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The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m− 2) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ~6000 g m− 2 (mean ≈350 g m− 2), while predicted values ranged from 0 to ~4000 g m− 2 (mean ≈275 g m− 2), resulting in model validation root-mean-squared-error (RMSE) ≈400 g m− 2 and R2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling 

Abstract Northern peatlands are a globally significant source of methane (CH₄), and emissions are projected to increase due to warming and permafrost loss. Understanding the microbial mechanisms behind patterns in CH₄production in peatlands will be key to predicting annual emissions changes, with stable carbon isotopes (δ¹³C‐CH₄) being a powerful tool for characterizing these drivers. Given that δ¹³C‐CH₄is used in top‐down atmospheric inversion models to partition sources, our ability to model CH₄production pathways and associated δ¹³C‐CH₄values is critical. We sought to characterize the role of environmental conditions, including hydrologic and vegetation patterns associated with permafrost thaw, on δ¹³C‐CH₄values from high‐latitude peatlands. We measured porewater and emitted CH₄stable isotopes, pH, and vegetation composition from five boreal‐Arctic peatlands. Porewater δ¹³C‐CH₄was strongly associated with peatland type, with δ¹³C enriched values obtained from more minerotrophic fens (−61.2 ± 9.1‰) compared to permafrost‐free bogs (−74.1 ± 9.4‰) and raised permafrost bogs (−81.6 ± 11.5‰). Variation in porewater δ¹³C‐CH₄was best explained by sedge cover, CH₄concentration, and the interactive effect of peatland type and pH (r² = 0.50,p < 0.001). Emitted δ¹³C‐CH₄varied greatly but was positively correlated with porewater δ¹³C‐CH₄. We calculated a mixed atmospheric δ¹³C‐CH₄value for northern peatlands of −65.3 ± 7‰ and show that this value is more sensitive to landscape drying than wetting under permafrost thaw scenarios. Our results suggest northern peatland δ¹³C‐CH₄values are likely to shift in the future which has important implications for source partitioning in atmospheric inversion models. 

Abstract Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we presentThe Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m⁻²) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic. 

This dataset provides estimates of live, oven-dried aboveground biomass of all plants (tree, shrub, graminoid, forb, bryophyte) and all woody plants (tree, shrub) at 30-meter resolution across the Arctic tundra biome. Estimates of woody plant dominance are also provided as: (woody plant biomass / plant biomass) * 100. Plant biomass and woody plant biomass were estimated for each pixel (grams per square meter [g / m2]) using field harvest data for calibration/validation along with modeled seasonal surface reflectance data derived using Landsat satellite imagery and the Continuous Change Detection and Classification algorithm, and other supplementary predictors related to topography, region (e.g. bioclimate zone, ecosystem type), land cover, and derivative spectral products. Modeling was performed in a two-stage process using random forest models. First, biomass presence/absence was predicted using probability forests. Then, biomass quantity was predicted using regression forests. The model outputs were combined to produce final biomass estimates. Pixel uncertainty was assessed using Monte Carlo iterations. Field and remote sensing data were permuted during each iteration and the median (50th percentile, p500) predictions for each pixel were considered best estimates. In addition, this dataset provides the lower (2.5th percentile, p025) and upper (97.5th percentile, p975) bounds of a 95% uncertainty interval. Estimates of woody plant dominance are not modeled directly, but rather derived from plant biomass and woody plant biomass best estimates. The Pan Arctic domain includes both the Polar Arctic, defined using bioclimate zone data from the Circumpolar Arctic Vegetation Mapping Project (CAVM; Walker et al., 2005), and the Oro Arctic (treeless alpine tundra at high latitudes outside the Polar Arctic), defined using tundra ecoregions from the RESOLVE ecoregions dataset (Dinerstein et al., 2017) and treeline data from CAVM (CAVM Team, 2003). The mapped products focus on Arctic tundra vegetation biomass, but the coarse delineation of this biome meant some forested areas were included within the study domain. Therefore, this dataset also provides a tree mask product that can be used to mask out areas with canopy height ≥ 5 meters. This mask helps reduce, but does not eliminate entirely, areas of dense tree cover within the domain. Users should be cautious of predictions in forested areas as the models used to predict biomass were not well constrained in these areas. This dataset includes 132 files: 128 cloud-optimized GeoTIFFs, 2 tables in comma-separated values (CSV) format, 1 vector polygon in Shapefile format, and one figure in JPEG format. Raster data is provided in the WGS 84 / North Pole LAEA Bering Sea projection (EPSG:3571) at 30 meter (m) resolution. Raster data are tiled with letters representing rows and numbers representing columns, but note that some tiles do not contain unmasked pixels. We included all tiles nonetheless to maintain consistency. Tiling information can be found in the ‘metadata’ directory as a figure (JPEG) or shapefile. 

Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic Plant Aboveground Biomass Synthesis Dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass grams per meter squared (g/m^2) on 2327 sample plots in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic. 

Abstract Lakes play a significant role in the global carbon cycle, acting as sources and sinks of carbon dioxide (CO₂). In situ measurements of CO₂flux (FCO₂) from lakes have generally been collected during daylight, despite indications of significant diel variability. This introduces bias when scaling up to whole‐lake annual aquatic carbon budgets. We conducted an international sampling program to ascertain the extent of diel variation in FCO₂across lakes. We sampled 21 lakes over 41 campaigns and measured FCO₂at 4‐h intervals over a full diel cycle. Rates of FCO₂ranged from −3.16 to 4.39 mmol m⁻² h⁻¹. Integrated over a day, FCO₂ranged from −381.68 to 878.49 mg C m⁻²d⁻¹(mean = 76.54) across campaigns. We identified three characteristic diel patterns in FCO₂related to trophic status and show that for half of the campaigns, daily flux estimates were biased by > 50% if based on a single (daytime) measurement. 

Peatlands account for 15 to 30% of the world’s soil carbon (C) stock and are important controls over global nitrogen (N) cycles. However, C and N concentrations are known to vary among peatlands contributing to the uncertainty of global C inventories, but there are few global studies that relate peatland classification to peat chemistry. We analyzed 436 peat cores sampled in 24 countries across six continents and measured C, N, and organic matter (OM) content at three depths down to 70 cm. Sites were distinguished between northern (387) and tropical (49) peatlands and assigned to one of six distinct broadly recognized peatland categories that vary primarily along a pH gradient. Peat C and N concentrations, OM content, and C:N ratios differed significantly among peatland categories, but few differences in chemistry with depth were found within each category. Across all peatlands C and N concentrations in the 10–20 cm layer, were 440 ± 85.1 g kg -1 and 13.9 ± 7.4 g kg -1 , with an average C:N ratio of 30.1 ± 20.8. Among peatland categories, median C concentrations were highest in bogs, poor fens and tropical swamps (446–532 g kg -1 ) and lowest in intermediate and extremely rich fens (375–414 g kg -1 ). The C:OM ratio in peat was similar across most peatland categories, except in deeper samples from ombrotrophic tropical peat swamps that were higher than other peatlands categories. Peat N concentrations and C:N ratios varied approximately two-fold among peatland categories and N concentrations tended to be higher (and C:N lower) in intermediate fens compared with other peatland types. This study reports on a unique data set and demonstrates that differences in peat C and OM concentrations among broadly classified peatland categories are predictable, which can aid future studies that use land cover assessments to refine global peatland C and N stocks. 

Abstract. Methane emissions from boreal and arctic wetlands, lakes, and rivers areexpected to increase in response to warming and associated permafrost thaw.However, the lack of appropriate land cover datasets for scalingfield-measured methane emissions to circumpolar scales has contributed to alarge uncertainty for our understanding of present-day and future methaneemissions. Here we present the Boreal–Arctic Wetland and Lake Dataset(BAWLD), a land cover dataset based on an expert assessment, extrapolatedusing random forest modelling from available spatial datasets of climate,topography, soils, permafrost conditions, vegetation, wetlands, and surfacewater extents and dynamics. In BAWLD, we estimate the fractional coverage offive wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes(17 % of the global land surface). Land cover classes were defined usingcriteria that ensured distinct methane emissions among classes, as indicatedby a co-developed comprehensive dataset of methane flux observations. InBAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain)with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetlandclasses, covering ∼ 28 % each of the total wetland area,while the highest-methane-emitting marsh and tundra wetland classes occupied5 % and 12 %, respectively. Lakes, defined to include all lentic open-waterecosystems regardless of size, covered 1.4 × 106 km2(6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lakearea, while high-emitting peatland and yedoma lakes covered 18 % and 4 %,respectively. Small (<0.1 km2) glacial, peatland, and yedomalakes combined covered 17 % of the total lake area but contributeddisproportionally to the overall spatial uncertainty in lake area with a95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12  × 106 km2 (0.5 % of domain), of which 8 % was associated withhigh-methane-emitting headwaters that drain organic-rich landscapes.Distinct combinations of spatially co-occurring wetland and lake classeswere identified across the BAWLD domain, allowing for the mapping of“wetscapes” that have characteristic methane emission magnitudes andsensitivities to climate change at regional scales. With BAWLD, we provide adataset which avoids double-accounting of wetland, lake, and river extentsand which includes confidence intervals for each land cover class. As such,BAWLD will be suitable for many hydrological and biogeochemical modellingand upscaling efforts for the northern boreal and arctic region, inparticular those aimed at improving assessments of current and futuremethane emissions. Data are freely available athttps://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).