Wetland methane (CH4) emissions ($${F}_{{{CH}}_{4}}$$ F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$ F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$ F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$ F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$ F C H 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.

Abstract. Permafrost peatlands store large amounts of carbon potentially vulnerable to decomposition. However, the fate of that carbon in a changing climate remains uncertain in models due to complex interactions among hydrological, biogeochemical, microbial, and plant processes. In this study, we estimated effects of climate forcing biases present in global climate reanalysis products on carbon cycle predictions at a thawing permafrost peatland in subarctic Sweden. The analysis was conducted with a comprehensive biogeochemical model (ecosys) across a permafrost thaw gradient encompassing intact permafrost palsa with an ice core and a shallow active layer, partly thawed bog with a deeper active layer and a variable water table, and fen with a water table close to the surface, each with distinct vegetation and microbiota. Using in situ observations to correct local cold and wet biases found in the Global Soil Wetness Project Phase 3 (GSWP3) climate reanalysis forcing, we demonstrate good model performance by comparing predicted and observed carbon dioxide (CO2) and methane (CH4) exchanges, thaw depth, and water table depth. The simulations driven by the bias-corrected climate suggest that the three peatland types currently accumulate carbon from the atmosphere, although the bog and fen sites can have annual positive radiative forcing impacts due to their higher CH4 emissions. Our simulations indicate that projected precipitation increases could accelerate CH4 emissions from the palsa area, even without further degradation of palsa permafrost. The GSWP3 cold and wet biases for this site significantly alter simulation results and lead to erroneous active layer depth (ALD) and carbon budget estimates. Biases in simulated CO2 and CH4 exchanges from biased climate forcing are as large as those among the thaw stages themselves at a landscape scale across the examined permafrost thaw gradient. Future studies should thus not only focus on changes in carbon budget associated with morphological changes in thawing permafrost, but also recognize the effects of climate forcing uncertainty on carbon cycling.

Projected 21st century changes in high‐latitude climate are expected to have significant impacts on permafrost thaw, which could cause substantial increases in emissions to the atmosphere of carbon dioxide (CO2) and methane (CH4, which has a global warming potential 28 times larger than CO2 over a 100‐year horizon). However, predicted CH4 emission rates are very uncertain due to difficulties in modeling complex interactions among hydrological, thermal, biogeochemical, and plant processes. Methanogenic production pathways (i.e., acetoclastic [AM] and hydrogenotrophic [HM]) and the magnitude of CH4 emissions may both change as permafrost thaws, but a mechanistic analysis of controls on such shifts in CH4 dynamics is lacking. In this study, we reproduced observed shifts in CH4 emissions and production pathways with a comprehensive biogeochemical model (ecosys) at the Stordalen Mire in subarctic Sweden. Our results demonstrate that soil temperature changes differently affect AM and HM substrate availability, which regulates magnitudes of AM, HM, and thereby net CH4 emissions. We predict very large landscape‐scale, vertical, and temporal variations in the modeled HM fraction, highlighting that measurement strategies for metrics that compare CH4 production pathways could benefit from model informed scale of temporal and spatial variance. Finally, our findings suggest that the warming and wetting trends projected in northern peatlands could enhance peatland AM fraction and CH4 emissions even without further permafrost degradation.

Abstract. Methane (CH4) emissions from wetlands are likely increasing and important in global climate change assessments. However, contemporary terrestrial biogeochemical model predictions of CH4 emissions are very uncertain, at least in part due to prescribed temperature sensitivity of CH4 production and emission. While statistically consistent apparent CH4 emission temperature dependencies have been inferred from meta-analyses across microbial to ecosystem scales, year-round ecosystem-scale observations have contradicted that finding. Here, we show that apparent CH4 emission temperature dependencies inferred from year-round chamber measurements exhibit substantial intra-seasonal variability, suggesting that using static temperature relations to predict CH4 emissions is mechanistically flawed. Our model results indicate that such intra-seasonal variability is driven by substrate-mediated microbial and abiotic interactions: seasonal cycles in substrate availability favors CH4 production later in the season, leading to hysteretic temperature sensitivity of CH4 production and emission. Our findings demonstrate the uncertainty of inferring CH4 emission or production rates from temperature alone and highlight the need to represent microbial and abiotic interactions in wetland biogeochemical models.

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The recent rise in atmospheric methane (CH4) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year−1) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.

CH4 emissions from inland waters are highly uncertain in the current global CH4 budget, especially for streams, rivers, and other lotic systems. Previous studies have attributed the strong spatiotemporal heterogeneity of riverine CH4 to environmental factors such as sediment type, water level, temperature, or particulate organic carbon abundance through correlation analysis. However, a mechanistic understanding of the basis for such heterogeneity is lacking. Here, we combine sediment CH4 data from the Hanford reach of the Columbia River with a biogeochemical-transport model to show that vertical hydrologic exchange flows (VHEFs), driven by the difference between river stage and groundwater level, determine CH4 flux at the sediment–water interface. CH4 fluxes show a nonlinear relationship with the magnitude of VHEFs, where high VHEFs introduce O2 into riverbed sediments, which inhibit CH4 production and induce CH4 oxidation, and low VHEFs cause transient reduction in CH4 flux (relative to production) due to reduced advective CH4 transport. In addition, VHEFs lead to the hysteresis of temperature rise and CH4 emissions because high river discharge caused by snowmelt in spring leads to strong downwelling flow that offsets increasing CH4 production with temperature rise. Our findings reveal how the interplay between in-stream hydrologic flux besides fluvial-wetland connectivity and microbial metabolic pathways that compete with methanogenic pathways can produce complex patterns in CH4 production and emission in riverbed alluvial sediments.

Abstract. Methane (CH4) emissions from wetlands are likely increasing and important in global climate change assessments. However, contemporary terrestrial biogeochemical model predictions of CH4 emissions are very uncertain, at least in part due to prescribed temperature sensitivity of CH4 production and emission. While statistically consistent apparent CH4 emission temperature dependencies have been inferred from meta-analyses across microbial to ecosystem scales, year-round ecosystem-scale observations have contradicted that finding. Using flux observations and mechanistic modeling in two heavily studied high-latitude research sites (Stordalen, Sweden, and Utqiaġvik, Alaska, USA), we show here that substrate-mediated hysteretic microbial and abiotic interactions lead to intra-seasonally varying temperature sensitivity of CH4 production and emission. We find that seasonally varying substrate availability drives lower and higher modeled methanogen biomass and activity, and thereby CH4 production, during the earlier and later periods of the thawed season, respectively. Our findings demonstrate the uncertainty of inferring CH4 emission or production from temperature alone, and highlight the need to represent microbial and abiotic interactions in wetland biogeochemical models.