discuss the remaining biases and uncertainties in the model, and the challenges of extending models from the laboratory scale to the landscape scale.
