Mikhail Mastepanov

Snow cover is a key component of Arctic ecosystems, regulating both biotic and abiotic processes such as plant phenology, soil thermal regimes, greenhouse gas fluxes, and the timing of glacier melt. We present a 26-year (2000–2025) record of snow-free days derived from the MODIS Terra cloud-gap-filled snow cover product at three long-term monitoring sites in high, mid, and low-Arctic Greenland. The MODIS-derived counts correlated well with in situ observations, most strongly at the high-arctic site, the only location where a direct comparison to in situ observations was possible. The mean snow-free season length is 76, 78 and 103 days at high-, mid-, and low-Arctic respectively, with a standard deviation of approximately two weeks at each site despite their contrasting latitudinal positions. We find no statistically significant trends in the number of snow-free days at any site over the record, consistent with the absence of significant trends in Northern Hemisphere snow cover in the Greenland and North American sector over the same period. The dominant driver of interannual variability shifts along the latitudinal gradient: snowfall was the dominant predictor of snow free day counts at the high arctic site, positive degree-days become co-limiting alongside snowfall at the low-Arctic site and at the mid-Arctic site positive degree-days are the only significant predictor, allthought snowfall and positive-degree-days together explain little of the interannual variance there, which point to additional physical controls. The high interannual variability in snow-free season length underscores the necessity of monitoring records spanning multiple decades in order to detect climate-change signals in snow cover and the many Arctic ecosystem processes that depend on it.

Snow cover is a key component of Arctic ecosystems, regulating both biotic and abiotic processes such as plant phenology, soil thermal regimes, greenhouse gas fluxes, and the timing of glacier melt. We present a 26-year (2000–2025) record of snow-free days derived from the MODIS Terra cloud-gap-filled snow cover product at three long-term monitoring sites in high, mid, and low-Arctic Greenland. The MODIS-derived counts correlated well with in situ observations, most strongly at the high-arctic site, the only location where a direct comparison to in situ observations was possible. The mean snow-free season length is 76, 78 and 103 days at high-, mid-, and low-Arctic respectively, with a standard deviation of approximately two weeks at each site despite their contrasting latitudinal positions. We find no statistically significant trends in the number of snow-free days at any site over the record, consistent with the absence of significant trends in Northern Hemisphere snow cover in the Greenland and North American sector over the same period. The dominant driver of interannual variability shifts along the latitudinal gradient: snowfall was the dominant predictor of snow free day counts at the high arctic site, positive degree-days become co-limiting alongside snowfall at the low-Arctic site and at the mid-Arctic site positive degree-days are the only significant predictor, allthought snowfall and positive-degree-days together explain little of the interannual variance there, which point to additional physical controls. The high interannual variability in snow-free season length underscores the necessity of monitoring records spanning multiple decades in order to detect climate-change signals in snow cover and the many Arctic ecosystem processes that depend on it.

Nitrous oxide (N₂O) emissions from cultivated peatlands are often dominated by sporadic but high-magnitude ”hot moments”, creating major challenges for emission prediction across variable conditions. We measured N₂O fluxes using automated chambers across a water table gradient and contrasting fertilization treatments at a cultivated peatland in northern Norway, over growing seasons in 2022–2024. Hot moments represented only ~6% of all observations yet accounted for 13–92% of cumulative N₂O emissions depending on water table and fertilization conditions. While increasing fertilization only moderately elevated background fluxes, it substantially increased total emissions by promoting both the occurrence and magnitude of N₂O hot moments. An XGBoost model correctly predicted most events with a detection rate of >99% for hot moments and a 2.4% false alarm rate. The model identified the fertilization index, a variable combining fertilization dose and time elapsed since application, as the dominant driver of hot-moments. Soil moisture exerted a nonlinear, conditional influence: hot-moment probability peaked at intermediate to high moisture levels (~50–65%) only when nitrogen availability (indicated by fertilization index) was sufficiently high. A Class-Swap framework, in which dedicated XGBoost regression models were trained separately for background and hot-moment flux regimes, substantially improved prediction accuracy over a single-model benchmark with 29% reduction in mean absolute error and 95% reduction in bias. By assigning progressively greater weights to large fluxes (>95th percentile) to address the imbalance in the N₂O flux dataset, we show that the Class-Swap framework was more effective than weighting adjustment alone. Our results suggest that sparse sampling and static emission factors are insufficient to capture peatland N₂O dynamics, and high-frequency monitoring is required. Improved hot-moment prediction can strengthen N₂O mitigation by identifying high-risk windows in which high fertilization coincides with intermediate soil moisture, informing adaptive fertilizer timing and rates to avoid conditions that trigger hot moments.

