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To understand controls on soil organic matter chemical composition across North America, we collected 13C NMR spectra and conducted and synthesized additional biogeochemical measurements from NEON Megapit soil samples as well as additional samples (total n = 42). This dataset supports the findings described in the associated manuscript by Hall, Ye et al. (2020). 

Abstract Previous studies found conflicting results on the importance of temperature and precipitation versus geochemical variables for predicting soil organic carbon (SOC) concentrations and trends with depth, and most utilized linear statistical models. To reconcile the controversy, we used data from 2574 mineral horizons from 675 pits from National Ecological Observatory Network sites across North America, typically collected to 1 m depth. Climate was a fundamental predictor of SOC and played similarly important roles as some geochemical predictors. Yet, this only emerged in the generalized additive mixed model and random forest model and was obscured in the linear mixed model. Relationships between water availability and SOC were strongest in very dry ecosystems and SOC increased most strongly at mean annual temperature < 0°C. In all models, depth, oxalate‐extractable Al (Al_ox), pH, and exchangeable calcium plus exchangeable magnesium were important while silt + clay, oxalate‐extractable Fe (Fe_ox), and vegetation type were weaker predictors. Climate and pH were independently related to SOC and also interacted with geochemical composition: Fe_oxand Al_oxrelated more strongly to SOC in wet or cold climates. Most predictors had nonlinear threshold relationships with SOC, and a saturating response to increasing reactive metals indicates soils where SOC might be limited by C inputs. We observed a mostly constant relative importance of geochemical and climate predictors of SOC with increasing depth, challenging previous statements. Overall, our findings challenge the notion that climate is redundant after accounting for geochemistry and demonstrate that considering their nonlinearities and interactions improves spatial predictions of SOC. 

Abstract. Advancing our understanding of Earth system dynamics (ESD) depends on thedevelopment of models and other analytical tools that apply physical,biological, and chemical data. This ambition to increase understanding anddevelop models of ESD based on site observations was the stimulus forcreating the networks of Long-Term Ecological Research (LTER), Critical ZoneObservatories (CZOs), and others. We organized a survey, the results of whichidentified pressing gaps in data availability from these networks, inparticular for the future development and evaluation of models that representESD processes, and provide insights for improvement in both data collectionand model integration. From this survey overview of data applications in the context of LTER andCZO research, we identified three challenges: (1) widen application ofterrestrial observation network data in Earth system modelling,(2) develop integrated Earth system models that incorporate processrepresentation and data of multiple disciplines, and (3) identifycomplementarity in measured variables and spatial extent, and promotingsynergies in the existing observational networks. These challenges lead toperspectives and recommendations for an improved dialogue between theobservation networks and the ESD modelling community, including co-locationof sites in the existing networks and further formalizing theserecommendations among these communities. Developing these synergies willenable cross-site and cross-network comparison and synthesis studies, whichwill help produce insights around organizing principles, classifications,and general rules of coupling processes with environmental conditions.