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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. Plain Language Summary 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. Key Points 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

Fires are critical pathways of carbon loss from boreal forest soils, whereas microbial communities form equally critical controls over carbon accumulation between fires. We used a chronosequence in Alaska to test Read's hypothesis that arbuscular mycorrhizal fungi should dominate ecosystems with low accumulation of surface litter, and ectomycorrhizal fungi should proliferate where organic horizons are well-developed. This pattern is expected because ectomycorrhizal fungi display a greater capacity to mineralize organic compounds than do arbuscular mycorrhizal fungi. The sites were located in upland forests near Delta Junction, Alaska, and represent stages at 3, 15, 45, and 80 years following fire. Soil organic matter accumulated 2.8-fold over time. Fire did not noticeably reduce the abundance of arbuscular mycorrhizal fungi. In contrast, ectomycorrhizal colonization required up to 15 years to return to pre-fire levels. As a result, dominant mycorrhizal groups shifted from arbuscular to ectomycorrhizal fungi as succession progressed. Bacterial functional diversity was greatest in the oldest sites. Altogether, microbes that can mineralize organic compounds (i.e., ectomycorrhizae and bacteria) recovered more slowly than those that cannot (i.e., arbuscular mycorrhizae). Potential net N mineralization and standing pools of ammonium-N were relatively low in the youngest site. In addition, glomalin stocks were positively correlated with arbuscular mycorrhizal hyphal length, peaking early in the chronosequence. Our results indicate that microbial succession may influence soil carbon and nitrogen dynamics in the first several years following fire, by augmenting carbon storage in glomalin while inhibiting mineralization of organic compounds.

Caribou are an integral component of high-latitude ecosystems and represent a major subsistence food source for many northern people. The availability and quality of winter habitat is critical to sustain these caribou populations. Caribou commonly use older spruce woodlands with adequate terrestrial lichen, a preferred winter forage, in the understory. Changes in climate and fire regime pose a significant threat to the long-term sustainability of this important winter habitat. Computer simulations performed with a spatially explicit vegetation succession model (ALFRESCO) indicate that changes in the frequency and extent of fire in interior Alaska may substantially impact the abundance and quality of winter habitat for caribou. We modeled four different fire scenarios and tracked the frequency, extent, and spatial distribution of the simulated fires and associated changes to vegetation composition and distribution. Our results suggest that shorter fire frequencies (i.e., less time between recurring fires) on the winter range of the Nelchina caribou herd in eastern interior Alaska will result in large decreases of available winter habitat, relative to that currently available, in both the short and long term. A 30% shortening of the fire frequency resulted in a 3.5-fold increase in the area burned annually and an associated 41% decrease in the amount of spruce-lichen forest found on the landscape. More importantly, simulations with more frequent fires produced a relatively immature forest age structure, compared to that which currently exists, with few stands older than 100 years. This age structure is at the lower limits of stand age classes preferred by caribou from the Nelchina herd. Projected changes in fire regime due to climate warming and/or additional prescribed burning could substantially alter the winter habitat of caribou in interior Alaska and lead to changes in winter range use and/or population dynamics.

Wildfires in boreal forests release substantial amounts of carbon into the atmosphere. However, current land-surface models are limited in their representation of fire processes, including their ignition and spread. This study thus developed FireDL, a novel data-driven machine-learning model for the prediction of natural wildfires, and combined it with a land-surface model to better understand the impact of fire on carbon fluxes. FireDL has a two-stage deep learning structure that sequentially combines a long short-term memory (LSTM) algorithm and an artificial neural network (ANN). Preliminary random forest analysis identified fire duration as an important factor in predicting the burned area. Thus, in FireDL, the LSTM algorithm was employed to predict fire occurrence and duration, utilizing lightning, vegetation, and climate datasets. Subsequently, the ANN predicted the total burned area using the LTSM-derived fire duration predictions and climate datasets as input. FireDL produced a robust performance in predicting large fires (>10 000 ha), achieving a correlation coefficient of 0.72. The daily-scaled burned area predictions derived from FireDL were integrated into the Community Land Model version 5—Biogeochemistry (CLM5-BGC) to produce CLM5-BGC-FireDL. This integration considerably improved carbon emission estimations. Notably, the total net ecosystem exchange (NEE) estimated using CLM5-BGC-FireDL in 2019, the year with the highest recorded burned area during our study, was twice that estimated using the standard CLM5-BGC. Discrepancies in the NEE can significantly influence atmospheric CO2 levels, highlighting the importance of our fire prediction model in forecasting the burned area and carbon emissions. The use of FireDL with future climate scenarios is thus anticipated to yield valuable insights into ecosystem management and climate change mitigation strategies.

Wildland fires exert substantial impacts on tundra ecosystems of the high northern latitudes (HNL), ranging from biogeochemical impact on climate system to habitat suitability for various species. Cloud-to-ground (CG) lightning is the primary ignition source of wildfires. It is critical to understand mechanisms and factors driving lightning strikes in this cold, treeless environment to support operational modeling and forecasting of fire activity. Existing studies on lightning strikes primarily focus on Alaskan and Canadian boreal forests where land-atmospheric interactions are different and, thus, not likely to represent tundra conditions. In this study, we designed an empirical-dynamical method integrating Weather Research and Forecast (WRF) simulation and machine learning algorithm to model the probability of lightning strikes across Alaskan tundra between 2001 and 2017. We recommended using Thompson 2-moment and Mellor-Yamada-Janjic schemes as microphysics and planetary boundary layer parameterizations for WRF simulations in the tundra. Our modeling and forecasting test results have shown a strong capability to predict CG lightning probability in Alaskan tundra, with the values of area under the receiver operator characteristics curves above 0.9. We found that parcel lifted index and vertical profiles of atmospheric variables, including geopotential height, dew point temperature, relative humidity, and velocity speed, important in predicting lightning occurrence, suggesting the key role of convection in lightning formation in the tundra. Our method can be applied to data-scarce regions and support future studies of fire potential in the HNL.

