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In this larger study, we are asking the question: Is old carbon that comprises the bulk of the soil organic matter pool released in response to thawing of permafrost? We are answering this question by using a combination of field and laboratory experiments to measure radiocarbon isotope ratios in soil organic matter, soil respiration, and dissolved organic carbon, in tundra ecosystems. The objective of these proposed measurements is to develop a mechanistic understanding of the SOM sources contributing to C losses following permafrost thawing. We are making these measurements at an established tundra field site near Healy, Alaska in the foothills of the Alaska Range. Field measurements center on a natural experiment where permafrost has been observed to warm and thaw over the past several decades. This area represents a gradient of sites each with a different degree of change due to permafrost thawing. As such, this area is unique for addressing questions at the time and spatial scales relevant for change in arctic ecosystems. 

The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes weekly thaw depth measurements collected from winter warming, summer warming, and control treatment plots at CiPEHR. Additional measurements from on-plot gas flux wells, water table monitoring wells, and off-plot locations are also reported. Note that the experimental warming portion of this experiment concluded in 2022. These data are a continuation of measurements taken at previously warmed plots but plots were not actively manipulated after 2022. 

Abstract. Rapid warming across the Arctic is the primary driver of widespread permafrost thaw, with far-reaching consequences for local ecosystem resilience, the regional carbon budget, and the global climate system. Because permafrost characteristics and vulnerability are tightly linked to land cover, particularly vegetation type and surface properties, understanding these dynamics requires accurate and detailed land cover information. Spatial variation in vegetation cover influences energy balance, snow insulation, and soil moisture, factors that directly affect permafrost stability. Consequently, high-resolution land cover products are essential for assessing the ecological impacts of permafrost thaw and for improving the representation of permafrost-related processes in predictive models. However, many global land cover datasets fail to capture the spatial heterogeneity and fine-scale ecological features that influence permafrost dynamics, while more detailed regional products often lack coverage across broader, continental extents. This gap presents a challenge for large-scale assessments of permafrost vulnerability under accelerating climate change. To create a spatially cohesive land cover map that accurately represents the distribution of ecosystems across the Arctic-Boreal region, we integrated existing global and regional land cover datasets using a workflow including machine learning techniques. This approach seamlessly combines diverse data sources, enhancing representation and accuracy. The resulting map represents high-latitude land cover types at a 1-km spatial resolution, better capturing the spatial heterogeneity of the landscape compared to coarser resolution land surface products, with a total of 35 land cover classes, including 20 forest types (e.g., Larch, Birch, Mixed forests), 6 shrubland classes, and wetlands subdivided into bog, fen, and marsh. To achieve this, we used a global land cover map, the European Space Agency Climate Change Initiative Land Cover data (ESA CCI-LC), as the base map and integrated regional maps across the circumpolar region with finer-resolution land cover information to capture the diversity of land cover types. This approach ensured consistent classification across geopolitical boundaries while incorporating representative vegetation communities at a region-specific level. We show that regional land cover products can be successfully fused to yield a higher-resolution thematic content at the circumpolar scale in comparison to existing global products. The hybrid land cover product can be freely access via https://doi.org/10.5281/zenodo.15231293 (Briones et al 2025). 

Abstract Ecosystems at high latitudes are changing rapidly in response to climate change. To understand changes in carbon fluxes across seasonal to multi‐decadal timescales, long‐term in situ measurements from eddy covariance networks are needed. However, there are large spatiotemporal gaps in the high‐latitude eddy covariance network. Here we used the relative extrapolation error index in machine learning‐based upscaled gross primary production as a measure of network representativeness and as the basis for a network optimization. We show that the relative extrapolation error index has steadily decreased from 2001 to 2020, suggesting diminishing upscaling errors. In experiments where we limit site activity by either setting a maximum duration or by ending measurements at a fixed time those errors increase significantly, in some cases setting the network status back more than a decade. Our experiments also show that with equal site activity across different theoretical network setups, a more spread out design with shorter‐term measurements functions better in terms of larger‐scale representativeness than a network with fewer long‐term towers. We developed a method to select optimized site additions for a network extension, which blends an objective modeling approach with expert knowledge. This method greatly outperforms an unguided network extension and can compensate for suboptimal human choices. For the Canadian Arctic we show several optimization scenarios and find that especially the Canadian high Arctic and north east tundra benefit greatly from addition sites. Overall, it is important to keep sites active and where possible make the extra investment to survey new strategic locations. 

