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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. 

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. 

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. 

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 Science, engineering, and society increasingly require integrative thinking about emerging problems in complex systems, a notion referred to as convergence science. Due to the concurrent pressures of two main stressors—rapid climate change and industrialization, Arctic research demands such a paradigm of scientific inquiry. This perspective represents a synthesis of a vision for its application in Arctic system studies, developed by a group of disciplinary experts consisting of social and earth system scientists, ecologists, and engineers. Our objective is to demonstrate how convergence research questions can be developed via a holistic view of system interactions that are then parsed into material links and concrete inquiries of disciplinary and interdisciplinary nature. We illustrate the application of the convergence science paradigm to several forms of Arctic stressors using the Yamal Peninsula of the Russian Arctic as a representative natural laboratory with a biogeographic gradient from the forest‐tundra ecotone to the high Arctic. 

Abstract AimClimate change regulates autumn leaf senescence date (LSD), exhibiting a strong phenological control of plant carbon uptake. Unlike the delaying effect of daily mean temperature (T_mean) on LSD, the impact of warming asymmetry in daytime and nighttime, as evidenced by variations of the diurnal temperature range (DTR), remains elusive. The objectives of this study were to investigate physiological and ecological impacts of DTR on LSD using long‐termin situobservations and to predict the future trends of LSD under warming. LocationEurope. Time period1950–2015. Major taxa studiedPlant phenology. MethodsWe used partial correlation analysis, multiple linear regression and ridge regression to explore the impacts of DTR on LSD. To quantify the importance of potential drivers of LSD, we trained random forest models and applied the SHapley Additive exPlanations method to isolate the marginal contributions of each predictor on LSD. For LSD modelling and projection, we first evaluated two temperature‐driven LSD models [i.e., cooling‐degree‐day (CDD, without DTR effect) and day–night‐temperature CDD (DN_CDD, with DTR effect)], then applied them to predict future LSDs. ResultsWe found that observational increases inT_meanand DTR had contrasting effects on LSD. IncreasedT_meandelayed the LSD, whereas larger DTR overall had an advancing effect. Considering the DTR effect, theT_meansensitivity of LSD was 14% lower than presently estimated (2.4 vs. 2.8 days °C⁻¹). Warming asymmetry‐related drought stress and plant functional traits (i.e., plant isohydricity and water‐use efficiency) potentially explained the advancing effect of DTR on LSD. We found that current projections of future LSD are overestimated because the DTR effect is discounted, suggesting the need for an adequate understanding of how plant phenology responds to warming asymmetry. Main conclusionsOur findings highlight the importance of DTR in controlling LSD variations with an advancing‐dominant effect and call for the improvement of phenology modelling incorporating the DTR effect. Given that DTR showed a globally narrowing trend over the last several decades, more efforts are needed to understand the potential ecological impacts of warming asymmetry and vegetation response to climate change.