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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. 

Abstract Previous studies discovered a spatially heterogeneous expansion of Siberian larch into the tundra of the Polar Urals (Russia). This study reveals that the spatial pattern of encroachment of tree stands is related to environmental factors including topography and snow cover. Structural and allometric characteristics of trees, along with terrain elevation and snow depth were collected along a transect 860 m long and 80 m wide. Terrain curvature indices, as representative properties, were derived across a range of scales in order to characterize microtopography. A density-based clustering method was used here to analyze the spatial and temporal patterns of tree stems distribution. Results of the topographic analysis suggest that trees tend to cluster in areas with convex surfaces. The clustering analysis also indicates that the patterns of tree locations are linked to snow distribution. Records from the earliest campaign in 1960 show that trees lived mainly at the middle and bottom of the transect across the areas of high snow depth. As trees expanded uphill following a warming climate trend in recent decades, the high snow depth areas also shifted upward creating favorable conditions for recent tree growth at locations that were previously covered with heavy snow. The identified landscape signatures of increasing tall vegetation, and the effects of microtopography and snow may facilitate the understanding of treeline dynamics at larger scales. 

Abstract Global climate change substantially influences vegetation spring phenology, that is, green‐up date (GUD), in the northern permafrost region. Changes in GUD regulate ecosystem carbon uptake, further feeding back to local and regional climate systems. Extant studies mainly focused on the direct effects of climate factors, such as temperature, precipitation, and insolation; however, the responses of GUD to permafrost degradation caused by warming (i.e., indirect effects) remain elusive yet. In this study, we examined the impacts of permafrost degradation on GUD by analyzing the long‐term trend of satellite‐based GUD in relation to permafrost degradation measured by the start of thaw (SOT) and active layer thickness (ALT). We found significant trends of advancing GUD, SOT, and thickening ALT (p < 0.05), with a spatially averaged slope of −2.1 days decade⁻¹, −4.1 days decade⁻¹, and +1.1 cm decade⁻¹, respectively. Using partial correlation analyses, we found more than half of the regions with significantly negative correlations between spring temperature and GUD became nonsignificant after considering permafrost degradation. GUD exhibits dominant‐positive (37.6% vs. 0.6%) and dominant‐negative (1.8% vs. 35.1%) responses to SOT and ALT, respectively. Earlier SOT and thicker ALT would enhance soil water availability, thus alleviating water stress for vegetation green‐up. Based on sensitivity analyses, permafrost degradation was the dominant factor controlling GUD variations in 41.7% of the regions, whereas only 19.6% of the regions were dominated by other climatic factors (i.e., temperature, precipitation, and insolation). Our results indicate that GUDs were more sensitive to permafrost degradation than direct climate change in spring among different vegetation types, especially in high latitudes. This study reveals the significant impacts of permafrost degradation on vegetation GUD and highlights the importance of permafrost status in better understanding spring phenological responses to future climate change. 

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.