Understanding changes in snow distribution in permafrost ecosystems is fundamental to predicting their response to future climate change. The expansion of tall shrubs into tundra ecosystems can trap snow and insulate permafrost ecosystems during the winter, but the overall insulation effect is dependent upon many ecosystem properties. To study shrub-snow-ground interactions, small temperature sensors were deployed at two research sites on the Seward Peninsula of Alaska, USA, during the 2019-2020 winter. Snow temperatures were used to extrapolate multiple metrics, including freezing n-factors, the snow insulation effect, snow cover duration, and the length of the snowmelt period. Statistical and spatial analysis showed that shrub patches were a dominant control on all snow metrics. Within shrub patches, average ground temperatures were 2.1 degrees C warmer, snow persisted 50 days longer, snow insulation was double, and a longer, later spring snowmelt period occurred compared to nonshrubby areas. Site-level differences contributed relatively little to variation in snow metrics, indicating that shrub presence is an overarching driver of snow-ground interactions at the locations examined. Shrub expansion, which is anticipated under climate change, will strongly impact future permafrost distribution and Arctic energy, water, and carbon cycles through snow-shrub-ground feedbacks.

In permafrost regions, the strong spatial and temporal variability in soil temperature cannot be explained by the weather forcing only. Understanding the local heterogeneity of soil thermal dynamics and their controls is essential to understand how permafrost systems respond to climate change and to develop process-based models or remote sensing products for predicting soil temperature. In this study, we analyzed soil temperature dynamics and their controls in a discontinuous permafrost region on the Seward Peninsula, Alaska. We acquired one-year temperature time series at multiple depths (at 5 or 10 cm intervals up to 85 cm depth) at 45 discrete locations across a 2.3 km ^2 watershed. We observed a larger spatial variability in winter temperatures than that in summer temperatures at all depths, with the former controlling most of the spatial variability in mean annual temperatures. We also observed a strong correlation between mean annual ground temperature at a depth of 85 cm and mean annual or winter season ground surface temperature across the 45 locations. We demonstrate that soils classified as cold, intermediate, or warm using hierarchical clustering of full-year temperature data closely match their co-located vegetation (graminoid tundra, dwarf shrub tundra, and tall shrub tundra, respectively). We show that the spatial heterogeneity in soil temperature is primarily driven by spatial heterogeneity in snow cover, which induces variable winter insulation and soil thermal diffusivity. These effects further extend to the subsequent summer by causing variable latent heat exchanges. Finally, we discuss the challenges of predicting soil temperatures from snow depth and vegetation height alone by considering the complexity observed in the field data and reproduced in a model sensitivity analysis.

Spring snowmelt occurs for a short duration on an annual time scale, but their timings considerably affect the carbon and hydrological cycle in high-latitude ecosystems. Here, we developed a simple snowmelt model, treating the ecosystem surface as a bulk-surface layer. Energy fluxes across this bulk surface and the snow-soil boundary determine snow temperature and the energy utilized for snowmelt. Parameterizing the bulk surface using decade-long eddy covariance site data from two Alaskan open black spruce forests offered an opportunity to quantitatively evaluate meteorological drivers affecting snowmelt timings without the needs for detailed canopy information. The sensitivity analysis suggested that the total snowfall on the forest floor, ranging from 0.35 m in 2016 to about 1 m in 2018 and 2020, was the most crucial driver for snowmelt timing. This factor accounted for a 10-day difference in the interannual variations in snow disappearance dates. The importance of the snowfall varied from year to year, and in 2013, the late snowmelt was characterized by low air temperatures, which increased sensible heat loss from the snowpack. The importance of atmospheric radiation was revealed in relatively warm years, such as 2016 and 2019. Our modeling approach necessitates adjusting one empirical parameter that reflects the heat conductivity from the bulk surface to the snow, based on observations. Nevertheless, despite this need for adjustment, the bulk-surface approach helps identify important meteorological drivers underlying observed snowmelt within a simple theoretical framework. It is essential to understand why and how spring snowmelt occurs in boreal forests because of its impact on both the biological and hydrological processes in the ecosystem. However, meteorological drivers related to snowmelt often change simultaneously, which makes it challenging to determine which single driver is vital for snowmelt. To address this challenge, we constructed a simple snowmelt model that treats the ecosystem surface as a single bulk layer. The simple model bypasses the needs for detailed canopy information and is easily constrained by observed data. Applying the model to decadal observation data from Alaskan boreal forests, we found that snowfall, air temperature, and atmospheric radiation were important meteorological drivers for explaining interannual variations in snowmelt timing. Each of them explained about 1-2 weeks of snowmelt disappearance dates. We also demonstrated that the model helped explain why the snowmelt was late in 2013 and early in 2016 and 2019 and why snow disappearance dates differed between study sites. While a simple model has its limitations, it can be beneficial for understanding snowmelt characteristics within a simple theoretical framework. A model-based approach was introduced to delineate the roles of meteorological drivers on interannual variations in snowmelt timing Snowfall was the most important driver of the interannual variation in snowmelt timing followed by air temperature and atmospheric radiation Late snowmelt in 2013 was attributed to low air temperature, increasing sensible heat loss from the snowpack

