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Abstract BackgroundThe increasing size, severity, and frequency of wildfires is one of the most rapid ways climate warming could alter the structure and function of high-latitude ecosystems. Historically, boreal forests in western North America had fire return intervals (FRI) of 70–130 years, but shortened FRIs are becoming increasingly common under extreme weather conditions. Here, we quantified pre-fire and post-fire C pools and C losses and assessed post-fire seedling regeneration in long (> 70 years), intermediate (30–70 years), and short (< 30 years) FRIs, and triple (three fires in < 70 years) burns. As boreal forests store a significant portion of the global terrestrial carbon (C) pool, understanding the impacts of shortened FRIs on these ecosystems is critical for predicting the global C balance and feedbacks to climate. ResultsUsing a spatially extensive dataset of 555 plots from 31 separate fires in Interior Alaska, our study demonstrates that shortened FRIs decrease the C storage capacity of boreal forests through loss of legacy C and regeneration failure. Total wildfire C emissions were similar among FRI classes, ranging from 2.5 to 3.5 kg C m⁻². However, shortened FRIs lost proportionally more of their pre-fire C pools, resulting in substantially lower post-fire C pools than long FRIs. Shortened FRIs also resulted in the combustion of legacy C, defined as C that escaped combustion in one or more previous fires. We found that post-fire successional trajectories were impacted by FRI, with ~ 65% of short FRIs and triple burns experiencing regeneration failure. ConclusionsOur study highlights the structural and functional vulnerability of boreal forests to increasing fire frequency. Shortened FRIs and the combustion of legacy C can shift boreal ecosystems from a net C sink or neutral to a net C source to the atmosphere and increase the risk of transitions to non-forested states. These changes could have profound implications for the boreal C-climate feedback and underscore the need for adaptive management strategies that prioritize the structural and functional resilience of boreal forest ecosystems to expected increases in fire frequency. 

Wildfires in the Arctic-boreal zone have increased in frequency over recent decades, carrying substantial ecological, social, and economic consequences. Remote sensing is crucial for mapping burned areas, monitoring wildfire dynamics, and evaluating their impacts. However, existing high-latitude burned area products suffer from significant discrepancies, particularly in Siberia, and their coarse spatial resolutions limit accuracy and utility. To address these gaps, we developed a convolutional neural network model to map burned areas at a 30-meter resolution across the Arctic-boreal zone using Landsat and Sentinel-2 imagery. Our model achieved promising results, with an Intersection Over Union (IOU) of 0.77 and an F1 score of 0.85 on unseen test data, performing better in North America (IOU=0.84) than Eurasia (IOU=0.72) due to differences in fire regimes and data quality. Predictions for six representative years showed our model’s burned area closely matched the median values of Landsat, MODIS, and VIIRS-based products, although alignment varied annually and spatially. Visual assessments indicated our approach was generally more accurate, notably in detecting unburned vegetation islands within fire perimeters missed by other products. This research has numerous potential applications, such as analyzing feedback between vegetation and burn patterns, characterizing spatial dynamics of unburned islands, and improving carbon emission estimates through detailed burn severity assessments. Here we have provided the primary series of scripts used to achieve the above results. In these scripts we use historical vector fire polygons to download imagery from Landsat 5, 7, 8, 9 and Sentinel-2 to train a deep learning model called a UNet++ in the Arctic-boreal zone. Imagery is downloaded from Google Earth Engine, while all other processing is done locally. The series of 6 scripts describes main steps from downloading training data, pre-processing it, training the model, and applying the model across the Arctic Boreal Zone. All scripting is done in python through .py scripts and Jupyter notebooks (.ipynb). Our study area includes Alaska, Canada and Eurasia, and we trained our model on all historical fire polygons from 1985-2020. 

