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Abstract The Arctic–Boreal Zone is rapidly warming, impacting its large soil carbon stocks. Here we use a new compilation of terrestrial ecosystem CO₂fluxes, geospatial datasets and random forest models to show that although the Arctic–Boreal Zone was overall an increasing terrestrial CO₂sink from 2001 to 2020 (mean ± standard deviation in net ecosystem exchange, −548 ± 140 Tg C yr⁻¹; trend, −14 Tg C yr⁻¹;P < 0.001), more than 30% of the region was a net CO₂source. Tundra regions may have already started to function on average as CO₂sources, demonstrating a shift in carbon dynamics. When fire emissions are factored in, the increasing Arctic–Boreal Zone sink is no longer statistically significant (budget, −319 ± 140 Tg C yr⁻¹; trend, −9 Tg C yr⁻¹), and the permafrost region becomes CO₂neutral (budget, −24 ± 123 Tg C yr⁻¹; trend, −3 Tg C yr⁻¹), underscoring the importance of fire in this region. 

Abstract Climate change is having significant impacts on Earth’s ecosystems and carbon budgets, and in the Arctic may drive a shift from an historic carbon sink to a source. Large uncertainties in terrestrial biosphere models (TBMs) used to forecast Arctic changes demonstrate the challenges of determining the timing and extent of this possible switch. This spread in model predictions can limit the ability of TBMs to guide management and policy decisions. One of the most influential sources of model uncertainty is model parameterization. Parameter uncertainty results in part from a mismatch between available data in databases and model needs. We identify that mismatch for three TBMs, DVM-DOS-TEM, SIPNET and ED2, and four databases with information on Arctic and boreal above- and belowground traits that may be applied to model parametrization. However, focusing solely on such data gaps can introduce biases towards simple models and ignores structural model uncertainty, another main source for model uncertainty. Therefore, we develop a causal loop diagram (CLD) of the Arctic and boreal ecosystem that includes unquantified, and thus unmodeled, processes. We map model parameters to processes in the CLD and assess parameter vulnerability via the internal network structure. One important substructure, feed forward loops (FFLs), describe processes that are linked both directly and indirectly. When the model parameters are data-informed, these indirect processes might be implicitly included in the model, but if not, they have the potential to introduce significant model uncertainty. We find that the parameters describing the impact of local temperature on microbial activity are associated with a particularly high number of FFLs but are not constrained well by existing data. By employing ecological models of varying complexity, databases, and network methods, we identify the key parameters responsible for limited model accuracy. They should be prioritized for future data sampling to reduce model uncertainty. 

Abstract Climate change is rapidly altering composition, structure, and functioning of the boreal biome, across North America often broadly categorized into ecoregions. The resulting complex changes in different ecoregions present a challenge for efforts to accurately simulate carbon dioxide (CO₂) and energy exchanges between boreal forests and the atmosphere with terrestrial ecosystem models (TEMs). Eddy covariance measurements provide valuable information for evaluating the performance of TEMs and guiding their development. Here, we compiled a boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from eight black spruce (Picea mariana)-dominated, mature forest stands. The eight forest stands, located in six boreal ecoregions of North America, differ in stand characteristics, disturbance history, climate, permafrost conditions and soil properties. By compiling various data streams, the benchmarking dataset comprises data to parameterize, force, and evaluate TEMs. Specifically, it includes half-hourly, gap-filled meteorological forcing data, ancillary data essential for model parameterization, and half-hourly, gap-filled or partitioned component flux data on CO₂(net ecosystem production, gross primary production [GPP], and ecosystem respiration [ER]) and energy (latent [LE] and sensible heat [H]) and their daily aggregates screened based on half-hourly gap-filling quality criteria. We present a case study with the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) to: (1) demonstrate the utility of our dataset to benchmark TEMs and (2) provide guidance for model development and refinement. Model skill was evaluated using several statistical metrics and further examined through the flux responses to their environmental controls. Our results suggest that CLASSIC tended to overestimate GPP and ER among all stands. Model performance regarding the energy fluxes (i.e., LE and H) varied greatly among the stands and exhibited a moderate correlation with latitude. We identified strong relationships between simulated fluxes and their environmental controls except for H, thus highlighting current strengths and limitations of CLASSIC. 

