Arctic rivers are intricate water networks that chemically and biologically process carbon before releasing it as carbon dioxide (CO2) into the atmosphere or carrying it to the ocean. Primary producers use inorganic carbon to build biomass at the basis of the trophic chain. Little is known about how Arctic rivers adapt to climate warming, changes in hydrology and biogeochemical properties. To quantify net and gross biological productivity we measured the dissolved oxygen-to-argon (O2/Ar) ratios and O2 triple isotopologue composition in the river Kolyma and in its tributary Ambolikha during late freshet (June) and base-flow conditions (August) in 2019. We found that hydrological factors restricted river productivity. The river system released CO2 into the atmosphere in June and August, however August emissions were only 6 % of late freshet emissions. Also, the Ambolikha tributary emitted twice as much CO2 per area than the main Kolyma channel in June. Due to higher river flow and turbidity in June, river production was reduced, while lower flows in August permitted more light penetration and a phytoplankton bloom at the confluence of tributary and main Kolyma channel. Total CO2 emissions per area during June and August amounted to (5±11) % of the gross carbon uptake estimated at the bloom site. Thus, in-stream metabolism can exceed riverine CO2 emissions under specific flow and light conditions. Arctic climate change may promote biological productivity in particular locations and increase its contribution to carbon budgets in Arctic rivers as flow slows during longer open water periods.

A document by Mark Schlutow. Click on the document to view its contents.

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 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.

Biogeochemical cycling in permafrost-affected ecosystems remains associated with large uncertainties, which could impact the Earth’s greenhouse gas budget and future climate mitigation policies. In particular, increased nutrient availability following permafrost thaw could perturb biogeochemical cycling in permafrost systems, an effect largely unexplored in global assessments. In this study, we enhance the terrestrial ecosystem model QUINCY, which fully couples carbon (C), nitrogen (N) and phosphorus (P) cycles in vegetation and soil, with processes relevant in high latitudes (e.g., soil freezing and snow dynamics). We use this enhanced model to investigate impacts of increased carbon and nutrient availability from permafrost thawing in comparison to other climate-induced effects and CO2 fertilization over 1960 to 2019 over a multitude of tundra sites. Our simulation results suggest that vegetation growth in high latitudes is acutely N-limited at our case study sites. Despite this, enhanced availability of nutrients in the deep active layer following permafrost thaw, simulated to be around 0.1 m on average since the 1960s, accounts for only 11 % of the total GPP increase averaged over all sites. Our analysis suggests that the decoupling of the timing of peak vegetative growth (week 27-29 of the year, corresponding to mid-to-late July) and maximum thaw depth (week 34-37, corresponding to mid-to-late August), lead to an incomplete plant use of newly available nutrients at the permafrost front. Due to resulting increased availability of N at the permafrost table, as well as alternating water saturation levels, increases in both nitrification and denitrification enhance N2O emissions in the simulations. Our model thus suggests a weak (5 mg N m-2 yr-1) but increasing source of N2O, which reaches trends of up to +1 mg N m-2 yr-1 per decade, locally, which is potentially of large importance for the global N2O budget.