Abstract Arctic permafrost soils store vast amounts of carbon (C)‐rich organic matter that has accumulated due to low temperatures that suppress microbial decomposition. As Arctic warming intensifies, soil microbes become increasingly active, even while plant growth remains dormant. Seasonal decoupling between plant and microbial decomposer growth can accelerate carbon dioxide (CO₂) release from soils, however, most Earth system models underestimate cold‐season C emissions and do not accurately represent the freeze–thaw transitions that govern microbial access to substrates during these critical periods. These model–data mismatches often stem from empirical formulations, such as using a fixed Q₁₀functions to represent microbial respiration, an oversimplification of a complex interplay of temperature, moisture, and substrate diffusion. To address this, we incorporated explicit, temperature‐dependent diffusional constraints on microbial activity, (the Dual Arrhenius Michaelis–Menten (DAMM) model), into the Stoichiometrically Coupled Acclimating Microbe–Plant–Soil (SCAMPS) model which uses the Q₁₀function to represent microbial respiration. We used this enhanced model (SCAMPS_DAMM) to simulate Arctic ecosystem responses to a 50‐year winter warming scenario and compared outcomes to the original SCAMPS framework. While both models predicted overall soil C losses under warming, SCAMPS_DAMM produced more constrained increases in microbial respiration and plant productivity. These differences led to similar total ecosystem C declines but divergent patterns of C and N allocation between plant and soil pools. Thus, incorporating mechanistic constraints on microbial access to substrates through explicit representation of temperature and moisture controls altered model projections of Arctic biogeochemical responses to climate change. 

Abstract. Landscapes are often assumed to be homogeneous when interpreting eddy covariance fluxes, which can lead to biases when gap-filling and scaling up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales. Plant functional types, soil moisture, thaw depth, and microtopography, for example, vary across the landscape and influence net ecosystem exchange (NEE) of carbon dioxide (CO2) and methane (CH4) fluxes. With warming temperatures, Arctic ecosystems are changing from a net sink to a net source of carbon to the atmosphere in some locations, but the Arctic's carbon balance remains highly uncertain. In this study we report results from growing season NEE and CH4 fluxes from an eddy covariance tower in the Yukon–Kuskokwim Delta in Alaska. We used footprint models and Bayesian Markov chain Monte Carlo (MCMC) methods to unmix eddy covariance observations into constituent land-cover fluxes based on high-resolution land-cover maps of the region. We compared three types of footprint models and used two land-cover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. We used artificially created gaps of withheld observations to compare gap-filling performance using our derived land-cover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. We also compared resulting regional carbon budgets when scaling up observations using heterogeneous and homogeneous approaches. Traditional gap-filling methods performed worse at predicting artificially withheld gaps in NEE than those that accounted for heterogeneous landscapes, while there were only slight differences between footprint models and land-cover maps. We identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). We resolved distinct seasonality in tundra growing season NEE fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO2 sink by a factor of 2 and underestimated CH4 emissions by a factor of 2 when compared to scaling with any method that accounts for landscape heterogeneity. We show how Bayesian MCMC, analytical footprint models, and high-resolution land-cover maps can be leveraged to derive detailed land-cover carbon fluxes from eddy covariance time series. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic. 

