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Deciduous tree cover is expected to increase in North American boreal forests with climate warming and wildfire. This shift in composition has the potential to generate biophysical cooling via increased land surface albedo. Here we use Landsat-derived maps of continuous tree canopy cover and deciduous fractional composition to assess albedo change over recent decades. We find, on average, a small net decrease in deciduous fraction from 2000 to 2015 across boreal North America and from 1992 to 2015 across Canada, despite extensive fire disturbance that locally increased deciduous vegetation. We further find near-neutral net biophysical change in radiative forcing associated with albedo when aggregated across the domain. Thus, while there have been widespread changes in forest composition over the past several decades, the net changes in composition and associated post-fire radiative forcing have not induced systematic negative feedbacks to climate warming over the spatial and temporal scope of our study.

Climate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near‐term (5–30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi‐model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long‐term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance‐induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near‐future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi‐model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage.

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.

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.

The quantity and preservation of carbon‐rich organic matter (OM) underlying permafrost uplands, and the evolution of carbon accumulation with millennial climate change, are large sources of uncertainty in carbon cycle feedbacks on climate change. We investigated permafrost OM accumulation and degradation over the Holocene using a transect of sediment cores dating back to at least c. 6 ka, from a hillslope in the Eight Mile Lake watershed, central Alaska. We find decimeter‐scale organic‐rich (111 ± 45 kg C m⁻³) and organic‐poor (49 ± 30 kg C m⁻³) layers below an upper peat, which store 35% ± 11% and 41% ± 20% of the carbon in the upper 1 m, respectively. In organic‐poor layers, scattered¹⁴C ages of plant macrofossils and higher percentages of degradedAlnusandBetulapollen indicate reworking by cryoturbation and hillslope processes. Whereas organic carbon to nitrogen ratios generally indicate OM freshening up‐core, amino acid bacterial biomarkers, includingd‐enantiomers and gamma‐aminobutyric acid, suggest enhanced degradation prior to 5 ka. Carbon accumulation rates increased from ∼4 to 14 g C m⁻² year⁻¹from c. 8 to 0.2 ka, coinciding with decreasing temperatures and increasing moisture regionally, which may have promoted OM accumulation. Carbon stocks within the upper 1 m average 66 ± 13 kg C m⁻², varying from 77 kg C m⁻²in a buried depression on the upper slope to 48 kg C m⁻²downslope. We conclude that heterogeneity in preserved OM reflects a combination of hillslope geomorphic processes, cryoturbation, and climatic variations over the Holocene.

We present a timeseries of¹⁴CO₂for the period 1910–2021 recorded by annual plants collected in the southwestern United States, centered near Flagstaff, Arizona. This timeseries is dominated by five commonly occurring annual plant species in the region, which is considered broadly representative of the southern Colorado Plateau. Most samples (1910–2015) were previously archived herbarium specimens, with additional samples harvested from field experiments in 2015–2021. We used this novel timeseries to develop a smoothed local record with uncertainties for “bomb spike”¹⁴C dating of recent terrestrial organic matter. Our results highlight the potential importance of local records, as we document a delayed arrival of the 1963–1964 bomb spike peak, lower values in the 1980s, and elevated values in the last decade in comparison to the most current Northern Hemisphere Zone 2 record. It is impossible to retroactively collect atmospheric samples, but archived annual plants serve as faithful scribes: samples from herbaria around the Earth may be an under-utilized resource to improve understanding of the modern carbon cycle.

Retrogressive thaw slumps (RTS)—thermal erosion of soil and vegetation after ground ice thaw—are increasing. Recovery of plant biomass after RTS is important for maintaining Arctic carbon (C) stocks and is regulated by nutrient availability for new plant growth. Many RTS are characterized by verdant shrub growth mid-succession, atypical of the surrounding nutrient-limited tundra. Here, we investigated the potential for internal and external sources of nitrogen (N) and phosphorus (P) to support mid-successional shrub growth at three Alaskan RTS chronosequences. We assessed patterns of soil and microbial CNP, soil NP cycling rates and stocks, N inputs via biological N₂-fixation, and thaw leachate over time after disturbance. We found a clear transfer of P stocks from mineral to organic soils with increasing site age, yet insufficient N from any one source to support observed shrub growth. Instead, multiple mechanisms may have contributed to mid-successional shrub growth, including sustained N-cycling with reduced plant biomass, N leaching from undisturbed tundra, uninvestigated sources of N₂-fixation, and most promising given the large resource, deep mineral soil N stocks. These potential mechanisms of N supply are critical for the regulation of the Arctic C cycle in response to an increasingly common climate-driven disturbance.