Abstract Permafrost degradation in peatlands is altering vegetation and soil properties and impacting net carbon storage. We studied four adjacent sites in Alaska with varied permafrost regimes, including a black spruce forest on a peat plateau with permafrost, two collapse scar bogs of different ages formed following thermokarst, and a rich fen without permafrost. Measurements included year‐round eddy covariance estimates of net carbon dioxide (CO₂), mid‐April to October methane (CH₄) emissions, and environmental variables. From 2011 to 2022, annual rainfall was above the historical average, snow water equivalent increased, and snow‐season duration shortened due to later snow return. Seasonally thawed active layer depths also increased. During this period, all ecosystems acted as slight annual sources of CO₂(13–59 g C m⁻² year⁻¹) and stronger sources of CH₄(11–14 g CH₄ m⁻²from ~April to October). The interannual variability of net ecosystem exchange was high, approximately ±100 g C m⁻² year⁻¹, or twice what has been previously reported across other boreal sites. Net CO₂release was positively related to increased summer rainfall and winter snow water equivalent and later snow return. Controls over CH₄emissions were related to increased soil moisture and inundation status. The dominant emitter of carbon was the rich fen, which, in addition to being a source of CO₂, was also the largest CH₄emitter. These results suggest that the future carbon‐source strength of boreal lowlands in Interior Alaska may be determined by the area occupied by minerotrophic fens, which are expected to become more abundant as permafrost thaw increases hydrologic connectivity. Since our measurements occur within close proximity of each other (≤1 km²), this study also has implications for the spatial scale and data used in benchmarking carbon cycle models and emphasizes the necessity of long‐term measurements to identify carbon cycle process changes in a warming climate. 

Abstract Boreal‐Arctic regions are key stores of organic carbon (C) and play a major role in the greenhouse gas balance of high‐latitude ecosystems. The carbon‐climate (C‐climate) feedback potential of northern high‐latitude ecosystems remains poorly understood due to uncertainty in temperature and precipitation controls on carbon dioxide (CO₂) uptake and the decomposition of soil C into CO₂and methane (CH₄) fluxes. While CH₄fluxes account for a smaller component of the C balance, the climatic impact of CH₄outweighs CO₂(28–34 times larger global warming potential on a 100‐year scale), highlighting the need to jointly resolve the climatic sensitivities of both CO₂and CH₄. Here, we jointly constrain a terrestrial biosphere model with in situ CO₂and CH₄flux observations at seven eddy covariance sites using a data‐model integration approach to resolve the integrated environmental controls on land‐atmosphere CO₂and CH₄exchanges in Alaska. Based on the combined CO₂and CH₄flux responses to climate variables, we find that 1970‐present climate trends will induce positive C‐climate feedback at all tundra sites, and negative C‐climate feedback at the boreal and shrub fen sites. The positive C‐climate feedback at the tundra sites is predominantly driven by increased CH₄emissions while the negative C‐climate feedback at the boreal site is predominantly driven by increased CO₂uptake (80% from decreased heterotrophic respiration, and 20% from increased photosynthesis). Our study demonstrates the need for joint observational constraints on CO₂and CH₄biogeochemical processes—and their associated climatic sensitivities—for resolving the sign and magnitude of high‐latitude ecosystem C‐climate feedback in the coming decades. 

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under- sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups. 