Snow cover is a key component of Arctic ecosystems, regulating both biotic and abiotic processes such as plant phenology, soil thermal regimes, greenhouse gas fluxes, and the timing of glacier melt. We present a 26-year (2000–2025) record of snow-free days derived from the MODIS Terra cloud-gap-filled snow cover product at three long-term monitoring sites in high, mid, and low-Arctic Greenland. The MODIS-derived counts correlated well with in situ observations, most strongly at the high-arctic site, the only location where a direct comparison to in situ observations was possible. The mean snow-free season length is 76, 78 and 103 days at high-, mid-, and low-Arctic respectively, with a standard deviation of approximately two weeks at each site despite their contrasting latitudinal positions. We find no statistically significant trends in the number of snow-free days at any site over the record, consistent with the absence of significant trends in Northern Hemisphere snow cover in the Greenland and North American sector over the same period. The dominant driver of interannual variability shifts along the latitudinal gradient: snowfall was the dominant predictor of snow free day counts at the high arctic site, positive degree-days become co-limiting alongside snowfall at the low-Arctic site and at the mid-Arctic site positive degree-days are the only significant predictor, allthought snowfall and positive-degree-days together explain little of the interannual variance there, which point to additional physical controls. The high interannual variability in snow-free season length underscores the necessity of monitoring records spanning multiple decades in order to detect climate-change signals in snow cover and the many Arctic ecosystem processes that depend on it.

Snow cover is a key component of Arctic ecosystems, regulating both biotic and abiotic processes such as plant phenology, soil thermal regimes, greenhouse gas fluxes, and the timing of glacier melt. We present a 26-year (2000–2025) record of snow-free days derived from the MODIS Terra cloud-gap-filled snow cover product at three long-term monitoring sites in high, mid, and low-Arctic Greenland. The MODIS-derived counts correlated well with in situ observations, most strongly at the high-arctic site, the only location where a direct comparison to in situ observations was possible. The mean snow-free season length is 76, 78 and 103 days at high-, mid-, and low-Arctic respectively, with a standard deviation of approximately two weeks at each site despite their contrasting latitudinal positions. We find no statistically significant trends in the number of snow-free days at any site over the record, consistent with the absence of significant trends in Northern Hemisphere snow cover in the Greenland and North American sector over the same period. The dominant driver of interannual variability shifts along the latitudinal gradient: snowfall was the dominant predictor of snow free day counts at the high arctic site, positive degree-days become co-limiting alongside snowfall at the low-Arctic site and at the mid-Arctic site positive degree-days are the only significant predictor, allthought snowfall and positive-degree-days together explain little of the interannual variance there, which point to additional physical controls. The high interannual variability in snow-free season length underscores the necessity of monitoring records spanning multiple decades in order to detect climate-change signals in snow cover and the many Arctic ecosystem processes that depend on it.

Measurements of surface-atmosphere carbon dioxide (CO ₂ ) and methane (CH ₄ ) fluxes have been relatively sparse across the Arctic tundra and boreal biomes, causing significant uncertainties in carbon budget estimates from the region. While the availability of Arctic-boreal carbon flux data has increased substantially over the past decade, the data have remained spread across different repositories, scientific articles, and unpublished sources, making it difficult to leverage. Here we present a new dataset of monthly Arctic-boreal carbon fluxes (ABCFlux v2) across terrestrial (wetlands and uplands) and freshwater (lakes and rivers) ecosystems compiled from previous syntheses including the Arctic-boreal CO ₂ flux database (ABCFlux v1), the Boreal-Arctic Wetland and Lake Methane Dataset (BAWLD-CH4), and the Global River Methane Database (GRiMeDB). In addition, we consider data from general-purpose (e.g., Zenodo) and flux network repositories, literature, and site principal investigators. The dataset includes surface-atmosphere CO ₂ fluxes of gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange (NEE), alongside CH ₄ fluxes. For aquatic ecosystems, we split CH ₄ fluxes into diffusive and ebullitive flux pathways, and included potential emissions from transient storage in the water column (“storage fluxes”), alongside CO ₂ and CH ₄ concentrations dissolved in the surface water. Fluxes are measured through a variety of methods including chamber and eddy covariance techniques alongside bubble traps, ice-surveys, and concentration-based turbulence-driven modelling in aquatic ecosystems. The monthly flux data are reported together with supporting methodological and environmental metadata. The resulting ABCFlux v2 has 23 847 flux site-months, 8182 concentration site-months, and 199 seasonal observations from 1024 sites, and includes 56 139 reported fluxes (i.e. sum of GPP, Reco, NEE, and CH ₄ fluxes) from the years 1984 to 2024. The majority of monthly observations occurred after 1999. Wetlands had the highest number of site-month observations (8758), followed by boreal forest (6981), lotic ecosystems (6275), lentic ecosystems (3799) and upland tundra (3308). Measurements of CO ₂ dominated the dataset across most ecosystem types (25 222) except for lentic ecosystems, where CH ₄ flux site-months (3098) were more frequent than CO ₂ flux site-months (2915). Overall, ABCFlux v2 includes 160 % more site-months for terrestrial CO ₂ flux data compared to ABCFlux v1. Integrating and updating BAWLD-CH4 flux data from growing season averages to monthly fluxes resulted in 5671 site-months of chamber CH ₄ data compared to 762 site-years. This collaborative initiative, involving contributions from over 260 researchers, provides a comprehensive overview of the current state of the Arctic-boreal carbon flux network and its data, and serves as an important step in reducing uncertainties in Arctic-boreal carbon budgets and in enhancing our understanding of climate feedbacks. The data can be accessed at ORNL DAAC at https://doi.org/10.3334/ORNLDAAC/2448 (Virkkala et al., 2026).

