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Accurate measurement of net radiation in the high-latitude Arctic regions is challenging since rain and snow events often introduce substantial measurement errors. To reduce the precipitation-induced measurement errors of downward radiation, customized data-driven methods are developed to reconstruct downward radiative fluxes from the biased radiation measurements. This study uses four years of field data across ten plots covered with forest, trees, and tundra in the Polar Urals from July 2018 to July 2022. Rain and snow on the radiometers absorb and block shortwave radiation and emit longwave radiation, leading to underestimation of downward shortwave and overestimation of downward longwave radiation. Snow causes more errors than rain. Seasonal variation of reconstructed net radiation for three dominant vegetation types indicates that their differences are most pronounced in April and least in September. Furthermore, forest and tree plots consistently exhibit higher magnitudes of net radiation and longer seasons of positive net radiation than tundra plots. This study advances methodologies for reconstructing corrupted net radiation data in the Arctic and offers insights into the variability of net radiation patterns within the forest-tundra ecotone. 

Timberline marks the transitions from continuous forests to sparse forests and tundra landscapes. As the spatial distribution and dynamics of timberline are closely associated with regional energy and carbon balance, mapping timberline is important to a wide range of environmental and ecological studies. However, current timberline delineation approaches remain under-developed. We proposed an automatic timberline delineation method based on a seeded region-growing segmentation technique and satellite-derived products of tree fractional cover. We applied our approach to the West Siberian Plain and Alaska treeline regions as defined by the Circumpolar Arctic Vegetation Map. The results demonstrate the effectiveness of the proposed method for the accurate delineation of the timberlines that spatially align well with very-high-resolution satellite images. Based on the delineated timberlines, we find regional-scale tree encroachment to be not as substantial as previously reported. The proposed approach can be applied to understanding climate-induced forest responses and inform forest management practices. 

Abstract Nenets reindeer pastoralists of Yamal in the Russian Arctic, successfully deal with rapidly changing climate and natural gas industrialization. We present results from our long-term ethnographic study (2001–present) on the adaptive strategies that Nenets nomadic households have employed over time, their tradeoffs, inherent risks, and social implications of these strategies. While some strategies limit the adaptive flexibility of herding, they simultaneously enable agency that keeps Nenets households on the land—critical for maintaining their nomadism. Rapid climate change in the Arctic, which could lead to increased icing of pastures, makes reindeer herding more vulnerable. We examine meteorological data from Yamal to better understand the climatic trends challenging reindeer nomadism. Our analysis is relevant for policymakers through understanding Nenets adaptation and interactions with ecological processes and institutions. 

ArcticDEM provides the public with an unprecedented opportunity to access very high-spatial resolution digital elevation models (DEMs) covering the pan-Arctic surfaces. As it is generated from stereo-pairs of optical satellite imagery, ArcticDEM represents a mixture of a digital surface model (DSM) over a non-ground areas and digital terrain model (DTM) at bare grounds. Reconstructing DTM from ArcticDEM is thus needed in studies requiring bare ground elevation, such as modeling hydrological processes, tracking surface change dynamics, and estimating vegetation canopy height and associated forest attributes. Here we proposed an automated approach for estimating DTM from ArcticDEM in two steps: (1) identifying ground pixels from WorldView-2 imagery using a Gaussian mixture model (GMM) with local refinement by morphological operation, and (2) generating a continuous DTM surface using ArcticDEMs at ground locations and spatial interpolation methods (ordinary kriging (OK) and natural neighbor (NN)). We evaluated our method at three forested study sites characterized by different canopy cover and topographic conditions in Livengood, Alaska, where airborne lidar data is available for validation. Our results demonstrate that (1) the proposed ground identification method can effectively identify ground pixels with much lower root mean square errors (RMSEs) (<0.35 m) to the reference data than the comparative state-of-the-art approaches; (2) NN performs more robustly in DTM interpolation than OK; (3) the DTMs generated from NN interpolation with GMM-based ground masks decrease the RMSEs of ArcticDEM to 0.648 m, 1.677 m, and 0.521 m for Site-1, Site-2, and Site-3, respectively. This study provides a viable means of deriving high-resolution DTM from ArcticDEM that will be of great value to studies focusing on the Arctic ecosystems, forest change dynamics, and earth surface processes. 

