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 (3853485 20 years (mean, with a variation range from the 2.5th to 97.5th percentile)) is over 8 times longer than topsoil τ (15137 11 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 datadriven 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).