Wetland Methane Emissions from the Boreal-Arctic Region: Magnitude, Temporal Dynamics, and Dominant Drivers

K. Yuan¹, F. Li², Q. Zhu³ and M. Chen⁴

¹Lawrence Berkeley National Laboratory, Climate and Ecosystem Sciences Division, Berkeley, CA 94720; 510-334-9800, E-mail: kunxiaojiayuan@lbl.gov
²Stanford University, Department of Earth System Science, Stanford, CA 94305
³Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA 94720
⁴Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706

Methane (CH₄) is the second most important greenhouse gas, with a global warming potential 28-34 times greater than that of CO₂ over a 100-year time horizon. Wetlands are the largest natural source of CH₄ and represent ~20-30% of global CH₄ emissions. Wetland CH₄ emissions from the Boreal-Arctic region are highly sensitive to climate change, where significant warming has been observed recently. However, the magnitude, temporal dynamics, and dominant drivers of Boreal Arctic wetland CH₄ emissions remain uncertain, partially due to limitations in understanding and modeling of wetland CH₄ emission processes, and limited ground observations. In this work, we leveraged causality-guided machine learning, multi-source CH₄ observations (including eddy covariance towers in FLUXNET-CH₄ and chambers), and remote sensing, to estimate wetland CH₄ emissions in the Boreal-Arctic area from 2002 to 2021. Based on the generated regional dataset, we found a significant long-term trend and strong interannual variability of wetland CH₄ emissions dominated by warming and vegetation activities. Furthermore, we benchmarked the performance of 13 bottom-up and 21 top-down models in the current Global Carbon Project. Besides large discrepancies among the models, we also found that most models failed to capture the magnitude and trend of wetland CH₄ emissions in the Boreal-Arctic area during the past two decades.

Figure 1. Inter-annual wetland CH₄ emissions in the Boreal-Arctic estimated by top-down models (blue lines), bottom-up models (gold lines), and this study (the red line).