Heidi Rodenhizer

Carbon emissions from disturbances in the Arctic-Boreal Zone (ABZ) are not adequately included in global carbon budgets due to insufficient measurements and model representation. Due to this data gap, we conducted an expert assessment to examine the potential trajectories of multiple disturbance types in the ABZ and their impacts on net carbon fluxes over the next one to three centuries. Across five disturbance categories – anthropogenic (e.g., oil and gas infrastructure), biotic, fire, abrupt permafrost thaw, and weather – experts agreed that the extent, frequency, range, and severity of disturbances will increase for at least the next century. Taken together, our assessment suggests that ABZ disturbances could result in 154–417 TgC yr ^− 1 (median expert estimates) greater net carbon emissions by 2100 relative to 2000–2020 for three climate change mitigation scenarios – global mean temperature increase held below (1) 1.5°C by 2100, (2) 2°C by 2100, and (3) Representative Concentration Pathway (RCP) 8.5 (~ 2.6–4.8°C by 2100 with rising temperatures beyond that). Critically, the experts agreed these impacts will persist long after global net zero anthropogenic emissions are reached, causing continued carbon emissions that are currently unaccounted for. Key research priorities for reducing uncertainty in these estimates were 1) improved spatio-temporal monitoring of ABZ disturbances to quantify their extent and distribution, 2) increased measurements of ecosystem carbon fluxes during and following disturbance events, and 3) incorporation of disturbance processes into models.

During land model development, simulated carbon dynamics are often benchmarked against observational data sets to evaluate model performance. Functional relationship benchmarks are the relationship between a driving variable (e.g., temperature) and a response variable (e.g., ecosystem respiration) and are a promising tool for assessing model performance by evaluating modeled sensitivities to changing environmental conditions. However, observed functional relationships can be influenced by choices made during data collection and throughout the benchmarking process, impacting the inferred skill of land models. To avoid misrepresenting a model's true performance, it is necessary to systematically evaluate best practices when constructing functional relationship benchmarks. We developed a set of guidelines for constructing functional relationship benchmarks, considering the choice of data set, number of daily observations, temporal extent, and temporal resolution across Alaska and Canada over a 20‐year period from 2001 to 2020. The temperature sensitivity of ecosystem respiration from observations, evaluated through an apparent Q ₁₀ , is highly variable both spatially and as a result of the data processing approach applied in the benchmark formation. When benchmarking 13 models from the Warming Permafrost Model Intercomparison Project (WrPMIP), the range in inferred model skill is substantially impacted by the choices applied in constructing functional relationship benchmarks. The inferred performance of a given model is most sensitive to the number of daily observations and temporal extent, followed by choice of benchmark data set and temporal averaging. Results from this analysis can guide the development of consistent and robust functional relationships for future model evaluation studies.

Accurate and up-to-date mapping of rapidly evolving landscapes is crucial for environmental monitoring, climate modeling, and robust AI-driven Earth observation. Traditional digitization struggles to keep pace with abrupt changes, especially in permafrost regions where retrogressive thaw slumps (RTS)—landslide-like features from ice-rich permafrost thaw—rapidly alter terrain. Their dynamic nature, irregular shapes, and remote locations challenge accurate delineation, often leading to outdated or imprecise labels. To repair outdated or imprecise geospatial labels for fast-changing features, we present a novel framework that leverages zero-shot AI segmentation models to automatically improve outdated labels to match the latest satellite imagery. We employ a multi-stage process where initial RTS boundary proposals are used alongside current imagery to generate model-ready prompts for AI segmentation. Our approach then utilizes a prompt-based segmentation model, the Segment Anything Model, to derive refined, up-to-date RTS boundary masks. To ensure high quality, these new delineations are validated against expert-annotated ground truth. Furthermore, we’ve developed a visualization interface for comprehensive expert review and quality control, offering both analytical summaries and interactive visual inspection of the generated outputs, helping to confirm the most accurate new polygons. This framework significantly improves upon existing RTS digitization by producing comparable delineations to those created manually while significantly reducing the label generation time. Its modular and reproducible design also supports scalable monitoring of diverse and remote permafrost landscapes. The inherent zero-shot capability of our method further extends its applicability beyond RTS, offering a versatile solution for enhancing object label quality across various dynamic environmental phenomena captured in satellite imagery.

Massive stores of ancient soil organic carbon (SOC) in permafrost can decompose with Arctic warming and accelerate global climate change. Declining SOC stocks are central to the permafrost carbon feedback, but direct measures of SOC loss are extremely rare due to methodological challenges related to subsidence in the Arctic. To fully capture changing SOC dynamics during thaw, we directly measured SOC stock and bulk soil radiocarbon ( ¹⁴ C) changes, while accounting for subsidence, during 13 years of permafrost thaw in a warming experiment in Interior Alaska. We found significant declines in SOC stocks: 14% (± 6%) in ambient plots that experienced regional warming and 23% (± 5%) in snow fence warmed plots, entirely in deep, mineral soil layers. Losses were largely driven by winter soil warming but were mediated by changing soil moisture and vegetation conditions. Plots with low shrub biomass had greater SOC losses, suggesting that vegetation community composition may play an important role in SOC storage. Surface soil ¹⁴ C measurements suggest that carbon inputs were three times greater in warming plots compared to ambient plots, but that decomposition increased proportionally leading to no detectable change in surface organic layers. We observed significant SOC losses of 5.2–8.1 kg C m ⁻² from deeper soil layers where carbon was sequestered ~2400 to ~4500 years ago. Our findings indicate that warmer soils in the winter will accelerate SOC losses, but that increasing density of shrub species through shrub expansion could help to mitigate SOC losses in deep soils. The significant loss of SOC from deep, mineral soils observed over just 13 years of ambient and experimental permafrost thaw highlights the vulnerability of this old C pool as it enters the active global carbon cycle.

