Mathias Göckede

Water-air fluxes of carbon dioxide (CO₂) and methane (CH₄) in lakes exhibit substantial spatial heterogeneity, often varying across remarkably fine spatial scales. While manual flux chamber measurements offer high spatial resolution and the potential to capture this variability, their application is typically constrained by labor intensity and logistical limitations. In contrast, eddy covariance (EC) measurements integrate fluxes over a larger footprint, effectively averaging over spatial gradients and complicating a process-based interpretation of the data. Bridging this gap requires methods that combine spatial precision with scalable, continuous monitoring – essential for advancing our mechanistic understanding of lake carbon dynamics. To facilitate highly resolved biogeochemical measurements, we developed BlueMinerva, an autonomous platform to monitor surface carbon fluxes with the static chamber method, and simultaneously determine physicochemical, biological, bathymetric, and meteorological variables at pre-defined locations. The platform can be programmed to navigate a user-defined track across a waterbody, and collect flux and ancillary data both in transit and at fixed locations for several hours to days, depending on the sensor configuration and related battery requirements. We deployed the setup at Dagow Lake (Germany) and in a small lake in the Stordalen Mire (Sweden). In total, we obtained 485 chamber-derived flux estimates over 72 measurement hours. We compared CO₂ flux estimates between measurements with two different gas analyzers that were simultaneously mounted on the BlueMinerva. The lower-cost sensor (CARBOCAP GMP343, Vaisala) performed equally well as the precise, but costlier sensor (LI-7810, LI-COR) as long as measurements were sufficiently long (around 5 min). Furthermore, we compared measured carbon fluxes with those from an eddy covariance tower at Dagow Lake where CH₄ fluxes diverged slightly, possibly linked to the usage of different sensors (closed-path versus open-path), while magnitudes of CO₂ fluxes matched well. At both lakes, we identified areas of higher emissions, especially for CH₄, and were thus able to resolve the spatial variability of carbon fluxes within the waterbodies. Concurrently, we measured differences in meteorological conditions, and critical limnological variables (water temperature, specific conductivity, pH, dissolved oxygen, chlorophyll, phycocyanin, turbidity, and fluorescent dissolved organic matter) – valuable measurements that enable a comprehensive assessment of environmental drivers behind flux variability. We conclude that platforms like the BlueMinerva have the potential to be adopted widely by scientists and stakeholders to better capture biogeochemical processes in lakes at high spatial and temporal resolution.

Water-air fluxes of carbon dioxide (CO₂) and methane (CH₄) in lakes exhibit substantial spatial heterogeneity, often varying across remarkably fine spatial scales. While manual flux chamber measurements offer high spatial resolution and the potential to capture this variability, their application is typically constrained by labor intensity and logistical limitations. In contrast, eddy covariance (EC) measurements integrate fluxes over a larger footprint, effectively averaging over spatial gradients and complicating a process-based interpretation of the data. Bridging this gap requires methods that combine spatial precision with scalable, continuous monitoring – essential for advancing our mechanistic understanding of lake carbon dynamics. To facilitate highly resolved biogeochemical measurements, we developed BlueMinerva, an autonomous platform to monitor surface carbon fluxes with the static chamber method, and simultaneously determine physicochemical, biological, bathymetric, and meteorological variables at pre-defined locations. The platform can be programmed to navigate a user-defined track across a waterbody, and collect flux and ancillary data both in transit and at fixed locations for several hours to days, depending on the sensor configuration and related battery requirements. We deployed the setup at Dagow Lake (Germany) and in a small lake in the Stordalen Mire (Sweden). In total, we obtained 485 chamber-derived flux estimates over 72 measurement hours. We compared CO₂ flux estimates between measurements with two different gas analyzers that were simultaneously mounted on the BlueMinerva. The lower-cost sensor (CARBOCAP GMP343, Vaisala) performed equally well as the precise, but costlier sensor (LI-7810, LI-COR) as long as measurements were sufficiently long (around 5 min). Furthermore, we compared measured carbon fluxes with those from an eddy covariance tower at Dagow Lake where CH₄ fluxes diverged slightly, possibly linked to the usage of different sensors (closed-path versus open-path), while magnitudes of CO₂ fluxes matched well. At both lakes, we identified areas of higher emissions, especially for CH₄, and were thus able to resolve the spatial variability of carbon fluxes within the waterbodies. Concurrently, we measured differences in meteorological conditions, and critical limnological variables (water temperature, specific conductivity, pH, dissolved oxygen, chlorophyll, phycocyanin, turbidity, and fluorescent dissolved organic matter) – valuable measurements that enable a comprehensive assessment of environmental drivers behind flux variability. We conclude that platforms like the BlueMinerva have the potential to be adopted widely by scientists and stakeholders to better capture biogeochemical processes in lakes at high spatial and temporal resolution.

