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Tundra and boreal ecosystems encompass the northern circumpolar permafrost region and are experiencing rapid environmental change with important implications for the global carbon (C) budget. We analysed multi-decadal time series containing 302 annual estimates of carbon dioxide (CO2) flux across 70 permafrost and non-permafrost ecosystems, and 672 estimates of summer CO2 flux across 181 ecosystems. We find an increase in the annual CO2 sink across non-permafrost ecosystems but not permafrost ecosystems, despite similar increases in summer uptake. Thus, recent non-growing-season CO2 losses have substantially impacted the CO2 balance of permafrost ecosystems. Furthermore, analysis of interannual variability reveals warmer summers amplify the C cycle (increase productivity and respiration) at putatively nitrogen-limited sites and at sites less reliant on summer precipitation for water use. Our findings suggest that water and nutrient availability will be important predictors of the C-cycle response of these ecosystems to future warming.

Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic-boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic-boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19% of the monthly observations), snow diffusion (3%) and eddy covariance (78%) techniques. The largest number of observations were collected during the climatological summer (June-August; 32%), and fewer observations were available for autumn (September-October; 25%), winter (December-February; 18%), and spring (March-May; 25%). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, 10.3334/ORNLDAAC/1934). Copyright:

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. Plain Language Summary: The Arctic is one of earth's regions most severely affected by climate change as ice sheets melt and permafrost thaws. This permafrost is rich in carbon which will be released in time. How fast and in which form this carbon is released is highly uncertain. Release as methane would, for example, cause greater global warming in the near future than if it were released as carbon dioxide. It is therefore important to properly measure this carbon release so society can properly prepare for its effects. But as the Arctic is remote and has extreme conditions, there are not so many measuring stations and they might not cover all essential regions. We investigate how well this network represents the Arctic, and where there might be gaps both in space and time. We find that it is essential to keep currently active sites running since if we were to stop measurements our ability to predict this carbon release would not only stagnate but rapidly decline. We developed a method to identify regions that are underrepresented and by incorporating expert knowledge and a new optimization algorithm we can find ideal locations to expand the monitoring network, to better prepare for the future. Key Points: The network of high‐latitude eddy covariance sites has expanded over time, still, sites should remain active and new remote locations addedWhen sites are discontinued, upscaling model skill declines over time, and more quickly in large networksNetwork optimization methods as shown here are a valuable tool to ensure a representative network design [ABSTRACT FROM AUTHOR]

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 applies the most complete database of eddy covariance (EC) observations available to date, and 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 reflectance, and soil moisture. Our results, modeled from 138 site-years across 26 sites, had relatively strong predictive skill with a mean R2 of 0.46 and 0.62 and a mean absolute error (MAE) of 23 nmol m-2 s-1 and 21 nmol m-2 s-1 for daily and monthly fluxes, respectively. Based on the model results, we estimated an annual average of 20.8 ±2.1 Tg CH4 yr-1 for the northern wetland region (2016–2022) and total budgets ranged from 13.7–44.1 Tg CH4 yr-1, depending on wetland map extents. Although 86 % of the estimated CH4 budget occurred during the May–October period, a considerable amount (1.4 ±0.2 Tg CH4) occurred during winter. Regionally, the West Siberian wetlands accounted for a majority (51 %) of the interannual variation in domain CH4 emissions. Significant issues with data coverage remain, with only 23 % of the sites observing year-round and most of the data from 11 wetland sites in Alaska and 10 bog/fen sites in Canada and Fennoscandia, and in general, Western Siberian Lowlands are underrepresented by EC CH4 sites. Our results provide high spatiotemporal information on the wetland emissions in the high-latitude carbon cycle and possible responses to climate change. Continued, all-season tower observations and improved soil moisture products are needed for future improvement of CH4 upscaling. The dataset can be found at https://doi.org/10.5281/zenodo.10802154 (Ying et al., 2024). [ABSTRACT FROM AUTHOR]

The northern permafrost region has been projected to shift from a net sink to a net source of carbon under global warming. However, estimates of the contemporary net greenhouse gas (GHG) balance and budgets of the permafrost region remain highly uncertain. Here, we construct the first comprehensive bottom-up budgets of CO2, CH4, and N2O across the terrestrial permafrost region using databases of more than 1000 in situ flux measurements and a land cover-based ecosystem flux upscaling approach for the period 2000-2020. Estimates indicate that the permafrost region emitted a mean annual flux of 12 (-606, 661) Tg CO2-C yr-1, 38 (22, 53) Tg CH4-C yr-1, and 0.67 (0.07, 1.3) Tg N2O-N yr-1 to the atmosphere throughout the period. Thus, the region was a net source of CH4 and N2O, while the CO2 balance was near neutral within its large uncertainties. Undisturbed terrestrial ecosystems had a CO2 sink of -340 (-836, 156) Tg CO2-C yr-1. Vertical emissions from fire disturbances and inland waters largely offset the sink in vegetated ecosystems. When including lateral fluxes for a complete GHG budget, the permafrost region was a net source of C and N, releasing 144 (-506, 826) Tg C yr-1 and 3 (2, 5) Tg N yr-1. Large uncertainty ranges in these estimates point to a need for further expansion of monitoring networks, continued data synthesis efforts, and better integration of field observations, remote sensing data, and ecosystem models to constrain the contemporary net GHG budgets of the permafrost region and track their future trajectory. [ABSTRACT FROM AUTHOR]

