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Abstract The warming Arctic could accelerate climate change as permafrost soil carbon is released as greenhouse gas emissions from boreal forest, tundra, and wetland ecosystems. Record climate conditions are increasingly common, with the 2023–2024 winter (September–April) documented as having the warmest land surface air temperatures on record for the Arctic region. However, corresponding impacts on ecosystem greenhouse gas fluxes typically take several years to diagnose, creating a knowledge gap between contemporary climate events and these fluxes. Here we synthesized near real-time data from 19 eddy covariance flux tower sites across the Arctic through the summer of 2024. This analysis revealed record net carbon dioxide and methane emissions occurring in winter, coinciding with warm 2023–2024 winter conditions. The increasing recognition of the importance of winter in shaping ecosystem carbon balance is still challenged by the difficulty of collecting data, with far more carbon flux measurements available in summer as compared to year-round. Improving the observation network’s extent and ability to deliver near real-time updates could provide immediate knowledge about the speed and strength of the permafrost carbon feedback to climate change. This increased awareness could help nations adapt their emissions policies aimed to avoid the worst impacts of climate change. 

Abstract Purpose of ReviewWe review how ‘abrupt thaw’ has been used in published studies, compare these definitions to abrupt processes in other Earth science disciplines, and provide a definitive framework for how abrupt thaw should be used in the context of permafrost science. Recent FindingsWe address several aspects of permafrost systems necessary for abrupt thaw to occur and propose a framework for classifying permafrost processes as abrupt thaw in the future. Based on a literature review and our collective expertise, we propose that abrupt thaw refers to thaw processes that lead to a substantial persistent environmental change within a few decades. Abrupt thaw typically occurs in ice-rich permafrost but may be initiated in ice-poor permafrost by external factors such as hydrologic change (i.e., increased streamflow, soil moisture fluctuations, altered groundwater recharge) or wildfire. SummaryPermafrost thaw alters greenhouse gas emissions, soil and vegetation properties, and hydrologic flow, threatening infrastructure and the cultures and livelihoods of northern communities. The term ‘abrupt thaw’ has emerged in scientific discourse over the past two decades to differentiate processes that rapidly impact large depths of permafrost, such as thermokarst, from more gradual, top-down thaw processes that impact centimeters of near-surface permafrost over years to decades. However, there has been no formal definition for abrupt thaw and its use in the scientific literature has varied considerably. Our standardized definition of abrupt thaw offers a path forward to better understand drivers and patterns of abrupt thaw and its consequences for global greenhouse gas budgets, impacts to infrastructure and land-use, and Arctic policy- and decision-making. 

Abstract At the edge of alpine and Arctic ecosystems all over the world, a transition zone exists beyond which it is either infeasible or unfavorable for trees to exist, colloquially identified as the treeline. We explore the possibility of a thermodynamic basis behind this demarcation in vegetation by considering ecosystems as open systems driven by thermodynamic advantage—defined by vegetation’s ability to dissipate heat from the earth’s surface to the air above the canopy. To deduce whether forests would be more thermodynamically advantageous than existing ecosystems beyond treelines, we construct and examine counterfactual scenarios in which trees exist beyond a treeline instead of the existing alpine meadow or Arctic tundra. Meteorological data from the Italian Alps, United States Rocky Mountains, and Western Canadian Taiga-Tundra are used as forcing for model computation of ecosystem work and temperature gradients at sites on both sides of each treeline with and without trees. Model results indicate that the alpine sites do not support trees beyond the treeline, as their presence would result in excessive CO $$_2$$ 2 loss and extended periods of snowpack due to temperature inversions (i.e., positive temperature gradient from the earth surface to the atmosphere). Further, both Arctic and alpine sites exhibit negative work resulting in positive feedback between vegetation heat dissipation and temperature gradient, thereby extending the duration of temperature inversions. These conditions demonstrate thermodynamic infeasibility associated with the counterfactual scenario of trees existing beyond a treeline. Thus, we conclude that, in addition to resource constraints, a treeline is an outcome of an ecosystem’s ability to self-organize towards the most advantageous vegetation structure facilitated by thermodynamic feasibility. 