Carbon emissions from disturbances in the Arctic-Boreal Zone (ABZ) are not adequately included in global carbon budgets due to insufficient measurements and model representation. Due to this data gap, we conducted an expert assessment to examine the potential trajectories of multiple disturbance types in the ABZ and their impacts on net carbon fluxes over the next one to three centuries. Across five disturbance categories – anthropogenic (e.g., oil and gas infrastructure), biotic, fire, abrupt permafrost thaw, and weather – experts agreed that the extent, frequency, range, and severity of disturbances will increase for at least the next century. Taken together, our assessment suggests that ABZ disturbances could result in 154–417 TgC yr ^− 1 (median expert estimates) greater net carbon emissions by 2100 relative to 2000–2020 for three climate change mitigation scenarios – global mean temperature increase held below (1) 1.5°C by 2100, (2) 2°C by 2100, and (3) Representative Concentration Pathway (RCP) 8.5 (~ 2.6–4.8°C by 2100 with rising temperatures beyond that). Critically, the experts agreed these impacts will persist long after global net zero anthropogenic emissions are reached, causing continued carbon emissions that are currently unaccounted for. Key research priorities for reducing uncertainty in these estimates were 1) improved spatio-temporal monitoring of ABZ disturbances to quantify their extent and distribution, 2) increased measurements of ecosystem carbon fluxes during and following disturbance events, and 3) incorporation of disturbance processes into models.

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).

Understanding how heterogeneous landscapes contribute to ecosystem‐exchange fluxes remains a significant challenge for utilizing eddy covariance (EC) measurements. This work presents FLUGS, a novel framework that infers land‐cover‐specific ecosystem‐exchange fluxes provided the EC time series of aggregated fluxes and the land cover map of the ecosystem. Using a multitask machine learning approach based on Kernel Ridge Regression combined with high‐resolution flux footprints, FLUGS learns the environmental response functions (ERFs) from EC data for each land cover class simultaneously. FLUGS integrates a state‐of‐the‐art atmospheric transport model that provides accurate, computationally efficient footprint estimates with a statistically rigorous, convex optimization method for submeso‐scale flux inversion. The approach is versatile, robust to multicollinearity and yields smooth and interpretable ERFs with a unique global optimum. We evaluate FLUGS using virtual and physical experiments derived from synthetic data and observational data from an EC site in Northeast Siberia. Across all tests, the framework accurately reconstructs land‐cover‐specific sensible heat and fluxes and captures systematic differences between land cover classes that reflect local hydrological and ecological conditions. FLUGS also provides statistical performance metrics and cross‐tower consistency diagnostics that quantify uncertainty in heterogeneous landscapes. By offering a fast, transparent workflow for spatially decomposing ecosystem fluxes, FLUGS opens new opportunities to attribute EC fluxes to ecological processes, benchmark land‐surface models and improve our understanding of land‐atmosphere interaction.

