Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover times

Abstract. The turnover time (τ) of global soil organic carbon is central to the functioning of terrestrial ecosystems. Yet our spatially explicit understanding of the depth-dependent variations and environmental controls of τ at a global scale remains incomplete. In this study, we combine multiple state-of-the-art observation-based datasets, including over 90 000 geo-referenced soil profiles, the latest root observations distributed globally, and large numbers of satellite-derived environmental variables, to generate global maps of apparent τ in topsoil (0–0.3 m) and subsoil (0.3–1 m) layers, with a spatial resolution of 30 arcsec (∼1 km at the Equator). We show that subsoil τ (385203485 years (mean, with a variation range from the 2.5th to 97.5th percentile)) is over 8 times longer than topsoil τ (1511137 years). The cross-validation shows that the fitted machine learning models effectively captured the variabilities in τ, with R2 values of 0.87 and 0.70 for topsoil and subsoil τ mapping, respectively. The prediction uncertainties of the τ maps were quantified for better user applications. The environmental controls on topsoil and subsoil τ were investigated at global, biome, and local scales. Our analyses illustrate the ways in which temperature, water availability, physio-chemical properties, and depth jointly exert impacts on τ. The data-driven approaches allow us to identify their interactions, thereby enriching our comprehension of mechanisms driving nonlinear τ–environment relationships at global to local scales. The distributions of dominating factors of τ at local scales were mapped for purposes of identifying context-dependent controls on τ across different regions. We further reveal that the current Earth system models may underestimate τ by comparing model-derived maps with our observation-derived τ maps. The resulting maps, with new insights, as demonstrated in this study, will facilitate future modelling efforts relating to carbon cycle–climate feedbacks and support effective carbon management. The dataset is archived and freely available at https://doi.org/10.5281/zenodo.14560239 (Zhang, 2025a).