Boreal forests harbor as much carbon (C) as the atmosphere and significant amounts of organic nitrogen (N), the nutrient most likely to limit plant productivity in high‐latitude ecosystems. In the boreal biome, the primary disturbance is wildfire, which consumes plant biomass and soil material, emits greenhouse gasses, and influences long‐term C and N cycling. Climate warming and drying is increasing wildfire severity and frequency and is combusting more soil organic matter (SOM). Combustion of surface SOM exposes deeper older layers of accumulated soil material that previously escaped combustion during past fires, here termed legacy SOM. Postfire SOM decomposition and nutrient availability are determined by these layers, but the drivers of legacy SOM decomposition are unknown. We collected soils from plots after the largest fire year on record in the Northwest Territories, Canada, in 2014. We used radiocarbon dating to measure Δ¹⁴C (soil age index), soil extractions to quantify N pools and microbial biomass, and a 90‐day laboratory incubation to measure the potential rate of element mineralization and understand patterns and drivers of legacy SOM C decomposition and N availability. We discovered that bulk soil C age predicted C decomposition, where cumulatively, older soil (approximately −450.0‰) produced 230% less C during the incubation than younger soil (~0.0‰). Soil age also predicted C turnover times, with old soil turnover 10 times slower than young soil. We found respired C was younger than bulk soil C, indicating most C enters and leaves relatively quickly, while the older portion remains a stable C sink. Soil age and other indices were unrelated to N availability, but microbial biomass influenced N availability, with more microbial biomass immobilizing soil N pools. Our results stress the importance of legacy SOM as a stable C sink and highlight that soil age drives the pace and magnitude of soil C contributions to the atmosphere between wildfires.

Wetlands are responsible for 20%–31% of global methane (CH₄) emissions and account for a large source of uncertainty in the global CH₄budget. Data‐driven upscaling of CH₄fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH₄emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH₄flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH₄fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH₄emissions of 146 ± 43 TgCH₄ y⁻¹for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH₄ y⁻¹) and overlaps with top‐down atmospheric inversion models (155–200 TgCH₄ y⁻¹). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH₄fluxes has the potential to produce realistic extra‐tropical wetland CH₄emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).

Permafrost thaw causes the seasonally thawed active layer to deepen, causing the Arctic to shift toward carbon release as soil organic matter becomes susceptible to decomposition. Ground subsidence initiated by ice loss can cause these soils to collapse abruptly, rapidly shifting soil moisture as microtopography changes and also accelerating carbon and nutrient mobilization. The uncertainty of soil moisture trajectories during thaw makes it difficult to predict the role of abrupt thaw in suppressing or exacerbating carbon losses. In this study, we investigated the role of shifting soil moisture conditions on carbon dioxide fluxes during a 13‐year permafrost warming experiment that exhibited abrupt thaw. Warming deepened the active layer differentially across treatments, leading to variable rates of subsidence and formation of thermokarst depressions. In turn, differential subsidence caused a gradient of moisture conditions, with some plots becoming consistently inundated with water within thermokarst depressions and others exhibiting generally dry, but more variable soil moisture conditions outside of thermokarst depressions. Experimentally induced permafrost thaw initially drove increasing rates of growing season gross primary productivity (GPP), ecosystem respiration (R_eco), and net ecosystem exchange (NEE) (higher carbon uptake), but the formation of thermokarst depressions began to reverse this trend with a high level of spatial heterogeneity. Plots that subsided at the slowest rate stayed relatively dry and supported higher CO₂fluxes throughout the 13‐year experiment, while plots that subsided very rapidly into the center of a thermokarst feature became consistently wet and experienced a rapid decline in growing season GPP,R_eco, and NEE (lower carbon uptake or carbon release). These findings indicate that Earth system models, which do not simulate subsidence and often predict drier active layer conditions, likely overestimate net growing season carbon uptake in abruptly thawing landscapes.