Climate change effects are strongest in forest ecosystems at the limit of their distributions. Despite the evidence that treelines have shifted upwards by hundreds of meters, knowledge of the associated changes in the stand biomass is limited. In this study, stand biomass and changes to it during the last centuries were estimated along 20 altitudinal transects reaching from the historical (located in the 1950s–1960s) closed forest line up to the current treelines on mountain slopes of three subarctic regions of Russia (Kola Peninsula, Polar Urals, and Putorana Plateau) along a 2200 km long longitudinal gradient. The estimates were based on allometric measurements of 139 trees of five species (Betula pubescens Ehrh. ssp. tortuosa, Pinus sylvestris L., Picea abies Ledeb. ssp. obovata, Larix sibirica Ledeb., and Larix gmelinii Rupr.), stand structure assessments, and the demographic patterns of 9300 trees. During the 20th century, the growth and establishment of trees at the forest–mountain tundra transition (340–500 m width) increased exponentially. Since 1910 forest expansion and densification led to an accumulation of 621–748 tons of aboveground stand biomass per km of treeline length. The accumulation was two times higher below than above the contemporary closed forest line. Data analysis of weather stations showed that the 20th century’s climate had changed in a similar manner in the three study regions, namely vegetation periods became longer (8–10 days) and warmer (0.6–0.9 °C) and more snow fell in the cold period (+10–30%). Our results indicate that regional patterns in stand biomass at the treeline ecotone are primarily related to tree species composition as determined by macroclimatic conditions (e.g., continentality, sunshine hours), snowpack depth, and growing season duration. However, the stand biomass accumulation was driven by increases of early summer temperatures and early winter precipitation during the last century.

This study analyzed summer observations of diurnal and seasonal surface energy budgets across several monitoring sites within the Arctic tundra underlain by permafrost. In these areas, latent and sensible heat fluxes have comparable magnitudes, and ground heat flux enters the subsurface during short summer intervals of the growing period, leading to seasonal thaw. The maximum entropy production (MEP) model was tested as an input and parameter parsimonious model of surface heat fluxes for the simulation of energy budgets of these permafrost‐underlain environments. Using net radiation, surface temperature, and a single parameter characterizing the thermal inertia of the heat exchanging surface, the MEP model estimates latent, sensible, and ground heat fluxes that agree closely with observations at five sites for which detailed flux data are available. The MEP potential evapotranspiration model reproduces estimates of the Penman‐Monteith potential evapotranspiration model that requires at least five input meteorological variables (net radiation, ground heat flux, air temperature, air humidity, and wind speed) and empirical parameters of surface resistance. The potential and challenges of MEP model application in sparsely monitored areas of the Arctic are discussed, highlighting the need for accurate measurements and constraints of ground heat flux.

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.

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.

Ground heat flux ( G ₀ ) 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, G ₀ is 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 reconstruct G ₀ across 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 available G ₀ data (measured or modeled) for snow‐free period as a reference. When observed G ₀ is 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 developed G ₀ reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies.

Altitudinal forest limits are typically climatically dependent, such that increasing temperatures connected to global warming are causing upslope shifts in treeline ecotones worldwide. However, at the local and regional levels, the degree of such a response is dependent on differences in climate, topography and soil features. In recent decades, attempts have been undertaken to estimate tree stand dynamics with remote sensing methods, but their resolution is still too coarse for a precise assessment of stand structural changes, and requires ground-truthing, which is not possible without historical data collected on a single-tree level. We used aerial photos (1962) and satellite images (2021) in combination with historical inventory data to investigate changes in open forest positions at different spatial scales at the eastern macroslope of the Polar Urals over the past 60 years. Additionally, obtained remote sensing data were validated on a single-slope level using tree crown size estimations. Our investigations showed that since 1960 up to present day, the total crown coverage increased from 6.9 to 22.1% within the test polygon. A highly spatially variable upslope advance in an open forest boundary was identified from 1.7 up to 7.1 m in altitude per decade. We revealed that the rate of tree stand transformations was to a great extent depended on the stand density in the 1960s, soil substrate type, moisture regime, slope aspect and inclination. Our results highlighted the necessity to consider the abovementioned factors when trying to predict climate-induced tree distributional responses in subarctic mountain regions.