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Abstract Unabated 21st-century climate change will accelerate Arctic-Subarctic permafrost thaw which can intensify microbial degradation of carbon-rich soils, methane emissions, and global warming. The impact of permafrost thaw on future Arctic-Subarctic wildfires and the associated release of greenhouse gases and aerosols is less well understood. Here we present a comprehensive analysis of the effect of future permafrost thaw on land surface processes in the Arctic-Subarctic region using the CESM2 large ensemble forced by the SSP3-7.0 greenhouse gas emission scenario. Analyzing 50 greenhouse warming simulations, which capture the coupling between permafrost, hydrology, and atmosphere, we find that projected rapid permafrost thaw leads to massive soil drying, surface warming, and reduction of relative humidity over the Arctic-Subarctic region. These combined processes lead to nonlinear late-21st-century regime shifts in the coupled soil-hydrology system and rapid intensification of wildfires in western Siberia and Canada. 

Abstract Soil organic matter decomposition and its interactions with climate depend on whether the organic matter is associated with soil minerals. However, data limitations have hindered global-scale analyses of mineral-associated and particulate soil organic carbon pools and their benchmarking in Earth system models used to estimate carbon cycle–climate feedbacks. Here we analyse observationally derived global estimates of soil carbon pools to quantify their relative proportions and compute their climatological temperature sensitivities as the decline in carbon with increasing temperature. We find that the climatological temperature sensitivity of particulate carbon is on average 28% higher than that of mineral-associated carbon, and up to 53% higher in cool climates. Moreover, the distribution of carbon between these underlying soil carbon pools drives the emergent climatological temperature sensitivity of bulk soil carbon stocks. However, global models vary widely in their predictions of soil carbon pool distributions. We show that the global proportion of model pools that are conceptually similar to mineral-protected carbon ranges from 16 to 85% across Earth system models from the Coupled Model Intercomparison Project Phase 6 and offline land models, with implications for bulk soil carbon ages and ecosystem responsiveness. To improve projections of carbon cycle–climate feedbacks, it is imperative to assess underlying soil carbon pools to accurately predict the distribution and vulnerability of soil carbon. 

Abstract Nutrient limitation is widespread in terrestrial ecosystems. Accordingly, representations of nitrogen (N) limitation in land models typically dampen rates of terrestrial carbon (C) accrual, compared with C‐only simulations. These previous findings, however, rely on soil biogeochemical models that implicitly represent microbial activity and physiology. Here we present results from a biogeochemical model testbed that allows us to investigate how an explicit versus implicit representation of soil microbial activity, as represented in the MIcrobial‐MIneral Carbon Stabilization (MIMICS) and Carnegie‐Ames‐Stanford Approach (CASA) soil biogeochemical models, respectively, influence plant productivity, and terrestrial C and N fluxes at initialization and over the historical period. When forced with common boundary conditions, larger soil C pools simulated by the MIMICS model reflect longer inferred soil organic matter (SOM) turnover times than those simulated by CASA. At steady state, terrestrial ecosystems experience greater N limitation when using the MIMICS‐CN model, which also increases the inferred SOM turnover time. Over the historical period, however, warming‐induced acceleration of SOM decomposition over high latitude ecosystems increases rates of N mineralization in MIMICS‐CN. This reduces N limitation and results in faster rates of vegetation C accrual. Moreover, as SOM stoichiometry is an emergent property of MIMICS‐CN, we highlight opportunities to deepen understanding of sources of persistent SOM and explore its potential sensitivity to environmental change. Our findings underscore the need to improve understanding and representation of plant and microbial resource allocation and competition in land models that represent coupled biogeochemical cycles under global change scenarios. 

Abstract Increased plant growth under elevated carbon dioxide (CO₂) slows the pace of climate warming and underlies projections of terrestrial carbon (C) and climate dynamics. However, this important ecosystem service may be diminished by concurrent changes to vegetation carbon‐to‐nitrogen (C:N) ratios. Despite clear observational evidence of increasing foliar C:N under elevated CO₂, our understanding of potential ecological consequences of foliar stoichiometric flexibility is incomplete. Here, we illustrate that when we incorporated CO₂‐driven increases in foliar stoichiometry into the Community Land Model the projected land C sink decreased two‐fold by the end of the century compared to simulations with fixed foliar chemistry. Further, CO₂‐driven increases in foliar C:N profoundly altered Earth's hydrologic cycle, reducing evapotranspiration and increasing runoff, and reduced belowground N cycling rates. These findings underscore the urgency of further research to examine both the direct and indirect effects of changing foliar stoichiometry on soil N cycling and plant productivity. 

