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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.

 

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. 

Protecting existing soil carbon (C) and harnessing the C sequestration potential of soils require an improved understanding of the processes through which soil organic matter accumulates in natural systems. Currently, competing hypotheses exist regarding the dominant mechanisms for soil C stabilization. Many long-standing hypotheses revolve around an assumed positive relationship between the quantity of organic inputs and soil C accumulation, while more recent hypotheses have shifted attention toward the complex controls of microbial processing and organo-mineral complexation. Here, we present the observed findings of soil response to 20 years of detrital manipulations in the wet, temperate forest of the H.J. Andrews Experimental Station. Annual additions of low-quality (high C:N content) wood litter to the soil surface led to a greater positive effect on observed mean soil C concentration relative to additions of higher-quality (low C:N content) needle litter over the 20-year study period. However, high variability in measurements of soil C led to a statistically non-significant difference in C concentration between the two treatments and the control soil. The observed soil C responses to these two addition treatments demonstrates the long timescale and potential magnitude of soil C responses to management or disturbance led changes in forest litter input composition. Detrital input reduction treatments, including cutting off live root activity and the aboveground removal of surface litter, led to relatively small, non-significant effects on soil C concentrations over the 20-year study period. Far greater negative effects on mean soil C concentrations were observed for the combined removal of both aboveground litter and belowground root activity, which led to an observed, yet also non-significant, 20% decline in soil C stocks. The substantial proportion of remaining soil C following these dramatic, long-term reductions in above- and belowground detrital inputs suggests that losses of C in these forest soils are not readily achieved over a few decades of reductions in detrital input and may require far greater periods of time or further perturbations to the environment. Further, the observed soil C responses to detrital manipulations support recent hypotheses regarding soil C stabilization, which emphasize litter quality and mineral stabilization as relevant controls over forest soil C. 

The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationshipsa priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions.

 

Abstract. Data collected from research networks presentopportunities to test theories and develop models about factors responsiblefor the long-term persistence and vulnerability of soil organic matter(SOM). Synthesizing datasets collected by different research networkspresents opportunities to expand the ecological gradients and scientificbreadth of information available for inquiry. Synthesizing these data ischallenging, especially considering the legacy of soil data that havealready been collected and an expansion of new network science initiatives.To facilitate this effort, here we present the SOils DAta Harmonizationdatabase (SoDaH; https://lter.github.io/som-website, last access: 22 December 2020), a flexible database designed to harmonize diverse SOM datasets frommultiple research networks. SoDaH is built on several network scienceefforts in the United States, but the tools built for SoDaH aim to providean open-access resource to facilitate synthesis of soil carbon data.Moreover, SoDaH allows for individual locations to contribute results fromexperimental manipulations, repeated measurements from long-term studies,and local- to regional-scale gradients across ecosystems or landscapes.Finally, we also provide data visualization and analysis tools that can beused to query and analyze the aggregated database. The SoDaH v1.0 dataset isarchived and availableat https://doi.org/10.6073/pasta/9733f6b6d2ffd12bf126dc36a763e0b4 (Wieder et al., 2020). 

This SOils DAta Harmonization (SoDaH) database is designed to bring together soil carbon data from diverse research networks into a harmonized dataset that can be used for synthesis activities and model development. The research network sources for SoDaH span different biomes and climates, encompass multiple ecosystem types, and have collected data across a range of spatial, temporal, and depth gradients. The rich data sets assembled in SoDaH consist of observations from monitoring efforts and long-term ecological experiments. The SoDaH database also incorporates related environmental covariate data pertaining to climate, vegetation, soil chemistry, and soil physical properties. The data are harmonized and aggregated using open-source code that enables a scripted, repeatable approach for soil data synthesis.