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Abstract Ecosystems at high latitudes are changing rapidly in response to climate change. To understand changes in carbon fluxes across seasonal to multi‐decadal timescales, long‐term in situ measurements from eddy covariance networks are needed. However, there are large spatiotemporal gaps in the high‐latitude eddy covariance network. Here we used the relative extrapolation error index in machine learning‐based upscaled gross primary production as a measure of network representativeness and as the basis for a network optimization. We show that the relative extrapolation error index has steadily decreased from 2001 to 2020, suggesting diminishing upscaling errors. In experiments where we limit site activity by either setting a maximum duration or by ending measurements at a fixed time those errors increase significantly, in some cases setting the network status back more than a decade. Our experiments also show that with equal site activity across different theoretical network setups, a more spread out design with shorter‐term measurements functions better in terms of larger‐scale representativeness than a network with fewer long‐term towers. We developed a method to select optimized site additions for a network extension, which blends an objective modeling approach with expert knowledge. This method greatly outperforms an unguided network extension and can compensate for suboptimal human choices. For the Canadian Arctic we show several optimization scenarios and find that especially the Canadian high Arctic and north east tundra benefit greatly from addition sites. Overall, it is important to keep sites active and where possible make the extra investment to survey new strategic locations. 

Globally, winter temperatures are rising, and snowpack is shrinking or disappearing entirely. Despite previous research and published literature reviews, it remains unknown whether biomes across the globe will cross important thresholds in winter temperature and precipitation that will lead to significant ecological changes. Here, we combine the widely used Köppen–Geiger climate classification system with worst-case-scenario projected changes in global monthly temperature and precipitation to illustrate how multiple climatic zones across Earth may experience shifting winter conditions by the end of this century. We then examine how these shifts may affect ecosystems within corresponding biomes. Our analysis demonstrates potential widespread losses of extreme cold (<−20°C) in Arctic, boreal, and cool temperate regions. We also show the possible disappearance of freezing temperatures (<0°C) and large decreases in snowfall in warm temperate and dryland areas. We identify important and potentially irreversible ecological changes associated with crossing these winter climate thresholds. 

Abstract. Our understanding of how rapid Arctic warming and permafrost thaw affect global climate dynamics is restricted by limited spatio-temporal 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 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 linked with an interactive online tool (https://www.bgc-jena.mpg.de/argo/). 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.13870390) (Vogt et al., 2024). 

The positive Arctic–methane (CH₄) feedback forms when more CH₄is released from the Arctic tundra to warm the climate, further stimulating the Arctic to emit CH₄. This study utilized the CLM-Microbe model to project CH₄emissions across five distinct Arctic tundra ecosystems on the Alaska North Slope, considering three Shared Socioeconomic Pathway (SSP) scenarios using climate data from three climate models from 2016 to 2100. Employing a hyper-resolution of 5 m × 5 m within 40,000 m²domains accounted for the Arctic tundra’s high spatial heterogeneity; three sites were near Utqiaġvik (US-Beo, US-Bes, and US-Brw), with one each in Atqasuk (US-Atq) and Ivotuk (US-Ivo). Simulated CH₄emissions substantially increased by a factor of 5.3 to 7.5 under the SSP5–8.5 scenario compared to the SSP1–2.6 and SSP2–4.5 scenarios. The projected CH₄emissions exhibited a stronger response to rising temperature under the SSP5–8.5 scenario than under the SSP1–2.6 and SSP2–4.5 scenarios, primarily due to strong temperature dependence and the enhanced precipitation-induced expansion of anoxic conditions that promoted methanogenesis. The CH₄transport via ebullition and plant-mediated transport is projected to increase under all three SSP scenarios, and ebullition dominated CH₄transport by 2100 across five sites. Projected CH₄emissions varied in temperature sensitivity, with a Q₁₀range of 2.7 to 60.9 under SSP1–2.6, 3.8 to 17.6 under SSP2–4.5, and 5.7 to 17.2 under SSP5–8.5. Compared with the other three sites, US-Atq and US-Ivo were estimated to have greater increases in CH₄emissions due to warmer temperatures and higher precipitation. The fact that warmer sites and warmer climate scenarios had higher CH₄emissions suggests an intensified positive Arctic–CH₄feedback in the 21st century. Microbial physiology and substrate availability dominated the enhanced CH₄production. The simulated intensified positive feedback underscores the urgent need for a more mechanistic understanding of CH₄dynamics and the development of strategies to mitigate CH₄across the Arctic. 

Abstract Purpose of ReviewWhile previously thought to be negligible, carbon emissions during the non-growing season (NGS) can be a substantial part of the annual carbon budget in the Arctic boreal zone (ABZ), which can shift the carbon balance of these ecosystems from a long-held annual carbon sink towards a net annual carbon source. The purpose of this review is to summarize NGS carbon dioxide (CO₂) flux research in the ABZ that has been published within the past 5 years. Recent FindingsWe explore the processes and magnitudes of CO₂fluxes, and the status of modeling efforts, and evaluate future directions. With technological advances, direct measurements of NGS fluxes are increasing at sites across the ABZ over the past decade, showing ecosystems in the ABZ are a large source of CO₂in the shoulder seasons, with low, consistent, winter emissions. SummaryEcosystem carbon cycling models are being improved with some challenges, such as modeling below ground and snow processes, which are critical to understanding NGS CO₂fluxes. A lack of representative in situ carbon flux data and gridded environmental data are leading limiting factors preventing more accurate predictions of NGS carbon fluxes. 