Abstract. Changes in the nitrogen (N) status of forest ecosystems can directly and indirectly influence their carbon (C) sequestration potential by altering soil organic matter (SOM) decomposition, soil enzyme activity, and plant–soil interactions. However, model representations of linked C–N cycles and SOM decay are not well validated against experimental data. Here, we use extensive data from the Fernow Experimental Forest long-term whole-watershed N fertilization study to compare the response to N perturbations of two soil models that represent decomposition dynamics differently (first-order decay versus microbially explicit reverse Michaelis–Menten kinetics). These two soil models were coupled to a common vegetation model which provided identical input data. Key responses to N additions measured at the study site included a shift in plant allocation to favor woody biomass over belowground carbon inputs, reductions in soil respiration, accumulation of particulate organic matter (POM), and an increase in soil C:N ratios. The vegetation model did not capture the often-observed shift in plant C allocation with N additions, which resulted in poor predictions of the soil responses. We modified the parameterization of the plant C allocation scheme to favor wood production over fine-root production with N additions, which significantly improved the vegetation and soil respiration responses. Additionally, to elicit an increase in the soil C stocks and C:N ratios with N additions, as observed, we modified the decay rates of the POM in the soil models. With these modifications, both models captured negative soil respiration and positive soil C stock responses in line with observations, but only the microbially explicit model captured an increase in soil C:N. Our results highlight the need for further model development to accurately represent plant–soil interactions, such as rhizosphere priming, and their responses to environmental change. 

Mineral stabilization of soil organic matter is an important regulator of the global carbon (C) cycle. However, the vulnerability of mineral-stabilized organic matter (OM) to climate change is currently unknown. We examined soil profiles from 34 sites across the conterminous USA to investigate how the abundance and persistence of mineral-associated organic C varied with climate at the continental scale. Using a novel combination of radiocarbon and molecular composition measurements, we show that the relationship between the abundance and persistence of mineral-associated organic matter (MAOM) appears to be driven by moisture availability. In wetter climates where precipitation exceeds evapotranspiration, excess moisture leads to deeper and more prolonged periods of wetness, creating conditions which favor greater root abundance and also allow for greater diffusion and interaction of inputs with MAOM. In these humid soils, mineral-associated soil organic C concentration and persistence are strongly linked, whereas this relationship is absent in drier climates. In arid soils, root abundance is lower, and interaction of inputs with mineral surfaces is limited by shallower and briefer periods of moisture, resulting in a disconnect between concentration and persistence. Data suggest a tipping point in the cycling of mineral-associated C at a climate threshold where precipitation equals evaporation. As climate patterns shift, our findings emphasize that divergence in the mechanisms of OM persistence associated with historical climate legacies need to be considered in process-based models. 

Abstract. Global change research demands a convergence among academic disciplines to understand complex changes in Earth system function. Limitations related to data usability and computing infrastructure, however, present barriers to effective use of the research tools needed for this cross-disciplinary collaboration. To address these barriers, we created a computational platform that pairs meteorological data and site-level ecosystem characterizations from the National Ecological Observatory Network (NEON) with the Community Terrestrial System Model (CTSM) that is developed with university partners at the National Center for Atmospheric Research (NCAR). This NCAR–NEON system features a simplified user interface that facilitates access to and use of NEON observations and NCAR models. We present preliminary results that compare observed NEON fluxes with CTSM simulations and describe how the collaboration between NCAR and NEON that can be used by the global change research community improves both the data and model. Beyond datasets and computing, the NCAR–NEON system includes tutorials and visualization tools that facilitate interaction with observational and model datasets and further enable opportunities for teaching and research. By expanding access to data, models, and computing, cyberinfrastructure tools like the NCAR–NEON system will accelerate integration across ecology and climate science disciplines to advance understanding in Earth system science and global change. 

Abstract From hillslope to small catchment scales (< 50 km 2 ), soil carbon management and mitigation policies rely on estimates and projections of soil organic carbon (SOC) stocks. Here we apply a process-based modeling approach that parameterizes the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with SOC measurements and remotely sensed environmental data from the Reynolds Creek Experimental Watershed in SW Idaho, USA. Calibrating model parameters reduced error between simulated and observed SOC stocks by 25%, relative to the initial parameter estimates and better captured local gradients in climate and productivity. The calibrated parameter ensemble was used to produce spatially continuous, high-resolution (10 m 2 ) estimates of stocks and associated uncertainties of litter, microbial biomass, particulate, and protected SOC pools across the complex landscape. Subsequent projections of SOC response to idealized environmental disturbances illustrate the spatial complexity of potential SOC vulnerabilities across the watershed. Parametric uncertainty generated physicochemically protected soil C stocks that varied by a mean factor of 4.4 × across individual locations in the watershed and a − 14.9 to + 20.4% range in potential SOC stock response to idealized disturbances, illustrating the need for additional measurements of soil carbon fractions and their turnover time to improve confidence in the MIMICS simulations of SOC dynamics.