Snow cover is a key component of Arctic ecosystems, regulating both biotic and abiotic processes such as plant phenology, soil thermal regimes, greenhouse gas fluxes, and the timing of glacier melt. We present a 26-year (2000–2025) record of snow-free days derived from the MODIS Terra cloud-gap-filled snow cover product at three long-term monitoring sites in high, mid, and low-Arctic Greenland. The MODIS-derived counts correlated well with in situ observations, most strongly at the high-arctic site, the only location where a direct comparison to in situ observations was possible. The mean snow-free season length is 76, 78 and 103 days at high-, mid-, and low-Arctic respectively, with a standard deviation of approximately two weeks at each site despite their contrasting latitudinal positions. We find no statistically significant trends in the number of snow-free days at any site over the record, consistent with the absence of significant trends in Northern Hemisphere snow cover in the Greenland and North American sector over the same period. The dominant driver of interannual variability shifts along the latitudinal gradient: snowfall was the dominant predictor of snow free day counts at the high arctic site, positive degree-days become co-limiting alongside snowfall at the low-Arctic site and at the mid-Arctic site positive degree-days are the only significant predictor, allthought snowfall and positive-degree-days together explain little of the interannual variance there, which point to additional physical controls. The high interannual variability in snow-free season length underscores the necessity of monitoring records spanning multiple decades in order to detect climate-change signals in snow cover and the many Arctic ecosystem processes that depend on it.

Active layer increase may contribute to greenhouse gas emissions, ecosystem change, and increased hazards. Here, we show the results of field measurements of active-layer thickness from 156 monitoring sites in Arctic, Antarctic and mountain permafrost regions. Active layer thickness increased significantly at 55% and 38% of sites in the Arctic and Antarctic regions, at more than 90% European mountain and high elevation Asian sites, and at sites in South America, demonstrating worldwide permafrost degradation during the first quarter of this century (2000–2024). The largest changes were observed in mountain regions where active layer doubled at several sites. The smallest changes were observed at sites with thick surficial organic horizons and high ground ice content. In continuous permafrost, sites generally exhibited smaller increases compared to sites in discontinuous permafrost. Regression analysis indicates Arctic active layer changes are attributable to increasing thawing degree-days, followed by increases in total rainfall. Other permafrost regions require more sites and longer time-series to draw conclusions regarding active layer change attribution.

Peatlands drained for agriculture and other uses release substantial carbon dioxide. Many countries estimate these emissions using the 2014 IPCC Tier 1 emission factors. Here we calibrated an ecosystem model with data from two cultivated peatland sites in Norway and simulate carbon dioxide emissions at 50 sites nationwide for 2001–2022. Model results showed that carbon dioxide emissions were strongly controlled by water table depth and aligned well with observations from other European peatlands of similar climate zones. Crucially, the Tier 1 emission factor matched our simulations only under very deep water tables (< –0.7 m), but overestimated emissions by 31–88% when water levels ranged from –0.7 m to –0.3 m. This indicates that Tier 1 methods may overstate emissions from cultivated peatlands in cool temperate and boreal regions, inflating estimates of mitigation potential. Tier 2 or 3 approaches can reduce uncertainty but require more field data.