This study presents an unoccupied aerial vehicle (UAV) platform used to resolve horizontal and vertical patterns of CO₂ and CH₄ mole fractions within the lower part of the atmospheric boundary layer. The obtained data contribute important information for upscaling fluxes from natural ecosystems over heterogeneous terrain and for constraining hot spots of greenhouse gas (GHG) emissions. This observational tool, therefore, has the potential to complement existing stationary carbon monitoring networks for GHGs, such as eddy covariance towers and manual flux chambers. The UAV platform is equipped with two gas analyzers for CO₂ and CH₄ that are connected sequentially. In addition, a 2D anemometer is deployed above the rotor plane to measure environmental parameters including 2D wind speed, air temperature, humidity, and pressure. Laboratory and field tests demonstrate that the platform is capable of providing data with reliable accuracy, with good agreement between the UAV data and tower-based measurements of CO₂, H₂O, and wind speed. Using interpolated maps of GHG mole fractions, with this tool we assessed the signal variability over a target area and identified potential hot spots. Our study shows that the UAV platform provides information about the spatial variability of the lowest part of the boundary layer, which to date remains poorly observed, especially in remote areas such as the Arctic. Furthermore, using the profile method, it is demonstrated that the GHG fluxes from a local sources can be calculated. Although subject to large uncertainties over the area of interest, the comparison between the eddy covariance method and UAV-based calculations showed acceptable qualitative agreement.

Our understanding of how rapid Arctic warming and permafrost thaw affect global climate dynamics is restricted by limited spatiotemporal data coverage due to logistical challenges and the complex landscape of Arctic regions. It is therefore crucial to make best use of the available observations, including the integrated data analysis across disciplines and observational platforms. To alleviate the data compilation process for syntheses, cross-scale analyses, Earth system models, and remote sensing applications, we introduce the ARctic greenhouse Gas Observation metadata version 1 (ARGO), a new meta-dataset comprised of greenhouse gas observations from various observational platforms across the Arctic and boreal biomes within the polar region of the Northern Hemisphere. ARGO provides a centralised repository for metadata on carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) measurements and is linked to an interactive online tool (https://www.bgc-jena.mpg.de/argo/, last access: 6 May 2025). This tool offers prompt metadata visualisation for the research community. Here, we present the structure and features of ARGO, underscoring its role as a valuable resource for advancing Arctic climate research and guiding synthesis efforts in the face of rapid environmental change in northern regions. The ARGO meta-dataset is openly available for download at Zenodo (https://doi.org/10.5281/zenodo.13870389; Vogt et al., 2024 a ).

The atmospheric concentration of nitrous oxide (N₂O) has increased significantly since 1800, mainly due to agricultural activities. However, due to their large area, nutrient-poor natural soils, including those in the (sub-) Arctic, also play a crucial role in N₂O emissions and consumption. Despite their importance, these soils have been understudied, due to methodological limitations in detecting low fluxes. Our study addresses this knowledge gap by testing a fast-responding portable gas analyser (PGA; Aeris MIRA Ultra ${\mathrm{N}}_{\mathrm{2}}\mathrm{O}/{\mathrm{CO}}_{\mathrm{2}}$ ) combined with manual chambers (height and diameter: 25 cm) for measuring N₂O fluxes from a nutrient-poor, sub-Arctic peatland. Our results show that this setup can detect and quantify low N₂O flux rates, with a mean and standard error of $-\mathrm{0.61}\pm \mathrm{0.08}$ µg N₂O-N ${\mathrm{m}}^{-\mathrm{2}}{\mathrm{h}}^{-\mathrm{1}}$ for a 5 min closure time, as observed in our study. More than 70 % of the measured N₂O fluxes exceeded the minimum detectable flux ($\mathrm{0.027}\pm \mathrm{0.0002}\mathrm{\mu }\mathrm{mol}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{h}}^{-\mathrm{1}}$ ), which varied according to chamber closure time. Our study highlights the importance of using fast-responding analysers to measure low N₂O fluxes and improve our understanding of diverse N₂O flux dynamics. For nutrient-poor soils, we recommend a chamber closure time of approximately 5 min. We also found that a non-linear flux calculation model yielded better results and was broadly applicable, including cases where data were linearly distributed. Overall, our study demonstrates the potential of fast-responding analysers to improve our understanding of N₂O flux dynamics in nutrient-poor soils.

Many natural ecosystems are composed of heterogeneous patches differentiated by wetness levels and vegetation composition, resulting in fine-scale flux patterns across the different landcovers that can be challenging to quantify. Here, we present a case study at Stordalen Mire in subarctic Sweden, where we conducted Uncrewed Aerial Vehicle (UAV) measurements of CO₂ mole fractions and combine them with a large-eddy simulation (LES) model through a site-level inversion method to differentiate the flux rate signatures from different patch types. We use the LES model EULAG (EUlerian LAGrangian) to simulate high-resolution flow patterns and benchmark the spatial variability of modelled concentrations with data from UAV-based grid surveys of CO₂ mixing ratio. Coupling the inversion results with eddy-covariance (EC) flux measurements for the time of the UAV flight allows quantifying net CO₂ fluxes for the individual landcover types. Model evaluation showed an R² up to 0.70, with model uncertainties mostly related to the transport model uncertainty and the UAV sampling footprint that does not evenly sample landcover types. The inversion fluxes were subsequently compared to patch-level chamber measurements of carbon dioxide from palsa, bog, and fen, and showed a good agreement in flux patterns across those patch types dominating the UAV-sampled footprint. Different landcover classification schemes were considered, and results showed a consistent improvement in the model performance when further representing the ecological and hydrological heterogeneities. Our novel technique shows promising results in estimating landcover-type flux heterogeneity within eddy-covariance tower footprints, thus providing a basis for upscaling of EC fluxes to a larger domain.

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).