Water-air fluxes of carbon dioxide (CO₂) and methane (CH₄) in lakes exhibit substantial spatial heterogeneity, often varying across remarkably fine spatial scales. While manual flux chamber measurements offer high spatial resolution and the potential to capture this variability, their application is typically constrained by labor intensity and logistical limitations. In contrast, eddy covariance (EC) measurements integrate fluxes over a larger footprint, effectively averaging over spatial gradients and complicating a process-based interpretation of the data. Bridging this gap requires methods that combine spatial precision with scalable, continuous monitoring – essential for advancing our mechanistic understanding of lake carbon dynamics. To facilitate highly resolved biogeochemical measurements, we developed BlueMinerva, an autonomous platform to monitor surface carbon fluxes with the static chamber method, and simultaneously determine physicochemical, biological, bathymetric, and meteorological variables at pre-defined locations. The platform can be programmed to navigate a user-defined track across a waterbody, and collect flux and ancillary data both in transit and at fixed locations for several hours to days, depending on the sensor configuration and related battery requirements. We deployed the setup at Dagow Lake (Germany) and in a small lake in the Stordalen Mire (Sweden). In total, we obtained 485 chamber-derived flux estimates over 72 measurement hours. We compared CO₂ flux estimates between measurements with two different gas analyzers that were simultaneously mounted on the BlueMinerva. The lower-cost sensor (CARBOCAP GMP343, Vaisala) performed equally well as the precise, but costlier sensor (LI-7810, LI-COR) as long as measurements were sufficiently long (around 5 min). Furthermore, we compared measured carbon fluxes with those from an eddy covariance tower at Dagow Lake where CH₄ fluxes diverged slightly, possibly linked to the usage of different sensors (closed-path versus open-path), while magnitudes of CO₂ fluxes matched well. At both lakes, we identified areas of higher emissions, especially for CH₄, and were thus able to resolve the spatial variability of carbon fluxes within the waterbodies. Concurrently, we measured differences in meteorological conditions, and critical limnological variables (water temperature, specific conductivity, pH, dissolved oxygen, chlorophyll, phycocyanin, turbidity, and fluorescent dissolved organic matter) – valuable measurements that enable a comprehensive assessment of environmental drivers behind flux variability. We conclude that platforms like the BlueMinerva have the potential to be adopted widely by scientists and stakeholders to better capture biogeochemical processes in lakes at high spatial and temporal resolution.

Water-air fluxes of carbon dioxide (CO₂) and methane (CH₄) in lakes exhibit substantial spatial heterogeneity, often varying across remarkably fine spatial scales. While manual flux chamber measurements offer high spatial resolution and the potential to capture this variability, their application is typically constrained by labor intensity and logistical limitations. In contrast, eddy covariance (EC) measurements integrate fluxes over a larger footprint, effectively averaging over spatial gradients and complicating a process-based interpretation of the data. Bridging this gap requires methods that combine spatial precision with scalable, continuous monitoring – essential for advancing our mechanistic understanding of lake carbon dynamics. To facilitate highly resolved biogeochemical measurements, we developed BlueMinerva, an autonomous platform to monitor surface carbon fluxes with the static chamber method, and simultaneously determine physicochemical, biological, bathymetric, and meteorological variables at pre-defined locations. The platform can be programmed to navigate a user-defined track across a waterbody, and collect flux and ancillary data both in transit and at fixed locations for several hours to days, depending on the sensor configuration and related battery requirements. We deployed the setup at Dagow Lake (Germany) and in a small lake in the Stordalen Mire (Sweden). In total, we obtained 485 chamber-derived flux estimates over 72 measurement hours. We compared CO₂ flux estimates between measurements with two different gas analyzers that were simultaneously mounted on the BlueMinerva. The lower-cost sensor (CARBOCAP GMP343, Vaisala) performed equally well as the precise, but costlier sensor (LI-7810, LI-COR) as long as measurements were sufficiently long (around 5 min). Furthermore, we compared measured carbon fluxes with those from an eddy covariance tower at Dagow Lake where CH₄ fluxes diverged slightly, possibly linked to the usage of different sensors (closed-path versus open-path), while magnitudes of CO₂ fluxes matched well. At both lakes, we identified areas of higher emissions, especially for CH₄, and were thus able to resolve the spatial variability of carbon fluxes within the waterbodies. Concurrently, we measured differences in meteorological conditions, and critical limnological variables (water temperature, specific conductivity, pH, dissolved oxygen, chlorophyll, phycocyanin, turbidity, and fluorescent dissolved organic matter) – valuable measurements that enable a comprehensive assessment of environmental drivers behind flux variability. We conclude that platforms like the BlueMinerva have the potential to be adopted widely by scientists and stakeholders to better capture biogeochemical processes in lakes at high spatial and temporal resolution.

