Rapid environmental change in the Arctic raises concerns about the future of wetland carbon dynamics. Current classifications may not capture the diverse environmental conditions affecting these fluxes. This study aims to fill this gap by identifying distinct wetland clusters using a synthesis of field measurements (CO2 and CH4 fluxes, soil moisture) and remote sensing and other geospatial data (snow season length, annual precipitation, growing degree days, normalized difference vegetation index, and potential peat depth). Our dataset includes 862 individual measurements of tundra and forest-tundra wetlands from 86 data sources covering the period 1988 - 2023 for a variety of Arctic regions, with dominant wetland types including fens and polygonal bogs. Clusters for CO2 and CH4 fluxes mostly did not overlap, indicating different controls on these two carbon fluxes. CH4 flux clusters depended on local moisture conditions, potential soil depth, and vegetation characteristics, while CO2 flux clusters varied significantly with climatic conditions and vegetation characteristics. Notably, no single wetland type from the current classification formed a distinct cluster in either carbon flux. This study highlights the importance of high spatial resolution soil and vegetation data for accurate scaling of wetland CH4 fluxes. However, this may be less critical for the upscaling of wetland CO2 fluxes.

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 0.36 (-620, 652) Tg CO2-C y-1, 38 (21, 53) Tg CH4-C y-1, and 0.62 (0.03, 1.2) Tg N2O-N y-1 to the atmosphere throughout the period. While the region was a net source of CH4 and N2O, the CO2 budget was near neutral with large uncertainties. Terrestrial ecosystems remained a CO2 sink, but emissions from fire disturbances and inland waters largely offset the sink in vegetated ecosystems. Including lateral fluxes, the permafrost region was a net source of C and N, releasing 136 (-517, 821) Tg C y-1 and 3.2 (1.9, 4.8) Tg N y-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.

The long-term net sink of carbon (C), nitrogen (N) and greenhouse gases (GHGs) in the northern permafrost region is projected to weaken or shift under climate change. But large uncertainties remain, even on present-day GHG budgets. We compare bottom-up (data-driven upscaling, process-based models) and top-down budgets (atmospheric inversion models) of the main GHGs (CO2, CH4, and N2O) and lateral fluxes of C and N across the region over 2000-2020. Bottom-up approaches estimate higher land to atmosphere fluxes for all GHGs compared to top-down atmospheric inversions. Both bottom-up and top-down approaches respectively show a net sink of CO2 in natural ecosystems (-31 (-667, 559) and -587 (-862, -312), respectively) but sources of CH4 (38 (23, 53) and 15 (11, 18) Tg CH4-C yr-1) and N2O (0.6 (0.03, 1.2) and 0.09 (-0.19, 0.37) Tg N2O-N yr-1) in natural ecosystems. Assuming equal weight to bottom-up and top-down budgets and including anthropogenic emissions, the combined GHG budget is a source of 147 (-492, 759) Tg CO2-Ceq yr-1 (GWP100). A net CO2 sink in boreal forests and wetlands is offset by CO2 emissions from inland waters and CH4 emissions from wetlands and inland waters, with a smaller additional warming from N2O emissions. Priorities for future research include representation of inland waters in process-based models and compilation of process-model ensembles for CH4 and N2O. Discrepancies between bottom-up and top-down methods call for analyses of how prior flux ensembles impact inversion budgets, more in-situ flux observations and improved resolution in upscaling.

The Arctic is warming four times faster than the global average, resulting in widespread ground thaw and state changes. Due to the rapid rate and large scale of ecosystem shifts, identifying and understanding Arctic boreal zone changes and feedbacks requires frequent observations across multiple scales. The last decade has witnessed significant increases in the number and coverage of in-situ, airborne, and satellite observations. However, additional resolution, coverage, and sustained, long-term time series data records are urgently required to characterize and understand the considerable heterogeneity of Northern permafrost environments. Here, we review the physical and technical gaps that limit the ability to detect rapid state changes and tipping points in permafrost ecosystems. Understanding and accurately forecasting changes to the Arctic is an essential component of managing climate change in this rapidly transforming system.

Ecosystems at high latitudes are under increasing stress from climate change. To understand changes in carbon fluxes, 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. Using a case study in the Canadian Arctic we show several optimization scenarios and compare these to a random site selection among reasonable choices. This method greatly outperforms an unguided network extension and can compensate for suboptimal human choices. Overall, it is important to keep sites active and where possible make the extra investment to survey new strategic locations.