Arctic permafrost is undergoing rapid changes due to climate warming in high latitudes. Retrogressive thaw slumps (RTS) are one of the most abrupt and impactful thermal-denudation events that change Arctic landscapes and accelerate carbon feedbacks. Their spatial distribution remains poorly characterised due to time-intensive conventional mapping methods. While numerous RTS studies have published standalone digitisation datasets, the lack of a centralised, unified database has limited their utilisation, affecting the scale of RTS studies and the generalisation ability of deep learning models. To address this, we established the Arctic Retrogressive Thaw Slumps (ARTS) dataset containing 23,529 RTS-present and 20,434 RTS-absent digitisations from 20 standalone datasets. We also proposed a Data Curation Framework as a working standard for RTS digitisations. This dataset is designed to be comprehensive, accessible, contributable, and adaptable for various RTS-related studies. This dataset and its accompanying curation framework establish a foundation for enhanced collaboration in RTS research, facilitating standardised data sharing and comprehensive analyses across the Arctic permafrost research community.

Increasing wildfire occurrence and intensity have immediate effects on northern ecosystems due to combustion of aboveground vegetation and belowground soil organic matter. These immediate impacts have indirect and longer term effects, including deepening of the active layer, changes in soil decomposition rates, and shifts in plant community composition. Despite the increasing fire impacts across the tundra region, the implications of wildfire on ecosystem carbon balance are not well understood. Using paired eddy covariance towers in unburned and burned tundra, we examined the effects of a 2015 wildfire on carbon dioxide and methane fluxes in a wetland tundra ecosystem in the Yukon–Kuskokwim Delta, Alaska, from 2020 to 2022. Wildfire increased the amplitude and variability of carbon uptake and release on seasonal and annual timescales and increased the temperature sensitivity of soil respiration. Seven years post fire, there was annual net uptake in both unburned and burned tundra based on net ecosystem exchange, with the sink strength of burned tundra exceeding that of the unburned tundra by 1.18–1.64 times. However, when considering emissions, it would take approximately 86 years to recover the carbon lost from the wildfire itself. Soil moisture was a dominant driver of fluxes and positively associated with higher rates of carbon dioxide uptake and release and methane release. This study underscores the importance of understanding the effects of wildfire-induced shifts on tundra carbon cycling, allowing better predictions of long-term landscape-scale climate feedbacks as the climate continues to warm.

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.

Retrogressive thaw slumps (RTS) are a form of abrupt permafrost thaw that can rapidly mobilize ancient frozen soil carbon, magnifying the permafrost carbon feedback. However, the magnitude of this effect is uncertain, largely due to limited information about the distribution and extent of RTS across the circumpolar region. Although deep learning methods such as Convolutional Neural Networks (CNN) have shown the ability to map RTS from high-resolution satellite imagery (≤10 m), challenges remain in deploying these models across large areas. Imagery selection and procurement remain one of the largest challenges to upscaling RTS mapping projects, as the user must balance cost with resolution and sensor quality. In this study, we compared the performance of three satellite imagery sources that differed in terms of sensor quality and cost in predicting RTS using a Unet3+ CNN model and identified RTS characteristics that impact detectability. Maxar WorldView imagery was the most expensive option, with a ground sample distance of 1.85 m in the multispectral bands (downloaded at 4 m resolution). Planet Labs PlanetScope imagery was a less expensive option with a ground sample distance of approximately 3.0–4.2 m (downloaded at 3 m resolution). Although PlanetScope imagery was downloaded at a higher resolution than WorldView, the radiometric footprint is around 10–12 m, resulting in less crisp imagery. Finally, Sentinel-2 imagery is freely available and has a 10 m resolution. We used 756 RTS polygons from seven sites across Arctic Canada and Siberia in model training and 63 RTS polygons in model testing. The mean IoU of the validation and testing data sets were 0.69 and 0.75 for the WorldView model, 0.70 and 0.71 for the PlanetScope model, and 0.66 and 0.68 for the Sentinel-2 model, respectively. The IoU of the RTS class was nonlinearly related to the RTS Area, showing a strong positive correlation that attenuated as the RTS Area increased. The models were better able to predict RTS that appeared bright on a dark background and were less able to predict RTS that had higher plant cover, indicating that bare ground was a primary way the models detected RTS. Additionally, the models performed less well in wet areas or areas with patchy ground cover. These results indicate that all imagery sources tested here were able to predict larger RTS, but higher-quality imagery allows more accurate detection of smaller RTS.

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 CO₂ fluxes 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.