Ivan Mammarella

Coasts are globally identified as major sinks of carbon. Still, the carbon balance in the coastal Baltic Sea remains unsolved. We analysed almost three years of continuous eddy covariance measurements of carbon dioxide fluxes in the coastal zone of the northern Baltic Sea in 2022–2024. Our results show that this coastal site acted as a small source of carbon dioxide into the atmosphere. The gap-filled carbon balances for 2023 and 2024 were 24.3 ± 0.3 g C m⁻² and 19.2 ± 0.3 g C m⁻², respectively. These results are comparable with earlier results by eddy covariance in the Baltic Sea. The measurements also reveal a consistent seasonal pattern of fluxes among the years, where the sea acts as a sink of carbon dioxide in April–May, as a variable source and sink in June–September, and a source in October–March. Maximum rates of monthly uptake were measured in April and the maximum emission values in November. Data gaps were filled using a random forest model which identified wind speed and sea salinity as the most important environmental co-variates of the carbon dioxide flux. Additionally, it was shown that the non-gapfilled monthly and yearly sums are offset due to lack of night-time data and thus uneven diurnal data coverage and that gap-filling is essential in calculating the yearly balances of fluxes.

Flux measurements have started to play an important role outside academia in assessing carbon sinks of different ecosystems and land-use types. If natural carbon solutions are to be deployed and monetized in carbon markets, more low-powered and low-cost flux systems should be deployed. There is a growing need for low-cost sensors that still fulfil the requirements for scientific applications. We present a case study that aimed to develop an inexpensive yet precise fast-response carbon dioxide (CO ₂ ) and water vapour (H ₂ O) sensor. A working prototype was field-tested against a scientific reference eddy covariance (EC) setup. Special attention was paid to response time, lowered sampling frequency, and auto-calibration related to the temperature. The enclosed-path EC prototype achieved a CO ₂ response time of 0.18 s and a noise level of 1 ppm at a 5 Hz sampling rate. The internal auto-calibration procedure was improved throughout the project such that CO ₂ signal drifting was avoided and the instrument was capable of measuring CO ₂ fluxes with high correlation relative to the reference EC setup ( R ² =0.96 during high-flux season).

Upland terrestrial soils have traditionally been regarded as a consistent sink for atmospheric methane (CH₄) in the global budget (GMB), removing an estimated 11–49 Tg CH₄ yr⁻¹. The inclusion of the terrestrial soil-sink as the dominant process describing the net atmospheric exchange of upland ecosystems relies on the key assumption that it equals the upland terrestrial budget. In this study, we used fluxes from 46 National Ecological Observatory Network tower sites across North America from 2021 to 2024 to characterize ecosystem-scale CH₄ exchange. Contrary to the terrestrial soil-sink expectation, 83% of upland sites exhibited net sources of CH₄ annually ranging from + 0.005 to + 1.09 g C m⁻² yr⁻¹, while only 17% of sites functioned as net sinks (-0.05 to -0.21 g C m⁻² yr⁻¹). The upland ecosystem sink was, at best, ~ 48% of the assumed GMB soil sink and never reached the GMB estimate. The soil sink and the upland terrestrial methane budget are distinct quantities defined by different scales of inference and measurement domains. Assuming they are equivalent in the GMB is a scale-inference error—a scale mismatch between the measurement domain and the inference 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).

Taylor’s hypothesis (TH) converts temporal observations to spatial information of the flow while carrying out measurements on a micrometeorological tower. Other than TH, there exists a more general elliptic model, which converts time to space by focusing on the geometry of the space–time correlation function. In the elliptic model, TH is recovered when the space–time correlation functions are straight lines, and when TH is invalid, they are approximated as elliptic curves. To test whether TH or an elliptic model was appropriate for a highly heterogeneous forest clear-cut flow, we examined the geometry of the space–time correlation function of temperature by using an extensive distributed temperature sensing (DTS) dataset collected during daytime convective conditions at a height of 3.1 m above the clearing. This was complemented with an eddy covariance (EC) dataset that measured the turbulence characteristics. When the mean wind was parallel to a nearby forest edge, the DTS-derived space–time correlation function of temperature fluctuations resembled elliptic curves, rather than straight lines as predicted by TH. Due to large turbulence intensities, the curvatures in the space–time correlation contours were caused by the random sweeping events associated with large-scale eddies that invalidated the frozen turbulence assumption in TH. The velocity scale of these sweeping events correlated with the turbulence kinetic energy of the clear-cut flow, thereby lending support to the random sweeping hypothesis. Upon converting time to space through an elliptic and TH-based scaling, our results demonstrated that both temporal DTS and EC temperature measurements were impacted by the random sweeping events. Significance Statement: The near-surface turbulence measurements serve as a cornerstone in land–atmosphere interaction research. Without spatial information, to associate these temporal measurements with spatial scales, one applies Taylor’s hypothesis (TH) by assuming that the space–time conversion can be achieved through the local mean wind speed. However, this hypothesis fails when the turbulence intensities are large and random sweeping effects associated with large-scale eddies invalidate the frozen turbulence assumption in TH. To account for this sweeping effect, we employ an elliptic model of space–time correlation in a forest clear-cut flow. The results suggest that in a highly turbulent and heterogeneous forest clear-cut flow, the relationship between space and time is more closely approximated as an ellipse rather than a simple linear one.