Background The Drought Code (DC) of the Canadian Fire Weather Index System (CFWIS) has been intuitively regarded by fire managers in Alaska, USA, as poorly representing the moisture content in the forest floor in lowland taiga forests on permafrost soils. Aims The aim of this study was to evaluate the DC using its own framework of water balance as cumulative additions of daily precipitation and substractions of actual evaporation. Methods We used eddy covariance measurements (EC) from three flux towers in Interior Alaska as a benchmark of natural evaporation. Key results The DC water balance model overpredicted drought for all 14 site-years that we analysed. Errors in water balance cumulated to 109 mm by the end of the season, which was 54% of the soil water storage capacity of the DC model. Median daily water balance was 6.3 times lower than that measured by EC. Conclusions About half the error in the model was due to correction of precipitation for canopy effects. The other half was due to dependence of the actual evaporation rate on the proportional ‘fullness’ of soil water storage in the DC model. Implications Fire danger situational awareness is improved by ignoring the DC in the CFWIS for boreal forests occurring on permafrost.

Measuring soil and snow temperature with high vertical and lateral resolution is critical for advancing the predictive understanding of thermal and hydro-biogeochemical processes that govern the behavior of environmental systems. Vertically resolved soil temperature measurements enable the estimation of soil thermal regimes, frozen-/thawed-layer thickness, thermal parameters, and heat and/or water fluxes. Similarly, they can be used to capture the snow depth and the snowpack thermal parameters and fluxes. However, these measurements are challenging to acquire using conventional approaches due to their total cost, their limited vertical resolution, and their large installation footprint. This study presents the development and validation of a novel distributed temperature profiling (DTP) system that addresses these challenges. The system leverages digital temperature sensors to provide unprecedented, finely resolved depth profiles of temperature measurements with flexibility in system geometry and vertical resolution. The integrated miniaturized logger enables automated data acquisition, management, and wireless transfer. A novel calibration approach adapted to the DTP system confirms the factory-assured sensor accuracy of ±0.1 ∘C and enables improving it to ±0.015 ∘C. Numerical experiments indicate that, under normal environmental conditions, an additional error of 0.01 % in amplitude and 70 s time delay in amplitude for a diurnal period can be expected, owing to the DTP housing. We demonstrate the DTP systems capability at two field sites, one focused on understanding how snow dynamics influence mountainous water resources and the other focused on understanding how soil properties influence carbon cycling. Results indicate that the DTP system reliably captures the dynamics in snow depth and soil freezing and thawing depth, enabling advances in understanding the intensity and timing in surface processes and their impact on subsurface thermohydrological regimes. Overall, the DTP system fulfills the needs for data accuracy, minimal power consumption, and low total cost, enabling advances in the multiscale understanding of various cryospheric and hydro-biogeochemical processes.

The spatial distribution of snow plays a vital role in sub-Arctic and Arctic climate, hydrology, and ecology due to its fundamental influence on the water balance, thermal regimes, vegetation, and carbon flux. However, the spatial distribution of snow is not well understood, and therefore, it is not well modeled, which can lead to substantial uncertainties in snow cover representations. To capture key hydro-ecological controls on snow spatial distribution, we carried out intensive field studies over multiple years for two small (2017–2019; ∼ 2.5 km2) sub-Arctic study sites located on the Seward Peninsula of Alaska. Using an intensive suite of field observations (> 22 000 data points), we developed simple models of the spatial distribution of snow water equivalent (SWE) using factors such as topographic characteristics, vegetation characteristics based on greenness (normalized different vegetation index, NDVI), and a simple metric for approximating winds. The most successful model was random forest, using both study sites and all years, which was able to accurately capture the complexity and variability of snow characteristics across the sites. Approximately 86 % of the SWE distribution could be accounted for, on average, by the random forest model at the study sites. Factors that impacted year-to-year snow distribution included NDVI, elevation, and a metric to represent coarse microtopography (topographic position index, TPI), while slope, wind, and fine microtopography factors were less important. The characterization of the SWE spatial distribution patterns will be used to validate and improve snow distribution modeling in the Department of Energy's Earth system model and for improved understanding of hydrology, topography, and vegetation dynamics in the sub-Arctic and Arctic regions of the globe.