Boreal and Arctic regions have warmed up to four times quicker than the rest of the planet since the 1970s. As a result, boreal and tundra ecosystems are experiencing more frequent and higher intensity extreme weather events and disturbances, such as wildfires. Yet limitations in ground and satellite data across the Arctic and boreal regions have challenged efforts to track these disturbances at regional scales. In order to effectively monitor the progression and extent of wildfires in the Arctic-boreal zone, it is essential to determine whether burned area (BA) products are accurate representations of BA. Here, we use 12 different datasets together with MODIS active fire data to determine the total yearly BA and seasonal patterns of fires in Arctic-boreal North America and Russia for the years 2001–2020. We found relatively little variability between the datasets in North America, both in terms of total BA and seasonality, with an average BA of 2.55 ± 1.24 (standard deviation) Mha/year for our analysis period, the majority (ca. 41%) of which occurs in July. In contrast, in Russia, there are large disparities between the products—GFED5 produces over four times more BA than GFED4s in southern Siberia. These disparities occur due to the different methodologies used; dNBR (differenced Normalized Burn Ratio) of short-term composites from Landsat images used alongside hotspot data was the most consistently successful in representing BA. We stress caution using GABAM in these regions, especially for the years 2001–2013, as Landsat-7 ETM+ scan lines are mistaken as burnt patches, increasing errors of commission. On the other hand, we highlight using regional products where possible, such as ABoVE-FED or ABBA in North America, and the Talucci et al. fire perimeter product in Russia, due to their detection of smaller fires which are often missed by global products. 

Fire severity is increasing in larch forests of the Siberian Arctic as climate warms, and initial fire impacts on tree demographic processes could be an especially important determinant of long-term forest structure and carbon (C) dynamics. We hypothesized that changes in post-fire larch recruitment impact C accumulation through tree density impacts on understory microclimate and permafrost thaw. We tested these hypotheses by quantifying C pools across a Cajander larch (Larix cajanderi Mayr.) tree density gradient within a fire perimeter near Cherskiy, Russia that burned in ~1940. Across the density gradient, from 2010 - 2017 we inventoried larch trees and harvested ground-layer vegetation to estimate above ground contribution to C pools. We also quantified woody debris C pools and sampled below ground C pools (soil, fine roots, and coarse roots) in the organic + upper mineral soils. Our findings should highlight the potential for a climate-driven increase in fire severity to alter tree recruitment, successional dynamics, and C cycling in Siberian larch forests. 

Fire severity is increasing in larch forests of the Siberian Arctic as climate warms, and initial fire impacts on tree demographic processes could be an especially important determinant of long-term forest structure and carbon (C) dynamics. We hypothesized that changes in post-fire larch recruitment impact C accumulation through tree density impacts on understory microclimate and permafrost thaw. We tested these hypotheses by quantifying C pools across a Cajander larch (Larix cajanderi Mayr.) tree density gradient within a fire perimeter near Cherskiy, Russia that burned in ~1940. Across the density gradient, from 2010 - 2017 we inventoried larch trees and harvested ground-layer vegetation to estimate above ground contribution to C pools. We also quantified woody debris C pools and sampled below ground C pools (soil, fine roots, and coarse roots) in the organic + upper mineral soils. Our findings should highlight the potential for a climate-driven increase in fire severity to alter tree recruitment, successional dynamics, and C cycling in Siberian larch forests. 

Fire severity is increasing in larch forests of the Siberian Arctic as climate warms, and initial fire impacts on tree demographic processes could be an especially important determinant of long-term forest structure and carbon (C) dynamics. We hypothesized that changes in post-fire larch recruitment impact C accumulation through tree density impacts on understory microclimate and permafrost thaw. We tested these hypotheses by quantifying C pools across a Cajander larch (Larix cajanderi Mayr.) tree density gradient within a fire perimeter near Cherskiy, Russia that burned in ~1940. Across the density gradient, from 2010 - 2017 we inventoried larch trees and harvested ground-layer vegetation to estimate above ground contribution to C pools. We also quantified snag and woody debris C pools and sampled below ground C pools (soil, fine roots, and coarse roots) in the organic + upper mineral soils. Our findings should highlight the potential for a climate-driven increase in fire severity to alter tree recruitment, successional dynamics, and C cycling in Siberian larch forests.