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Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20g g€¯S to 20g g€¯N) the spring onset of elevated CH4 emissions starts 3g€¯d earlier, and the CH4 emission season lasts 4g€¯d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at 10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

We describe a new coordination activity and initial results for a global synthesis of eddy covariance CH4 flux measurements.

Tidal wetlands provide valuable ecosystem services, including storing large amounts of carbon. However, the net exchanges of carbon dioxide (CO2) and methane (CH4) in tidal wetlands are highly uncertain. While several biogeochemical models can operate in tidal wetlands, they have yet to be parameterized and validated against high‐frequency, ecosystem‐scale CO2 and CH4 flux measurements across diverse sites. We paired the Cohort Marsh Equilibrium Model (CMEM) with a version of the PEPRMT model called PEPRMT‐Tidal, which considers the effects of water table height, sulfate, and nitrate availability on CO2 and CH4 emissions. Using a model‐data fusion approach, we parameterized the model with three sites and validated it with two independent sites, with representation from the three marine coasts of North America. Gross primary productivity (GPP) and ecosystem respiration (Reco) modules explained, on average, 73% of the variation in CO2 exchange with low model error (normalized root mean square error (nRMSE) <1). The CH4 module also explained the majority of variance in CH4 emissions in validation sites (R2 = 0.54; nRMSE = 1.15). The PEPRMT‐Tidal‐CMEM model coupling is a key advance toward constraining estimates of greenhouse gas emissions across diverse North American tidal wetlands. Further analyses of model error and case studies during changing salinity conditions guide future modeling efforts regarding four main processes: (a) the influence of salinity and nitrate on GPP, (b) the influence of laterally transported dissolved inorganic C on Reco, (c) heterogeneous sulfate availability and methylotrophic methanogenesis impacts on surface CH4 emissions, and (d) CH4 responses to non‐periodic changes in salinity. Plain Language Summary: Tidal wetlands play a crucial role in storing carbon, but there is uncertainty in how they absorb and release carbon dioxide and methane, two of the most important greenhouse gases. Existing models for tidal wetlands have yet to be thoroughly tested using high‐frequency data across diverse sites. The new model presented here was parameterized and validated using 13 years of data from five different tidal wetland sites with representation from the three marine coasts of North America. The model explained on average more than half of the variation in carbon dioxide and methane exchange with low model error across diverse tidal wetlands used in model validation. This is strong performance given the high variation in greenhouse gas fluxes in tidal wetlands, which are known to be biogeochemically complex environments. Our model helps in assessing the greenhouse gas budgets from diverse tidal wetlands and the ability of wetlands to combat climate change. An analysis of model results highlights the need for more research on how tidal pumping and pulse changes in soil and water chemistry, not just at the surface but also across soil layers, influence carbon dioxide and methane production and exchange. Key Points: We present a process‐based modeling approach to estimate greenhouse gas flux in tidal wetlands with representation from the three marine coasts of North AmericaModel validation showed that primary productivity, respiration and methane modules explained >50% of the variation in CO2 and CH4 exchangeThe PEPRMT‐Tidal‐CMEM model is a key advance toward constraining estimates of greenhouse gas emissions across diverse tidal wetlands [ABSTRACT FROM AUTHOR]

Methane (CH4) is a potent greenhouse gas that is both produced and consumed in soils by microbially mediated processes sensitive to soil redox. We evaluated the classical conceptual model of peatland CH4 dynamics—in which the water table position determines the vertical distribution of methanogenesis and methanotrophy—versus an emerging model in which methanogenesis and methanotrophy can both occur throughout the soil profile due to spatially heterogeneous redox and anaerobic CH4 oxidation. We simultaneously measured gross CH4 production and oxidation in situ across a microtopographical gradient in a drained temperate peatland and ex situ along the soil profile, giving us novel insight into the component fluxes of landscape-level net CH4 fluxes. Net CH4 fluxes varied among landforms (p

