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Future climate change will dramatically change the carbon balancein the soil, and this change will affect the terrestrial carbon stock and theclimate itself. Earth system models (ESMs) are used to understand the currentclimate and to project future climate conditions, but the soil organic carbon(SOC) stock simulated by ESMs and those of observational databases are notwell correlated when the two are compared at fine grid scales. However, thespecific key processes and factors, as well as the relationships among thesefactors that govern the SOC stock, remain unclear; the inclusion of suchmissing information would improve the agreement between modeled andobservational data. In this study, we sought to identify the influentialfactors that govern global SOC distribution in observational databases, aswell as those simulated by ESMs. We used a data-mining (machine-learning)(boosted regression trees – BRT) scheme to identify the factors affecting the SOC stock. Weapplied BRT scheme to three observational databases and 15 ESM outputs from thefifth phase of the Coupled Model Intercomparison Project (CMIP5) and examinedthe effects of 13 variables/factors categorized into five groups (climate,soil property, topography, vegetation, and land-use history). Globally, thecontributions of mean annual temperature, clay content, carbon-to-nitrogen(CN) ratio, wetland ratio, and land cover were high in observationaldatabases, whereas the contributions of the mean annual temperature, landcover, and net primary productivity (NPP) were predominant in the SOCdistribution in ESMs. A comparison of the influential factors at a globalscale revealed that the most distinct differences between the SOCs from theobservational databases and ESMs were the low clay content and CN ratiocontributions, and the high NPP contribution in the ESMs. The results of thisstudy will aid in identifying the causes of the current mismatches betweenobservational SOC databases and ESM outputs and improve the modeling ofterrestrial carbon dynamics in ESMs. This study also reveals how adata-mining algorithm can be used to assess model outputs

Data-mining analysis of the global distribution of soil carbon in observational databases and Earth system models