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. The warming of the Arctic is affecting the carbon cycle of tundraecosystems. Most research on carbon fluxes from Arctic tundra ecosystems hasfocused on abiotic environmental controls (e.g., temperature, rainfall, orradiation). However, Arctic tundra vegetation, and therefore the carbonbalance of these ecosystems, can be substantially impacted by herbivory. Inthis study we tested how vegetation consumption by brown lemmings (Lemmus trimucronatus) canimpact carbon exchange of a wet-sedge tundra ecosystem near Utqiaġvik,Alaska during the summer and the recovery of vegetation during the followingsummer. We placed brown lemmings in individual enclosure plots and testedthe impact of lemmings' herbivory on carbon dioxide (CO2) fluxes, methane(CH4) fluxes, and the normalized difference vegetation index (NDVI)immediately after lemming removal and during the following growing season.During the first summer of the experiment, lemmings' herbivory reduced plantbiomass (as shown by the decrease in the NDVI) and decreased net CO2uptake while not significantly impacting CH4 emissions. CH4emissions were likely not significantly affected due to CH4 beingproduced deeper in the soil and escaping from the stem bases of the vascularplants. The summer following the lemming treatments, NDVI and net CO2fluxes returned to magnitudes similar to those observed before the start ofthe experiment, suggesting a complete recovery of the vegetation and atransitory nature of the impact of lemming herbivory. Overall, lemmingherbivory has short-term but substantial effects on carbon sequestration byvegetation and might contribute to the considerable interannual variabilityin CO2 fluxes from tundra ecosystems. 

Spatial heterogeneity in methane (CH 4 ) flux requires a reliable upscaling approach to reach accurate regional CH 4 budgets in the Arctic tundra. In this study, we combined the CLM-Microbe model with three footprint algorithms to scale up CH 4 flux from a plot level to eddy covariance (EC) tower domains (200 m × 200 m) in the Alaska North Slope, for three sites in Utqiaġvik (US-Beo, US-Bes, and US-Brw), one in Atqasuk (US-Atq) and one in Ivotuk (US-Ivo), for a period of 2013–2015. Three footprint algorithms were the homogenous footprint (HF) that assumes even contribution of all grid cells, the gradient footprint (GF) that assumes gradually declining contribution from center grid cells to edges, and the dynamic footprint (DF) that considers the impacts of wind and heterogeneity of land surface. Simulated annual CH 4 flux was highly consistent with the EC measurements at US-Beo and US-Bes. In contrast, flux was overestimated at US-Brw, US-Atq, and US-Ivo due to the higher simulated CH 4 flux in early growing seasons. The simulated monthly CH 4 flux was consistent with EC measurements but with different accuracies among footprint algorithms. At US-Bes in September 2013, RMSE and NNSE were 0.002 μmol m −2  s −1 and 0.782 using the DF algorithm, but 0.007 μmol m −2  s −1 and 0.758 using HF and 0.007 μmol m −2  s −1 and 0.765 using GF, respectively. DF algorithm performed better than the HF and GF algorithms in capturing the temporal variation in daily CH 4 flux each month, while the model accuracy was similar among the three algorithms due to flat landscapes. Temporal variations in CH 4 flux during 2013–2015 were predominately explained by air temperature (67–74%), followed by precipitation (22–36%). Spatial heterogeneities in vegetation fraction and elevation dominated the spatial variations in CH 4 flux for all five tower domains despite relatively weak differences in simulated CH 4 flux among three footprint algorithms. The CLM-Microbe model can simulate CH 4 flux at both plot and landscape scales at a high temporal resolution, which should be applied to other landscapes. Integrating land surface models with an appropriate algorithm provides a powerful tool for upscaling CH 4 flux in terrestrial ecosystems. 

Abstract. Understanding the sources and sinks of methane (CH4)is critical to both predicting and mitigating future climate change. Thereare large uncertainties in the global budget of atmospheric CH4, butnatural emissions are estimated to be of a similar magnitude toanthropogenic emissions. To understand CH4 flux from biogenic sourcesin the United States (US) of America, a multi-scale CH4 observationnetwork focused on CH4 flux rates, processes, and scaling methods isrequired. This can be achieved with a network of ground-based observationsthat are distributed based on climatic regions and land cover. To determinethe gaps in physical infrastructure for developing this network, we need tounderstand the landscape representativeness of the current infrastructure.We focus here on eddy covariance (EC) flux towers because they are essentialfor a bottom-up framework that bridges the gap between point-based chambermeasurements and airborne or satellite platforms that inform policydecisions and global climate agreements. Using dissimilarity,multidimensional scaling, and cluster analysis, the US was divided into 10clusters distributed across temperature and precipitation gradients. Weevaluated dissimilarity within each cluster for research sites with activeCH4 EC towers to identify gaps in existing infrastructure that limitour ability to constrain the contribution of US biogenic CH4 emissionsto the global budget. Through our analysis using climate, land cover, andlocation variables, we identified priority areas for research infrastructureto provide a more complete understanding of the CH4 flux potential ofecosystem types across the US. Clusters corresponding to Alaska and theRocky Mountains, which are inherently difficult to capture, are the mostpoorly represented, and all clusters require a greater representation ofvegetation types.