Boreal and Arctic regions have warmed up to four times quicker than the rest of the planet since the 1970s. As a result, boreal and tundra ecosystems are experiencing more frequent and higher intensity extreme weather events and disturbances, such as wildfires. Yet limitations in ground and satellite data across the Arctic and boreal regions have challenged efforts to track these disturbances at regional scales. In order to effectively monitor the progression and extent of wildfires in the Arctic-boreal zone, it is essential to determine whether burned area (BA) products are accurate representations of BA. Here, we use 12 different datasets together with MODIS active fire data to determine the total yearly BA and seasonal patterns of fires in Arctic-boreal North America and Russia for the years 2001–2020. We found relatively little variability between the datasets in North America, both in terms of total BA and seasonality, with an average BA of 2.55 ± 1.24 (standard deviation) Mha/year for our analysis period, the majority (ca. 41%) of which occurs in July. In contrast, in Russia, there are large disparities between the products—GFED5 produces over four times more BA than GFED4s in southern Siberia. These disparities occur due to the different methodologies used; dNBR (differenced Normalized Burn Ratio) of short-term composites from Landsat images used alongside hotspot data was the most consistently successful in representing BA. We stress caution using GABAM in these regions, especially for the years 2001–2013, as Landsat-7 ETM+ scan lines are mistaken as burnt patches, increasing errors of commission. On the other hand, we highlight using regional products where possible, such as ABoVE-FED or ABBA in North America, and the Talucci et al. fire perimeter product in Russia, due to their detection of smaller fires which are often missed by global products. 

Abstract. The snow cover extent across the Northern Hemisphere has diminished, while the number of lightning ignitions and amount of burned area have increased over the last 5 decades with accelerated warming. However, the effects of earlier snow disappearance on fire are largely unknown. Here, we assessed the influence of snow disappearance timing on fire ignitions across 16 ecoregions of boreal North America. We found spatially divergent trends in earlier (later) snow disappearance, which led to an increasing (decreasing) number of ignitions for the northwestern (southeastern) ecoregions between 1980 and 2019. Similar northwest–southeast divergent trends were observed in the changing length of the snow-free season and correspondingly the fire season length. We observed increases (decreases) over northwestern (southeastern) boreal North America which coincided with a continental dipole in air temperature changes between 2001 and 2019. Earlier snow disappearance induced earlier ignitions of between 0.22 and 1.43 d earlier per day of earlier snow disappearance in all ecoregions between 2001 and 2019. Early-season ignitions (defined by the 20 % earliest fire ignitions per year) developed into significantly larger fires in 8 out of 16 ecoregions, being on average 77 % larger across the whole domain. Using a piecewise structural equation model, we found that earlier snow disappearance is a good direct proxy for earlier ignitions but may also result in a cascade of effects from earlier desiccation of fuels and favorable weather conditions that lead to earlier ignitions. This indicates that snow disappearance timing is an important trigger of land–atmosphere dynamics. Future warming and consequent changes in snow disappearance timing may contribute to further increases in western boreal fires, while it remains unclear how the number and timing of fire ignitions in eastern boreal North America may change with climate change. 

Abstract Climate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near‐term (5–30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi‐model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long‐term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance‐induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near‐future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi‐model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage. 

This dataset provides annual gridded estimates of fire locations and associated burn fraction per pixel for Alaska and Canada at approximately 500 meter (m) spatial resolution for the period 2001-2019. Gridded predictions of carbon combustion and burn depth for the same period within the Arctic-Boreal Vulnerability Experiment (ABoVE) extended domain using the burn area maps and field data are also available. Fire locations and date of burn (DOB) were detected by MODIS-derived active fire products. Burned area was primarily estimated from finer-scale Landsat imagery using a differenced Normalized Burn Ratio (dNBR) algorithm and upscaled to an approximate 500 m MODIS resolution. Aboveground combustion, belowground combustion, and burn depth were statistically modeled at the pixel level for every mapped burned pixel in the ABoVE extended domain based on field observations across Alaska and western Canada. Predictor variables included remotely sensed indicators of fire severity, topography, soils, climate, and fire weather. Quality flags for burned area and combustion are available. Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. These data are useful for studies of disturbance, fire ecology, and carbon cycling in boreal ecosystems. 

Abstract The Arctic–Boreal Zone is rapidly warming, impacting its large soil carbon stocks. Here we use a new compilation of terrestrial ecosystem CO₂fluxes, geospatial datasets and random forest models to show that although the Arctic–Boreal Zone was overall an increasing terrestrial CO₂sink from 2001 to 2020 (mean ± standard deviation in net ecosystem exchange, −548 ± 140 Tg C yr⁻¹; trend, −14 Tg C yr⁻¹;P < 0.001), more than 30% of the region was a net CO₂source. Tundra regions may have already started to function on average as CO₂sources, demonstrating a shift in carbon dynamics. When fire emissions are factored in, the increasing Arctic–Boreal Zone sink is no longer statistically significant (budget, −319 ± 140 Tg C yr⁻¹; trend, −9 Tg C yr⁻¹), and the permafrost region becomes CO₂neutral (budget, −24 ± 123 Tg C yr⁻¹; trend, −3 Tg C yr⁻¹), underscoring the importance of fire in this region.