PNNL

Abstract

Climate warming is expected to increase global methane (CH4) emissions from ecosystems in a similar hydrological regime. Although in situ eddy covariance (EC) measurements at ecosystems scale could detect CH4 flux changes, most EC systems have only a few years of data collected such that 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 wetland, upland and lake sites of the FLUXNETCH4 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 from atmospheric conditions from reanalysis. We found that, mainly driven by the rising temperature, half of the sites (n=11) showed significant increases in annual, seasonal and extreme FCH4. 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 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 spring for temperate sites, which highlights the growing importance of the shoulder seasons in annual budgets. Our results shed light on the effects of climate warming on wetland, which appears to be extending the CH4 emission seasons and boosting extreme emissions.