Representing dynamic grassland density in the land surface model ORCHIDEE r9010

Abstract. In semi-arid regions, grasses and shrubs often form spatially heterogeneous patterns interspersed with bare soil as a strategy to optimize resource use and maximise productivity. Accurately representing the matrix of vegetation and bare soil in global land surface models is essential for advancing the understanding of the carbon, water, and dust cycles. This study focuses on grasslands using the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms), which originally assumes a globally fixed grassland density representing a fixed number of individuals per unit of land. This assumption, referred to as the fixed density approach, limits the model's ability to capture grassland responses to environmental changes, resulting in unsustainable productivity and unrealistically frequent mortality events, particularly in resource-limited regions. To address these limitations, we introduced a dynamic density approach that simulates grassland density based on indicators of vegetation growth, such as reserve and labile carbon content in the grass. The simulated grassland density was consistent with field-based estimates from five regional case studies and showed a better representation of bare soil in grasslands than the fixed density approach. The emerging positive correlation between precipitation and simulated grassland density supported the validity of the approach. Compared to the fixed density approach, the dynamic density approach substantially reduced simulated mortality events, raised the aridity threshold for frequent mortality, improved the simulated leaf area index (LAI) both globally and in key semi-arid regions, and maintained realistic grassland productivity in regions where the presence of grassland is confirmed by remotely sensed LAI. This study not only demonstrates that simulating grassland density as a function of carbon availability improves ORCHIDEE's capacity to capture grassland dynamics under environmental variability, but also provides a promising foundation for investigating dust dynamics and subsequent land–atmosphere feedbacks in (semi-)arid regions.