It has been proposed that the large soil carbon (C) stocks of humid tropical forests result predominantly from C stabilization by reactive minerals, whereas oxygen (O2) limitation of decomposition has received much less attention. We examined the importance of these factors in explaining patterns of C stocks and turnover in the Luquillo Experimental Forest, Puerto Rico, using radiocarbon (14C) measurements of contemporary and archived samples. Samples from ridge, slope, and valley positions spanned three soil orders (Ultisol, Oxisol, Inceptisol) representative of humid tropical forests, and differed in texture, reactive metal content, O2 availability, and root biomass. Mineral-associated C comprised the large majority (87 ± 2%, n = 30) of total soil C. Turnover of most mineral-associated C (66 ± 2%) was rapid (11 to 26 years; mean and SE: 18 ± 3 years) in 25 of 30 soil samples across surface horizons (0-10 and 10-20 cm depths) and all topographic positions, independent of variation in reactive metal concentrations and clay content. Passive C with centennial-millennial turnover was typically much less abundant (34 ± 3%), even at 10-20 cm depths. Carbon turnover times and concentrations significantly increased with concentrations of reduced iron (Fe(II)) across all samples, suggesting that O2 availability may have limited the decomposition of mineral-associated C over decadal scales. Steady-state inputs of mineral-associated C were statistically similar among the three topographic positions, and could represent 10-25% of annual litter production. Observed trends in mineral-associated Δ14C over time could not be fit using the single-pool model used in many other studies, which generated contradictory relationships between turnover and Δ14C as compared with a more realistic two-pool model. The large C fluxes in surface and near-surface soils documented here are supported by findings from paired 14C studies in other types of ecosystems, and suggest that most mineral-associated C cycles relatively rapidly (decadal scales) across ecosystems that span a broad range of state factors.

Cold‐season methane (CH4) emissions may be poorly constrained in wetland models. We examined cold‐season CH4 emissions simulated by 16 models participating in the Global Carbon Project model intercomparison and analyzed temporal and spatial patterns in simulation results using prescribed inundation data for 2000–2020. Estimated annual CH4 emissions from northern (>60°N) wetlands averaged 10.0 ± 5.5 Tg CH4 yr−1. While summer CH4 emissions were well simulated compared to in‐situ flux measurement observations, the models underestimated CH4 during September to May relative to annual total (27 ± 9%, compared to 45% in observations) and substantially in the months with subzero air temperatures (5 ± 5%, compared to 27% in observations). Because of winter warming, nevertheless, the contribution of cold‐season emissions was simulated to increase at 0.4 ± 0.8% decade−1. Different parameterizations of processes, for example, freezing–thawing and snow insulation, caused conspicuous variability among models, implying the necessity of model refinement. Plain Language Summary: Wetlands in the northern high latitudes are a major source of methane (CH4) to the atmosphere, mainly during the warm season. Previously, models have assumed that cold‐season CH4 emissions are low, but recent observations suggest high‐latitude wetlands can be substantial sources even in winter. We compared CH4 emissions simulated by 16 state‐of‐the‐art wetland models, participating in a model intercomparison project with a focus on the cold‐season in northern wetlands. The model simulations indicated that nearly one third of annual emissions were simulated to occur from September to May, and CH4 emissions to the atmosphere were not negligible even under freezing air temperatures, although the results differed greatly among the models. However, field studies suggest cold‐season emissions account for an even larger fraction of annual emissions. These results highlight the contribution of cold‐season emissions to the annual CH4 budget, which future climatic warming is expected to affect severely, and they also show that simulations of cold‐season CH4 emissions from wetlands need to be improved. Key Points: Cold‐season methane (CH4) emissions simulated by 16 Global Carbon Project‐CH4 wetland models were analyzedMost models underestimate the cold‐season emissions in comparison with observational dataFurther model improvement by including cold‐season processes is required to reduce the model bias and uncertainty [ABSTRACT FROM AUTHOR]