Water-air fluxes of carbon dioxide (CO₂) and methane (CH₄) in lakes exhibit substantial spatial heterogeneity, often varying across remarkably fine spatial scales. While manual flux chamber measurements offer high spatial resolution and the potential to capture this variability, their application is typically constrained by labor intensity and logistical limitations. In contrast, eddy covariance (EC) measurements integrate fluxes over a larger footprint, effectively averaging over spatial gradients and complicating a process-based interpretation of the data. Bridging this gap requires methods that combine spatial precision with scalable, continuous monitoring – essential for advancing our mechanistic understanding of lake carbon dynamics. To facilitate highly resolved biogeochemical measurements, we developed BlueMinerva, an autonomous platform to monitor surface carbon fluxes with the static chamber method, and simultaneously determine physicochemical, biological, bathymetric, and meteorological variables at pre-defined locations. The platform can be programmed to navigate a user-defined track across a waterbody, and collect flux and ancillary data both in transit and at fixed locations for several hours to days, depending on the sensor configuration and related battery requirements. We deployed the setup at Dagow Lake (Germany) and in a small lake in the Stordalen Mire (Sweden). In total, we obtained 485 chamber-derived flux estimates over 72 measurement hours. We compared CO₂ flux estimates between measurements with two different gas analyzers that were simultaneously mounted on the BlueMinerva. The lower-cost sensor (CARBOCAP GMP343, Vaisala) performed equally well as the precise, but costlier sensor (LI-7810, LI-COR) as long as measurements were sufficiently long (around 5 min). Furthermore, we compared measured carbon fluxes with those from an eddy covariance tower at Dagow Lake where CH₄ fluxes diverged slightly, possibly linked to the usage of different sensors (closed-path versus open-path), while magnitudes of CO₂ fluxes matched well. At both lakes, we identified areas of higher emissions, especially for CH₄, and were thus able to resolve the spatial variability of carbon fluxes within the waterbodies. Concurrently, we measured differences in meteorological conditions, and critical limnological variables (water temperature, specific conductivity, pH, dissolved oxygen, chlorophyll, phycocyanin, turbidity, and fluorescent dissolved organic matter) – valuable measurements that enable a comprehensive assessment of environmental drivers behind flux variability. We conclude that platforms like the BlueMinerva have the potential to be adopted widely by scientists and stakeholders to better capture biogeochemical processes in lakes at high spatial and temporal resolution.

The Arctic-boreal region’s carbon balance is shifting, with major implications for global climate. Models predict high carbon emissions by 2100, consisting of greenhouse gases, carbon dioxide (CO ₂ ) and methane (CH ₄ ), thus a representative carbon flux monitoring network is crucial to constrain fluxes. Eddy flux towers are increasingly used to inform models, and the area towers represent define model limitations. The Arctic-boreal flux tower network has grown from one to over 100 over past decades, however, towers are not designed to have broad regional representation. We analyzed flux tower network improvements made between 2022 and 2024, when targeted sites were added in part by the Permafrost Pathways project. We assessed the representativeness of active sites for four measurement categories: growing season CO ₂ , growing season CH ₄ , year-round CO ₂ , and year-round CH ₄ . Sixteen new or upgraded sites improved representation over 23–55% of the Arctic-boreal region across categories. Seventy-five percent of the region was well represented given improvements in growing season CO ₂ measurements; however, only 45% of the domain was well represented for year-round CH ₄ . We show targeted improvements can obtain better representativeness with limited sites, but continued upgrades are necessary to close gaps, particularly in Russia where gaps remain large.

Understanding how greenhouse gases and pollutants move through the atmosphere is essential for predicting and mitigating their effects. We present the Boundary Layer Dispersion and Footprint Model (BLDFM), which solves the three-dimensional steady-state advection-diffusion equation in Eulerian form using a numerical approach based on the Fourier, linear shooting, and exponential integrator methods. In contrast to analytical Gaussian plume or stochastic Lagrangian models, this approach avoids several asymptotic assumptions required for closed-form solutions, such as the slender-plume approximation and power-law profiles. BLDFM relies solely on a steady-state Reynolds-averaged advection-diffusion equation with K-theory closure and horizontally homogeneous profiles that depend only on height. BLDFM is modular, decoupling the turbulence closure from the transport solver. The flexibility and modularity of the model may enable a wide range of applications, including climate impact studies, industrial emissions monitoring, and spatial flux attribution. We verify the numerical solver against an analytical test case and find excellent agreement. We also compare BLDFM with the well-established Kormann and Meixner (2001) footprint model (KM01), based on the analytical Gaussian plume. The results show overall good agreement, but some differences in the fetch of the footprints, attributed to KM01's neglect of streamwise turbulent mixing. Our results demonstrate the potential of BLDFM as a useful tool for atmospheric scientists, biogeochemists, ecologists, and engineers.