Abstract. The spatial distribution of snow plays a vital role in Arctic climate, hydrology, and ecology due to its fundamental influence on the water balance, thermal regimes, vegetation, and carbon flux. However, for earth system modelling, the spatial distribution of snow is not well understood, and therefore, it is not well modeled, which can lead to substantial uncertainties in snow cover representations. To capture key hydro-ecological controls on snow spatial distribution, we carried out intensive field studies over multiple years for two small (2017–2019, ~2.5 km2) sub-Arctic study sites located on the Seward Peninsula of Alaska. Using an intensive suite of field observations (> 22,000 data points), we developed simple models of spatial distribution of snow water equivalent (SWE) using factors such as topographic characteristics, vegetation characteristics based on greenness (normalized different vegetation index, NDVI), and a simple metric for approximating winds. The most successful model was the random forest using both study sites and all years, which was able to accurately capture the complexity and variability of snow characteristics across the sites. Approximately 86 % of the SWE distribution could be accounted for, on average, by the random forest model at the study sites. Factors that impacted year-to-year snow distribution included NDVI, elevation, and a metric to represent coarse microtopography (topographic position index, or TPI), while slope, wind, and fine microtopography factors were less important. The models were used to predict SWE at the locations through the study area and for all years. The characterization of the SWE spatial distribution patterns and the statistical relationships developed between SWE and its impacting factors will be used for the improvement of snow distribution modelling in the Department of Energy’s earth system model, and to improve understanding of hydrology, topography, and vegetation dynamics in the Arctic and sub-Arctic regions of the globe.

Spatiotemporal variation in ground-surface displacement caused by ground freeze–thaw and thermokarst is critical information to understand changes in the permafrost ecosystem. Measurement of ground displacement, especially in the disturbed ground underlain by ice-rich permafrost, is important to estimate the rate of permafrost and carbon loss. We conducted high-precision global navigation satellite system (GNSS) positioning surveys to measure the surface displacements of tundra in northern Alaska, together with maximum thaw depth (TD) and surface moisture measurements from 2017 to 2019. The measurements were performed along two to three 60–200 m transects per site with 1–5 m intervals at the three areas. The average seasonal thaw settlement (STS) at intact tundra sites ranged 5.8–14.3 cm with a standard deviation range of 2.1–3.3 cm. At the disturbed locations, averages and variations in STS and the maximum thaw depth were largest in all observed years and among all sites. The largest seasonal and interannual subsidence (44 and 56 cm/year, respectively) were recorded at points near troughs of degraded ice-wedge polygons or thermokarst lakes. Weak or moderate correlation between STS and TD found at the intact sites became obscure as the thermokarst disturbance progressed, leading to higher uncertainty in the prediction of TD from STS.

We report long-term continuous phenological and sky images taken by time-lapse cameras through the Phenological Eyes Network (http://www.pheno-eye.org. Accessed 29 May 2018) in various ecosystems from the Arctic to the tropics. Phenological images are useful in recording the year-to-year variability in the timing of flowering, leaf-flush, leaf-coloring, and leaf-fall and detecting the characteristics of phenological patterns and timing sensitivity among species and ecosystems. They can also help interpret variations in carbon, water, and heat cycling in terrestrial ecosystems, and be used to obtain ground-truth data for the validation of satellite-observed products. Sky images are useful in continuously recording atmospheric conditions and obtaining ground-truth data for the validation of cloud contamination and atmospheric noise present in satellite remote-sensing data. We have taken sky, forest canopy, forest floor, and shoot images of a range of tree species and landscapes, using time-lapse cameras installed on forest floors, towers, and rooftops. In total, 84 time-lapse cameras at 29 sites have taken 8 million images since 1999. Our images provide (1) long-term, continuous detailed records of plant phenology that are more quantitative than in situ visual phenological observations of index trees; (2) basic information to explain the responsiveness, vulnerability, and resilience of ecosystem canopies and their functions and services to changes in climate; and (3) ground-truthing for the validation of satellite remote-sensing observations.

A fibre-optic DTS (distributed temperature sensing) system using Raman-scattering optical time domain reflectometry was deployed to monitor a boreal forest research site in the interior of Alaska. Surface temperatures range between -40 degrees C in winter and 30 degrees C in summer at this site. In parallel experiments, a fibre-optic cable sensor system (multi-mode, GI50/125, dual core; 3 4 mm), monitored at high resolution, (0.5 m intervals at every 30 min) ground surface temperatures across the landscape. In addition, a high-resolution vertical profile was acquired at one-metre height above the upper subsurface. The total cable ran 2.7 km with about 2.0 km monitoring a horizontal surface path. Sections of the cable sensor were deployed in vertical coil configurations (1.2 m high) to measure temperature profiles from the ground up at 5 mm intervals. Measurements were made continuously over a 2-year interval from October 2012 to October 2014. Vegetation at the site (Poker Flat Research Range) consists primarily of black spruce underlain by permafrost. Land cover types within the study area were classified into six descriptive categories: relict thermokarst lake, open moss, shrub, deciduous forest, sparse conifer forest, and dense conifer forest. The horizontal temperature data exhibited spatial and temporal changes within the observed diurnal and seasonal variations. Differences in snow pack evolution and insulation effects co-varied with the land cover types. The apparatus used to monitor vertical temperature profiles generated high-resolution (ca. 5 mm) data for air column, snow cover, and ground surface. This research also identified several technical challenges in deploying and maintaining a DTS system under subarctic environments.