Reaching from interior Alaska across Canada to Labrador and Newfoundland, North America's boreal forest is the largest wilderness area left on the planet. It is critical habitat for billions of birds; more than 300 species regularly breed there. After the breeding season, many boreal birds migrate to seasonal habitats across the United States, Mexico, the Caribbean, and Central and South America. This volume brings together new research on boreal bird biology and conservation. It highlights the importance of the region to the global avifauna and to the connectivity between the boreal forest and ecoregions throughout the Americas. The contributions showcase a unique set of perspectives on the migration, wintering ecology, and conservation of bird communities that are tied to the boreal forest in ways that may not have been previously considered.

Variations in the Dole Effect, which have been used to infer past changes in biospheric productivity, are strongly affected by isotopic discrimination in soil respiration. Respiration through the alternative oxidase (AOX) pathway is associated with a higher discrimination than the one associated with the “normal” dark respiration pathway (the cytochrome pathway, COX). However, observations of O2 discrimination and AOX activity in undisturbed natural environments are scarce. In the current study we measured the O2 concentration and stable isotopes in the root zone of tundra, boreal forest and alpine forest soils. To estimate the discrimination from this data, we have performed O2diffusion experiments in gamma‐sterilized soil columns, with varying soil clay content. The discrimination found in the diffusion experiments was independent of clay content, and the value found, 14 ± 2‰, is the same as the one for binary diffusion of O2 in N2, indicating no interaction between the O2 and clay particles. Based on the field and laboratory results, the respiratory discrimination in the soils studied is 15–31‰, with the higher values associated with colder soils. The high discrimination found for cold (<6°C) soils indicates that AOX is a major respiratory pathway in these soils. This relationship between soil temperature and discrimination can be used in future interpretations of Dole Effect variations. Key Points High O2 discrimination was found for cold (<6 degree C) soils respiration No interaction between clay particles and diffusing O2 The results indicate high AOX activity with implications for the Dole Effect

(1) Increased atmospheric nitrogen deposition has shifted plant dominance from ericaceous plants to grass species. To elucidate the reduced competitiveness of heather, we tested the hypothesis that additions of nitrogen reduce the concentrations of phenolics and condensed tannins in ericaceous leaves and retard mycorrhizal colonisation in ericaceous plants. We also tested the negative effects of reduced light intensity on carbon-based secondary compounds and mycorrhizal colonisation in ericaceous plants. (2) We performed a field inventory at three heathland sites in the Netherlands varying in nutrient supply and light intensity. Leaves of ericaceous plants and grasses were collected and analysed for concentrations of tannins, phenolics and nutrients. Similarly, we took root samples to record mycorrhizal colonisation and soil samples to measure the soil mineralisation. In addition, we conducted two-factorial experiments with Calluna vulgaris plants, in which we varied fertiliser and shade levels under greenhouse and field conditions. (3) The field inventory revealed that nitrogen addition and shading both negatively affected the concentration of total phenolics. The total phenolics and condensed tannin concentrations were positively correlated (P < 0.001), but in the field experiment, the condensed tannins were not significantly affected by the treatments. Our results provide the first evidence that the carbon nutrient balance can be used to predict the amount of total phenolics in the dwarf shrub C. vulgaris. (4) In the field experiments, shading of plants resulted in significantly less mycorrhizal colonisation. Only in the greenhouse experiment did addition of nitrogen negatively affect mycorrhizal colonisation. (5) Our results imply that increased atmospheric nitrogen deposition can depress the tannin concentrations in ericaceous plants and the mycorrhizal colonisation in roots, thereby reducing the plants' competitiveness with respect to grasses. Additionally, if ericaceous plants are shaded by grasses that have become dominant due to increased nitrogen supply, these effects will be intensified and competitive replacement will be accelerated.

Shifts in the carbon balance of high-latitude ecosystems could result from differential responses of vegetation and soil processes to changing moisture and temperature regimes and to a lengthening of the growing season. Although shrub expansion and northward movement of treeline should increase carbon inputs, the effects of these vegetation changes on net carbon exchange have not been evaluated. We selected low shrub, tall shrub, and forest tundra sites near treeline in northwestern Alaska, representing the major structural transitions expected in response to warming. In these sites, we measured aboveground net primary production (ANPP) and vegetation and soil carbon and nitrogen pools, and used these data to parameterize the Terrestrial Ecosystem Model. We simulated the response of carbon balance components to air temperature and precipitation trends during 1981-2000. In areas experiencing warmer and dryer conditions, Net Primary Production (NPP) decreased and heterotrophic respiration (R H ) increased, leading to a decrease in Net Ecosystem Production (NEP). In warmer and wetter conditions NPP increased, but the response was exceeded by an increase in R H ; therefore, NEP also decreased. Lastly, in colder and wetter regions, the increase in NPP exceeded a small decline in R H , leading to an increase in NEP. The net effect for the region was a slight gain in ecosystem carbon storage over the 20 year period. This research highlights the potential importance of spatial variability in ecosystem responses to climate change in assessing the response of carbon storage in northern Alaska over the last two decades.