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Abstract Air temperature (Ta), snow depth (Sd), and soil temperature (Tg) are crucial variables for studying the above- and below-ground thermal conditions, especially in high latitudes. However,in-situobservations are frequently sparse and inconsistent across various datasets, with a significant amount of missing data. This study has assembled a comprehensive dataset ofin-situobservations of Ta, Sd, and Tg for the Northern Hemisphere (higher than 30°N latitude), spanning 1960–2021. This dataset encompasses metadata and daily data time series for 27,768, 32,417, and 659 gages for Ta, Sd, and Tg, respectively. Using the ERA5-Land reanalysis data product, we applied deep learning methodology to reconstruct the missing data that account for 54.5%, 59.3%, and 74.3% of Ta, Sd, and Tg daily time series, respectively. The obtained high temporal resolution dataset can be used to better understand physical phenomena and relevant mechanisms, such as the dynamics of land-surface-atmosphere energy exchange, snowpack, and permafrost. 

Abstract Improved modeling of permafrost active layer freeze‐thaw plays a crucial role in understanding the response of the Arctic ecosystem to the accelerating warming trend in the region over the past decades. However, modeling the dynamics of the active layer at diurnal time scale remains challenging using the traditional models of freeze‐thaw processes. In this study, a physically based analytical model is formulated to simulate the thaw depth of the active layer under changing boundary conditions of soil heat flux. Conservation of energy for the active layer leads to a nonlinear integral equation of the thaw depth using a temperature profile approximated from the analytical solution of the heat transfer equation forced by ground heat flux. Temporally variable ground heat flux is estimated using non‐gradient models when field observations are not available. Validation of the proposed model conducted against field data obtained from three Arctic forest and tundra sites demonstrates that the model is able to simulate both thaw depth and soil temperature profiles accurately. The model has the potential to estimate regional variability of the thaw depth for permafrost related applications. 

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 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 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. 

The Maximum Entropy Production (MEP) method for modeling surface energy budget has been developed and validated at local, regional and global scale including the Arctic regions. The MEP model has solid theoretical foundation built on the Bayesian probability theory, information theory, non-equilibrium thermodynamics and boundary layer turbulence theory. Its formulation has advantageous features including closing energy budget at any space-time scales, independence of moisture and temperature gradient, wind speed and surface roughness, and free of tunable empirical parameters. Application of the MEP model has been covering all types of land covers including Arctic permafrost tundra, sea ice and snow surfaces. Recent tests using field experimental observations suggest that the MEP model using fewer input data and model parameters is able to simulate surface energy budget accurately. It is a more efficient alternative to the classical Penman-Monteith model of potential evapotranspiration. The MEP method has potential to influence the study of Arctic water-energy cycles and climate change. 

A physically based model is formulated for the active layer depth of permafrost under changing boundary condition instead of constant boundary condition considered in the traditional Stefan problem. Time-varying ground heat flux is obtained from net radiation and surface temperature using the Maximum Entropy Production (MEP) model as the driver of the active layer melting process. Conductive heat flux at the melting front is approximated in terms of an analytical function of ground heat flux. The simulated active layer depth is in good agreement with the field observations.