On the magnitude and uncertainties of global and regional soil organic carbon: A comparative analysis using multiple estimates

Abstract. Globally, soil is one of the largest terrestrial carbon reservoirs, with soil organic carbon (SOC) regulating overall soil carbon dynamics. Robust quantification of SOC stocks in existing global observation-based estimates avails accurate predictions in carbon climate feedbacks and future climate trends. In this study, we investigated global and regional SOC estimates, based on five widely used global gridded SOC datasets (HWSD, WISE30sec, GSDE, SoilGrids250m, and GSOCmap), a regional permafrost dataset from Mishra et al. (UM2021), and a global-scale soil profile database (the World Soil Information Service soil profile database, WoSIS) reporting measurements of a series physical and chemical edaphic attributes. Our comparative analyses show that the magnitude and distribution of SOC varies widely among datasets, with certain datasets showing region-specific robustness. At the global scale, the magnitude of SOC stocks simulated by GSDE, GSOCmap, and WISE30sec are comparable, while estimates of SoilGrids250m and HWSD are at the upper and lower ends, respectively. Global SOC stocks ranged from 577–1171 Pg C and 1086–2678 Pg C at 0–30 cm and 0–100 cm depth. The spatial distribution of SOC stocks varies greatly among datasets, especially in the northern circumpolar and Tibetan Plateau permafrost regions. In general, the UM2021 and WISE30sec perform better in the northern circumpolar permafrost regions, and GSDE performs better in China. SOC stocks estimated by different datasets also show large variabilities across different soil layers and biomes. Overall, GSOCmap performs well at 0–30 cm depth, while SoilGrids250m and GSDE perform better at multiple depths. Among the five gridded global datasets, SoilGrids250m exhibits a more consistent spatial pattern and depth distribution with WoSIS. Large uncertainties in existing global gridded SOC estimates are generally derived from soil sampling density, diverse sources and mapping methods for soil datasets. We call for future efforts for standardizing soil sampling efforts, cross-dataset comparison, proper validation, and overall global collaboration to improve SOC estimates. The data are available at https://doi.org/10.6084/m9.figshare.20220234 (Lin et al., 2022).