Abstract Climate change is rapidly altering composition, structure, and functioning of the boreal biome, across North America often broadly categorized into ecoregions. The resulting complex changes in different ecoregions present a challenge for efforts to accurately simulate carbon dioxide (CO₂) and energy exchanges between boreal forests and the atmosphere with terrestrial ecosystem models (TEMs). Eddy covariance measurements provide valuable information for evaluating the performance of TEMs and guiding their development. Here, we compiled a boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from eight black spruce (Picea mariana)-dominated, mature forest stands. The eight forest stands, located in six boreal ecoregions of North America, differ in stand characteristics, disturbance history, climate, permafrost conditions and soil properties. By compiling various data streams, the benchmarking dataset comprises data to parameterize, force, and evaluate TEMs. Specifically, it includes half-hourly, gap-filled meteorological forcing data, ancillary data essential for model parameterization, and half-hourly, gap-filled or partitioned component flux data on CO₂(net ecosystem production, gross primary production [GPP], and ecosystem respiration [ER]) and energy (latent [LE] and sensible heat [H]) and their daily aggregates screened based on half-hourly gap-filling quality criteria. We present a case study with the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) to: (1) demonstrate the utility of our dataset to benchmark TEMs and (2) provide guidance for model development and refinement. Model skill was evaluated using several statistical metrics and further examined through the flux responses to their environmental controls. Our results suggest that CLASSIC tended to overestimate GPP and ER among all stands. Model performance regarding the energy fluxes (i.e., LE and H) varied greatly among the stands and exhibited a moderate correlation with latitude. We identified strong relationships between simulated fluxes and their environmental controls except for H, thus highlighting current strengths and limitations of CLASSIC. 

Abstract Northern peatlands are a globally significant source of methane (CH₄), and emissions are projected to increase due to warming and permafrost loss. Understanding the microbial mechanisms behind patterns in CH₄production in peatlands will be key to predicting annual emissions changes, with stable carbon isotopes (δ¹³C‐CH₄) being a powerful tool for characterizing these drivers. Given that δ¹³C‐CH₄is used in top‐down atmospheric inversion models to partition sources, our ability to model CH₄production pathways and associated δ¹³C‐CH₄values is critical. We sought to characterize the role of environmental conditions, including hydrologic and vegetation patterns associated with permafrost thaw, on δ¹³C‐CH₄values from high‐latitude peatlands. We measured porewater and emitted CH₄stable isotopes, pH, and vegetation composition from five boreal‐Arctic peatlands. Porewater δ¹³C‐CH₄was strongly associated with peatland type, with δ¹³C enriched values obtained from more minerotrophic fens (−61.2 ± 9.1‰) compared to permafrost‐free bogs (−74.1 ± 9.4‰) and raised permafrost bogs (−81.6 ± 11.5‰). Variation in porewater δ¹³C‐CH₄was best explained by sedge cover, CH₄concentration, and the interactive effect of peatland type and pH (r² = 0.50,p < 0.001). Emitted δ¹³C‐CH₄varied greatly but was positively correlated with porewater δ¹³C‐CH₄. We calculated a mixed atmospheric δ¹³C‐CH₄value for northern peatlands of −65.3 ± 7‰ and show that this value is more sensitive to landscape drying than wetting under permafrost thaw scenarios. Our results suggest northern peatland δ¹³C‐CH₄values are likely to shift in the future which has important implications for source partitioning in atmospheric inversion models. 

This data set is associated with Curasi et al., 2022 (https://doi.org/10.1088/1748-9326/ac6005) it includes detailed survey data for Eriophorum vaginatum spanning sites in the Alaskan, Canadian, and Russian Arctic. The data set includes detailed surveys of tussock density and diameter, shrub basal diameter and abundance, the relationship between tussock size and mass, the relationship between shrub size and mass, tussock chemical properties, soil properties, and bulk density. It also includes outputs and projections from a tussock machine learning ecological niche model. This data was collected to characterize the distribution of this foundation species across the landscape and illustrate its role in the ecosystem. The data collection methods and subsequent analysis are described in detail in https://doi.org/10.1088/1748-9326/ac6005 

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). 

Abstract Arctic wetlands are known methane (CH₄) emitters but recent studies suggest that the Arctic CH₄sink strength may be underestimated. Here we explore the capacity of well-drained Arctic soils to consume atmospheric CH₄using >40,000 hourly flux observations and spatially distributed flux measurements from 4 sites and 14 surface types. While consumption of atmospheric CH₄occurred at all sites at rates of 0.092 ± 0.011 mgCH₄ m⁻² h⁻¹(mean ± s.e.), CH₄uptake displayed distinct diel and seasonal patterns reflecting ecosystem respiration. Combining in situ flux data with laboratory investigations and a machine learning approach, we find biotic drivers to be highly important. Soil moisture outweighed temperature as an abiotic control and higher CH₄uptake was linked to increased availability of labile carbon. Our findings imply that soil drying and enhanced nutrient supply will promote CH₄uptake by Arctic soils, providing a negative feedback to global climate change.