Abstract:

Grasslands are the largest terrestrial ecosystem globally, with approximately 90% of their carbon stored as soil organic carbon (SOC), making them highly sensitive to climate change. Despite this, large−scale and high−resolution quantifications of SOC changes across the entire soil profile (0–100cm) under warming scenarios remain limited. To address this, this study employed a data−model fusion approach to quantify potential changes in SOC across the entire soil profile of natural grasslands in Northern China under a 1.5℃ warming scenario at a 1km spatial resolution. Results showed that warming could lead to an average decrease of 3.63%–4.22% in SOC density across the soil profile, equivalent to a soil carbon stock loss of 0.78 to 1.52Pg C (1Pg=10¹⁵g). However, these estimated carry substantial uncertainty, primarily due to limitations in input datasets and model representation of grassland productivity dynamics. Notably, projections varied significantly among different soil datasets, with SoilGrids250m projecting the largest SOC stock reduction (1.52Pg C, 95% confidence interval: 1.17–1.91Pg C), followed by WISE30sec (0.82Pg C, 95% confidence interval: 0.62–1.04Pg C) and GSDE (0.78Pg C, 95% confidence interval: 0.57–1.04Pg C). These findings underscored the potential negative impacts of global warming on grassland soil carbon storage, emphasizing the necessity of enhancing grassland ecosystem conservation and restoration efforts to ensure the sustainable use and development of these vital ecosystems.

Key words:  Global warming, Soil organic carbon, Grasslands, Model simulation, Data-model fusion