Abstract Ground heat flux (G₀) is a key component of the land‐surface energy balance of high‐latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming,G₀is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstructG₀across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using availableG₀data (measured or modeled) for snow‐free period as a reference. When observedG₀is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state‐of‐the‐art uncertainty quantification methods, the developedG₀reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies. 

Nenets reindeer pastoralists on the Yamal Peninsula of the Russian Arctic have demonstrated success in dealing with rapidly changing climatic conditions and the growing built environment associated with the natural gas industry. We pair our observations of a set of 28 Nenets households with hydrometeorological data to better understand the challenges of reindeer nomadism in this time of unprecedented change. We assembled a data set based on our ethnographic work with reindeer herding households beginning in 2001 through 2022, following 28 households at irregular intervals. The source of these data include surveys, participant observation, and digital communication. For this analysis we extracted information and coded variables for: reindeer herd size, migration distances, locations of summer and winter camps, annual frequency of camp movement, changes in migration patterns, and reasons for choice of migration route. These data were combined with relevant weather parameters derived from the ERA5 reanalysis data product for the immediate areas (30 kilometer (km) grid) surrounding summer and winter camps. We conducted a Bayesian logistic regression using the brms package in R Statistical Software (v4.1.2) analyzing factors contributing to ‘change’ or ‘no change’ in migration routes. Five ERA5 climate variables representing summer heating and winter warming and rain on snow (ROS) events were z-score normalized. Year of observation was treated as a factor. Posterior distribution of climate variables showed no discernable effects on household migration decisions. 

Abstract Satellite observations have shown widespread greening during the last few decades over the northern permafrost region, but the impact of vegetation greening on permafrost thermal dynamics remains poorly understood, hindering the understanding of permafrost‐vegetation‐climate feedbacks. Summer surface offset (SSO), defined as the difference between surface soil temperature and near‐surface air temperature in summer (June‐August), is often predicted as a function of surface thermal characteristics for permafrost modeling. Here we examined the impact of leaf area index (LAI), detected by satellite as a proxy to permafrost vegetation dynamics, on SSO variations from 2003 to 2021 across the northern permafrost region. We observed latitude‐ and biome‐dependent patterns of SSO changes, with a pronounced increase in Siberian shrublands and a decrease in Tibetan grasslands. Based on partial correlation and sensitivity analyses, we found a strong LAI signal (∼30% of climatic signal) on SSO with varying elevation‐ and canopy height‐dependent patterns. Positive correlations or sensitivities, that is, increases in LAI lead to higher SSO, were distributed in relatively cold and wet areas. Biophysical effects of permafrost greening on surface albedo, evapotranspiration, and soil moisture (SM) could link the connection between LAI and SSO. Increased LAI substantially reduced surface albedo and enhanced evapotranspiration, influenced energy redistribution, and further controlled interannual variability of SSO. We also found contrasting effects of LAI on surface SM, consequently leading to divergent impacts on SSO. The results offer a fresh perspective on how greening affects the thermal balance and dynamics of permafrost, which is enlightening for improved permafrost projections. 

Abstract This study develops a novel general framework to project the permafrost fate with rigorous uncertainty quantification to assess dominant sources. Borehole temperature records from three sites in the Russian western Arctic are used to constrain the uncertainty of a high‐fidelity freeze‐thaw model. Projections from 9 Global Climate Models (GCM) are stochastically downscaled to generate future trajectories of surface ground heat flux. Under the two emission scenarios SSP2‐4.5 and SSP5‐8.5, the projected average thawing depths by 2100 vary from 0.4 to 14.4 m or 2.1 to 17.7 m, and the increase in the top 10 m average temperature from 2015 to 2100 is 1.2–2.7°C or 1.9–3.0°C. The results show that the freeze‐thaw model uncertainty can sometimes dominate over that of GCM outputs, calling for site‐specific information to improve model accuracy. The framework is applicable for understanding permafrost degradation and related uncertainties at larger scales.