Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems

Abstract

Climate warming is expected to increase global methane (CH4) emissions from wetland ecosystems. Although in situ eddy covariance (EC) measurements at ecosystem scales can potentially detect CH4 flux changes, most EC systems have only a few years of data collected, so temporal trends in CH4 remain uncertain. Here, we use established drivers to hindcast changes in CH4 fluxes (FCH4) since the early 1980s. We trained a machine learning (ML) model on CH4 flux measurements from 22 [methane‐producing sites] in wetland, upland, and lake sites of the FLUXNET‐CH4 database with at least two full years of measurements across temperate and boreal biomes. The gradient boosting decision tree ML model then hindcasted daily FCH4 over 1981–2018 using meteorological reanalysis data. We found that, mainly driven by rising temperature, half of the sites (n = 11) showed significant increases in annual, seasonal, and extreme FCH4, with increases in FCH4 of ca. 10% or higher found in the fall from 1981–1989 to 2010–2018. The annual trends were driven by increases during summer and fall, particularly at high‐CH4‐emitting fen sites dominated by aerenchymatous plants. We also found that the distribution of days of extremely high FCH4 (defined according to the 95th percentile of the daily FCH4 values over a reference period) have become more frequent during the last four decades and currently account for 10–40% of the total seasonal fluxes. The share of extreme FCH4 days in the total seasonal fluxes was greatest in winter for boreal/taiga sites and in spring for temperate sites, which highlights the increasing importance of the non‐growing seasons in annual budgets. Our results shed light on the effects of climate warming on wetlands, which appears to be extending the CH4 emission seasons and boosting extreme emissions.

WetCH4: A Machine Learning-based Upscaling of Methane Fluxes of Northern Wetlands during 2016–2022

“…Compared to other ML algorithms, RF has shown to have better accuracy and lower uncertainty (Irvin et al, 2021;Kim et al, 2020). This approach has been previously applied to upscaling CH4 fluxes in wetlands and rice paddy (Davidson et al, 2017;Feron et al, 2024;McNicol et al, 2023;Ouyang et al, 2023;Peltola et al, 2019).…”

“…Zhang et al, 2020). Specifically, Random Forests (RF) was utilized in regional to global wetland CH4 upscaling (Davidson et al, 2017;Feron et al, 2024;McNicol et al, 2023;Peltola et al, 2019) for the robustness and prevention of overfitting to noise in the input data. For example, Peltola et al (2019) used RF and EC measurements to upscale monthly CH4 fluxes from the Arctic-boreal wetlands at 0.25°-0.5° spatial resolution in 2013-2014.…”

Indicators of Global Climate Change 2023: annual update of key indicators of the state of the climate system and human influence

“…Basu et al, 2022;Peng et al, 2022;Nisbet et al, 2023;Zhang et al, 2023). There is also a possible effect from CO3 fertilisation (Feron et al, 2024;Hu et al, 2023). Such carbon cycle feedbacks are not considered here as they are not a direct emission from human activity, yet they will contribute to greenhouse gas concentration rise, forcing and energy budget changes discussed in the next sections.…”

Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)

“…Ge et al (2024) have recently published a comprehensive review of the role of plants in methane fluxes, showing their influence not only on methane transport but also on methane production and oxidation Feron et al (2024). also show https://doi.org/10.5194/egusphere-2024-1331 Preprint.…”