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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.

 

Key Points A new semi‐analytical spin‐up (SASU) framework combines the default accelerated spin‐up method and matrix analytical algorithm SASU accelerates CLIM5 spin‐up by tens of times, becoming the fastest method to our knowledge SASU is applicable to most biogeochemical models and enables computationally costly study, for example, sensitivity analysis 

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 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. 

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. 

The integration of physical and social science data can enable novel frameworks, methodologies, and innovative solutions important for addressing complex socio-environmental problems. Unfortunately, many technical, procedural, and institutional challenges hamper effective data integration—detracting from interdisciplinary socio-environmental research and broader public impact. This paper reports on the experiences and challenges of social and physical data integration, as experienced by diverse Early Career Researchers (ECRs), and offers strategies for coping with and addressing these challenges. Through a workshop convened by the National Center for Atmospheric Research (NCAR) Innovator Program, 33 participants from different disciplines, career stages, and institutions across the United States identified four thematic data integration challenges related to complexity and uncertainty, communication, scale, and institutional barriers. They further recommended individual, departmental, and institutional scale responses to cope with and address these integration challenges. These recommendations seek to inform faculty and department support for ECRs, who are often encouraged—and even expected—to engage in integrative, problem-focused, and solutions-oriented research.

 

Climate change projections consistently demonstrate that warming temperatures and dwindling seasonal snowpack will elicit cascading effects on ecosystem function and water resource availability. Despite this consensus, little is known about potential changes in the variability of ecohydrological conditions, which is also required to inform climate change adaptation and mitigation strategies. Considering potential changes in ecohydrological variability is critical to evaluating the emergence of trends, assessing the likelihood of extreme events such as floods and droughts, and identifying when tipping points may be reached that fundamentally alter ecohydrological function. Using a single-model Large Ensemble with sophisticated terrestrial ecosystem representation, we characterize projected changes in the mean state and variability of ecohydrological processes in historically snow-dominated regions of the Northern Hemisphere. Widespread snowpack reductions, earlier snowmelt timing, longer growing seasons, drier soils, and increased fire risk are projected for this century under a high-emissions scenario. In addition to these changes in the mean state, increased variability in winter snowmelt will increase growing-season water deficits and increase the stochasticity of runoff. Thus, with warming, declining snowpack loses its dependable buffering capacity so that runoff quantity and timing more closely reflect the episodic characteristics of precipitation. This results in a declining predictability of annual runoff from maximum snow water equivalent, which has critical implications for ecosystem stress and water resource management. Our results suggest that there is a strong likelihood of pervasive alterations to ecohydrological function that may be expected with climate change. 

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.

 

Microbial biomass is known to decrease with soil drying and to increase after rewetting due to physiological assimilation and substrate limitation under fluctuating moisture conditions, but how the effects of moisture changes vary between dry and wet environments is unclear. Here, we conducted a meta‐analysis to assess the effects of elevated and reduced soil moisture on microbial biomass carbon (MBC) and nitrogen (MBN) across a broad range of forest sites between dry and wet regions. We found that the influence of both elevated and reduced soil moisture on MBC and MBN concentrations in forest soils was greater in dry than in wet regions. The influence of altered soil moisture on MBC and MBN concentrations increased significantly with the manipulation intensity but decreased with the length of experimental period, with a dramatic increase observed under a very short‐term precipitation pulse. Moisture effect did not differ between coarse‐ and fine‐textured soils. Precipitation intensity, experimental duration, and site standardized precipitation index (dry or wet climate) were more important than edaphic factors (i.e., initial water content, bulk density, clay content) in determining microbial biomass in response to altered moisture in forest soils. Different responses of microbial biomass in forest soils between dry and wet regions should be incorporated into models to evaluate how changes in the amount, timing and intensity of precipitation affect soil biogeochemical processes.