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Atmospheric methane (CH4) concentrations have gone through rapid changes since the last deglaciation; however, the reasons for abrupt increases around 14,700 and 11,600 years before present (yrs BP) are not fully understood. Concurrent with deglaciation, sea-level rise gradually inundated vast areas of the low-lying Beringian shelf. This transformation of what was once a terrestrial-permafrost tundra-steppe landscape, into coastal, and subsequently, marine environments led to new sources of CH4 from the region to the atmosphere. Here, we estimate, based on an extended geospatial analysis, the area of Beringian coastal wetlands in 1000-year intervals and their potential contribution to northern CH4 flux (based on present day CH4 fluxes from coastal wetland) during the past 20,000 years. At its maximum (∼14,000 yrs BP) we estimated CH4 fluxes from Beringia coastal wetlands to be 3.5 (+4.0/-1.9) Tg CH4 yr−1. This shifts the onset of CH4 fluxes from northern regions earlier, towards the Bølling-Allerød, preceding peak emissions from the formation of northern high latitude thermokarst lakes and wetlands. Emissions associated with the inundation of Beringian coastal wetlands better align with polar ice core reconstructions of northern hemisphere sources of atmospheric CH4 during the last deglaciation, suggesting a connection between rising sea level, coastal wetland expansion, and enhanced CH4 emissions.
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This content will become publicly available on January 1, 2026
WetCH 4 : a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022
Abstract. Wetlands are the largest natural source of methane (CH4) emissions globally. Northern wetlands (>45° N), accounting for 42 % of global wetland area, are increasingly vulnerable to carbon loss, especially as CH4 emissions may accelerate under intensified high-latitude warming. However, the magnitude and spatial patterns of high-latitude CH4 emissions remain relatively uncertain. Here, we present estimates of daily CH4 fluxes obtained using a new machine learning-based wetland CH4 upscaling framework (WetCH4) that combines the most complete database of eddy-covariance (EC) observations available to date with satellite remote-sensing-informed observations of environmental conditions at 10 km resolution. The most important predictor variables included near-surface soil temperatures (top 40 cm), vegetation spectral reflectance, and soil moisture. Our results, modeled from 138 site years across 26 sites, had relatively strong predictive skill, with a mean R2 of 0.51 and 0.70 and a mean absolute error (MAE) of 30 and 27 nmol m−2 s−1 for daily and monthly fluxes, respectively. Based on the model results, we estimated an annual average of 22.8±2.4 Tg CH4 yr−1 for the northern wetland region (2016–2022), and total budgets ranged from 15.7 to 51.6 Tg CH4 yr−1, depending on wetland map extents. Although 88 % of the estimated CH4 budget occurred during the May–October period, a considerable amount (2.6±0.3 Tg CH4) occurred during winter. Regionally, the Western Siberian wetlands accounted for a majority (51 %) of the interannual variation in domain CH4 emissions. Overall, our results provide valuable new high-spatiotemporal-resolution information on the wetland emissions in the high-latitude carbon cycle. However, many key uncertainties remain, including those driven by wetland extent maps and soil moisture products and the incomplete spatial and temporal representativeness in the existing CH4 flux database; e.g., only 23 % of the sites operate outside of summer months, and flux towers do not exist or are greatly limited in many wetland regions. These uncertainties will need to be addressed by the science community to remove the bottlenecks currently limiting progress in CH4 detection and monitoring. The dataset can be found at https://doi.org/10.5281/zenodo.10802153 (Ying et al., 2024).
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- Award ID(s):
- 2011276
- PAR ID:
- 10640629
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Copernicus
- Date Published:
- Journal Name:
- Earth System Science Data
- Volume:
- 17
- Issue:
- 6
- ISSN:
- 1866-3516
- Page Range / eLocation ID:
- 2507 to 2534
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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