Climate change influences cryospheric conditions in the boreal zone, which in turn affect carbon uptake and release by vegetation. Knowledge about these interactions is important to enhance understanding of how the carbon cycle in this region responds to climate change and to accurately assess its impact on the carbon balance. Based on a 10-year time series of CO2 flux measurements from five boreal evergreen forests in Finland and Canada, we investigated the relationship between yearly anomalies in snow melt, soil freeze and thaw and anomalies in the start- and end-of-season dates calculated from gross primary production and terrestrial ecosystem respiration. We used both in-situ and satellite-based information. Soil freeze and thaw, and snow melt dates were estimated from the ESA SMOS Level 3 Soil Freeze and Thaw, and the ESA Climate Change Initiative snow cover fraction on the ground data, respectively. At the investigated forest sites, we observed a time lag between the seasonal cycle of photosynthesis and respiration. Year-to-year changes in the timing of recovery of photosynthesis and ecosystem respiration were well correlated with year-to-year changes in snow melt and soil thaw dates, both from in-situ and satellite observations. Earlier snow melt and soil thaw were associated with higher annual carbon uptake. This study highlights linkages between carbon cycle and cryospheric variables. Satellite-based information on snow melt, soil freeze and thaw can support spatial evaluations of carbon balance model results, the upscaling of carbon fluxes and are important variables in ecological studies in northern regions.

Coasts are globally identified as major sinks of carbon. Still, the carbon balance in the coastal Baltic Sea remains unsolved. We analysed almost three years of continuous eddy covariance measurements of carbon dioxide fluxes in the coastal zone of the northern Baltic Sea in 2022–2024. Our results show that this coastal site acted as a small source of carbon dioxide into the atmosphere. The gap-filled carbon balances for 2023 and 2024 were 24.3 ± 0.3 g C m⁻² and 19.2 ± 0.3 g C m⁻², respectively. These results are comparable with earlier results by eddy covariance in the Baltic Sea. The measurements also reveal a consistent seasonal pattern of fluxes among the years, where the sea acts as a sink of carbon dioxide in April–May, as a variable source and sink in June–September, and a source in October–March. Maximum rates of monthly uptake were measured in April and the maximum emission values in November. Data gaps were filled using a random forest model which identified wind speed and sea salinity as the most important environmental co-variates of the carbon dioxide flux. Additionally, it was shown that the non-gapfilled monthly and yearly sums are offset due to lack of night-time data and thus uneven diurnal data coverage and that gap-filling is essential in calculating the yearly balances of fluxes.

Benchmark datasets for evaluating profile-based ecosystem flux methods across environmental gradients are currently lacking. This limits method evaluation, cross-site intercomparison, and the expansion of tower-based monitoring for gases not routinely measured by the eddy covariance (EC) method. Here, we present a benchmark dataset derived from 47 terrestrial towers in the National Ecological Observatory Network (NEON), integrating co-located EC, concentration profiles, tower geometry, and canopy structural metrics. We developed a dataset to evaluate the performance of three widely used gradient flux approaches – the modified Bowen ratio (MBR), aerodynamic (AE), and wind-profile (WP) methods – against co-located EC measurements of CO₂ and H₂O. We evaluate how canopy structure, sensor height configuration, and data filtering influence agreement with EC across ecosystems, with canopy heights ranging from 0.15 to 53 m. Performance varied strongly among approaches and measurement configurations. Across all ecosystems, 11% of height-pair combinations for CO₂ and 19% for H₂O achieved moderate-to-strong concordance with EC, with the MBR approach providing the most consistent performance. Reliable estimates most often occurred when both sampling heights were above the canopy (AA) or when one level was above the canopy and the other within sparse canopies (AW). Concordance declined as canopy complexity increased, highlighting persistent challenges in tall, complex forests. Since filtering substantially reduced data availability, we developed an ensemble framework that combined reliable estimates across the three GF approaches and height pairs. Ensemble GF fluxes reproduced seasonal diel dynamics measured by EC across ecosystems (R2 = 0.58–0.99), capturing both the seasonal magnitude and direction of net ecosystem exchange. Benchmark analyses show that the GF method can provide robust ecosystem-scale flux information when deployed under favorable structural and micrometeorological conditions. The released benchmark dataset provides a resource for testing new flux algorithms, optimizing sensor placement, benchmarking profile methods, and extending multi-gas tower observations to be more inclusive of gases that are difficult to measure with fast-response EC instrumentation, including CH₄, N₂O, volatile organic compounds, and reactive pollutants.

Coasts are globally identified as major sinks of carbon. Still, the carbon balance in the coastal Baltic Sea remains unsolved. We analysed almost three years of continuous eddy covariance measurements of carbon dioxide fluxes in the coastal zone of the northern Baltic Sea in 2022–2024. Our results show that this coastal site acted as a small source of carbon dioxide into the atmosphere. The gap-filled carbon balances for 2023 and 2024 were 24.3 ± 0.3 g C m−2 and 19.2 ± 0.3 g C m−2, respectively. These results are comparable with earlier results by eddy covariance in the Baltic Sea. The measurements also reveal a consistent seasonal pattern of fluxes among the years, where the sea acts as a sink of carbon dioxide in April–May, as a variable source and sink in June–September, and a source in October–March. Maximum rates of monthly uptake were measured in April and the maximum emission values in November. Data gaps were filled using a random forest model which identified wind speed and sea salinity as the most important environmental co-variates of the carbon dioxide flux. Additionally, it was shown that the non-gapfilled monthly and yearly sums are offset due to lack of night-time data and thus uneven diurnal data coverage and that gap-filling is essential in calculating the yearly balances of fluxes.