Abstract. Snow sublimation plays a fundamental role in the winter water balance. To date, few studies have quantified sublimation in tundra and boreal forest snow by direct measurements. Continuous latent heat data collected with eddy covariance (EC) measurements from 2010–2021 were used to calculate snow sublimation at six locations in northern Alaska: three Arctic tundra sites at distinct topographical and vegetation communities in the Imnavait Creek watershed on the North Slope underlain by continuous permafrost, and three lowland boreal forest/taiga sites in discontinuous permafrost in interior Alaska near Fairbanks. Mean surface sublimation rates range from 0.08–0.15 mm d−1 and 15–27 mm yr−1 at the six sites, representing, on average, 21 % of the measured solid precipitation and 8 %–16 % of the cumulative annual water vapor flux to the atmosphere (evaporation plus sublimation). The mean daily sublimation rates of the lowland boreal forest sites are higher than those of the tundra sites, but the longer snow cover period of the tundra sites leads to greater mean annual sublimation rates. We examined the potential controls, drivers, and trends of the sublimation rates by using meteorological data collected in conjunction with EC measurements. This research improves our understanding of how site conditions affect sublimation rates and highlights the fact that sublimation is a substantial component of the winter hydrologic cycle. In addition, the study contributes to the sparse literature on tundra and boreal sublimation measurements, and the measured rates are comparable to sublimation estimates in other northern climates. 

Small uncrewed aerial systems (sUASs) can be used to quantify emissions of greenhouse and other gases, providing flexibility in quantifying these emissions from a multitude of sources, including oil and gas infrastructure, volcano plumes, wildfire emissions, and natural sources. However, sUAS-based emission estimates are sensitive to the accuracy of wind speed and direction measurements. In this study, we examined how filtering and correcting sUAS-based wind measurements affects data accuracy by comparing data from a miniature ultrasonic anemometer mounted on a sUAS in a joust configuration to highly accurate wind data taken from a nearby eddy covariance flux tower (aka the Tower). These corrections had a small effect on wind speed error, but reduced wind direction errors from 50° to >120° to 20–30°. A concurrent experiment examining the amount of error due to the sUAS and the Tower not being co-located showed that the impact of this separation was 0.16–0.21 ms−1, a small influence on wind speed errors. Lower wind speed errors were correlated with lower turbulence intensity and higher relative wind speeds. There were also some loose trends in diminished wind direction errors at higher relative wind speeds. Therefore, to improve the quality of sUAS-based wind measurements, our study suggested that flight planning consider optimizing conditions that can lower turbulence intensity and maximize relative wind speeds as well as include post-flight corrections. 

Abstract. Wetlands are the largest natural source of methane (CH4) emissions globally. Northern wetlands (>45° N), accounting for 42 % of global wetland area, are increasingly vulnerable to carbon loss, especially as CH4 emissions may accelerate under intensified high-latitude warming. However, the magnitude and spatial patterns of high-latitude CH4 emissions remain relatively uncertain. Here, we present estimates of daily CH4 fluxes obtained using a new machine learning-based wetland CH4 upscaling framework (WetCH4) that combines the most complete database of eddy-covariance (EC) observations available to date with satellite remote-sensing-informed observations of environmental conditions at 10 km resolution. The most important predictor variables included near-surface soil temperatures (top 40 cm), vegetation spectral reflectance, and soil moisture. Our results, modeled from 138 site years across 26 sites, had relatively strong predictive skill, with a mean R2 of 0.51 and 0.70 and a mean absolute error (MAE) of 30 and 27 nmol m−2 s−1 for daily and monthly fluxes, respectively. Based on the model results, we estimated an annual average of 22.8±2.4 Tg CH4 yr−1 for the northern wetland region (2016–2022), and total budgets ranged from 15.7 to 51.6 Tg CH4 yr−1, depending on wetland map extents. Although 88 % of the estimated CH4 budget occurred during the May–October period, a considerable amount (2.6±0.3 Tg CH4) occurred during winter. Regionally, the Western Siberian wetlands accounted for a majority (51 %) of the interannual variation in domain CH4 emissions. Overall, our results provide valuable new high-spatiotemporal-resolution information on the wetland emissions in the high-latitude carbon cycle. However, many key uncertainties remain, including those driven by wetland extent maps and soil moisture products and the incomplete spatial and temporal representativeness in the existing CH4 flux database; e.g., only 23 % of the sites operate outside of summer months, and flux towers do not exist or are greatly limited in many wetland regions. These uncertainties will need to be addressed by the science community to remove the bottlenecks currently limiting progress in CH4 detection and monitoring. The dataset can be found at https://doi.org/10.5281/zenodo.10802153 (Ying et al., 2024).