Arctic terrestrial greenhouse gas (GHG) fluxes of carbon dioxide (CO 2), methane (CH 4), and nitrous oxide (N 2 O) play an important role in the global GHG budget. However, these GHG fluxes are rarely studied simultaneously, and our understanding of the conditions controlling them across spatial gradients is limited. Here, we explore the magnitudes and drivers of GHG fluxes across fine-scale terrestrial gradients during the peak growing season (July) in sub-Arctic Finland. We measured chamber-derived GHG fluxes and soil temperature, soil moisture, soil organic carbon and nitrogen stocks, soil pH, soil carbon-to-nitrogen (C/N) ratio, soil dissolved organic carbon content, vascular plant biomass, and vegetation type from 101 plots scattered across a heterogeneous tundra landscape (5 km 2). We used these field data together with high-resolution remote sensing data to develop machine learning models for predicting (i.e., upscaling) daytime GHG fluxes across the landscape at 2 m resolution. Our results show that this region was on average a daytime net GHG sink during the growing season. Although our results suggest that this sink was driven by CO 2 uptake, it also revealed small but widespread CH 4 uptake in upland vegetation types, almost surpassing the high wetland CH 4 emissions at the landscape scale. Average N 2 O fluxes were negligible. CO 2 fluxes were controlled primarily by annual average soil temperature and biomass (both increase net sink) and vegetation type, CH 4 fluxes by soil moisture (increases net emissions) and vegetation type, and N 2 O fluxes by soil C/N (lower C/N increases net source). These results demonstrate the potential of high spatial resolution modeling of GHG fluxes in the Arctic. They also reveal the dominant role of CO 2 fluxes across the tundra landscape but suggest that CH 4 uptake in dry upland soils might play a significant role in the regional GHG budget. [ABSTRACT FROM AUTHOR]

Arctic terrestrial greenhouse gas (GHG) fluxes of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) play an important role in the global GHG budget. However, these GHG fluxes are rarely studied simultaneously, and our understanding of the conditions controlling them across spatial gradients is limited. Here, we explore the magnitudes and drivers of GHG fluxes across fine-scale terrestrial gradients during the peak growing season (July) in sub-Arctic Finland. We measured chamber-derived GHG fluxes and soil temperature, soil moisture, soil organic carbon and nitrogen stocks, soil pH, soil carbon-to-nitrogen (C/N) ratio, soil dissolved organic carbon content, vascular plant biomass, and vegetation type from 101 plots scattered across a heterogeneous tundra landscape (5 km²). We used these field data together with high-resolution remote sensing data to develop machine learning models to predict (i.e., upscale) daytime GHG fluxes across the landscape at 2-m resolution. Our results show that this region was on average a daytime net GHG sink during the growing season. Although our results suggest that this sink was driven by CO2 uptake, it also revealed small but widespread CH4 uptake in upland vegetation types, shifting this region to an average net CH4 sink at the landscape scale during growing season, despite the presence of high-emitting wetlands. Average N2O fluxes were negligible. CO2 fluxes were controlled primarily by annual average soil temperature and biomass (both increase net sink) and vegetation type, CH4 fluxes by soil moisture (increases net emissions) and vegetation type, and N2O fluxes by soil C/N (lower C/N increases net source). These results demonstrate the potential of high spatial resolution modelling of GHG fluxes in the Arctic. They also reveal the dominant role of CO2 fluxes across the tundra landscape, but suggest that CH4 uptake might play a significant role in the regional GHG budget. [ABSTRACT FROM AUTHOR]

Due to climate change, increased microbial activity in high-latitude soils may lead to higher greenhouse gas (GHG) emissions. However, microbial GHG production and consumption mechanisms in tundra soils are not thoroughly understood. To investigate how the diversity and functional potential of bacterial and archaeal communities vary across vegetation types and soil layers, we analyzed 116 soil metatranscriptomes from 73 sites in the Finnish sub-Arctic. Meadow soils were characterized by higher pH and lower soil organic matter (SOM) and carbon/nitrogen ratio. By contrast, dwarf shrub-dominated ecosystems had higher SOM and lower pH. Although Actinobacteria, Acidobacteria, Alphaproteobacteria and Planctomycetes were dominant in all communities, there were significant differences at the genus level between vegetation types; plant polymer-degrading groups were more active in shrub-dominated soils than in meadows. Given that climate-change scenarios predict the expansion of shrubs at high latitudes, our results indicate that tundra soil microbial communities harbor potential decomposers of increased plant litter, which may affect the rate of carbon turnover in tundra soils. Additionally, transcripts of methanotrophs were detected in the mineral layer of all soils, which may moderate methane fluxes. This study provides new insights into possible shifts in tundra microbial diversity and activity due to climate change. [ABSTRACT FROM AUTHOR]