Anthropogenic land use and land cover change (LULCC), is projected to continue in the future. However, the influence of forest management on forest productivity change and subsequent LULCC projections remain under-investigated. This study explored the impacts of forest management-induced change in forest productivity on LULCC throughout the 21st century. Specifically, we developed a framework to softly couple the Global Change Analysis Model and Global Timber Model to consider forest management-induced forest productivity change, and projected future LULCC across the five Shared Socioeconomic Pathways (SSPs). We found future increases in forest management intensity overall drive the increase of forest productivity. The forest management-induced forest productivity change shows diverse responses across all SSPs, with a global increase from 2015 to 2100 ranging from 3.9% (SSP3) to 8.8% (SSP1). This further leads to an overall decrease in the total area with change of land use types, with the largest decrease under SSP1 (-7.5%) and smallest decrease under SSP3 (-0.7%) in 2100. Among land use types, considering forest management-induced change significantly reduces the expansion of managed forest, and also reduces the loss in natural land in 2100 across SSPs. This suggests that ignoring forest management-induced forest productivity change underestimates the efficiency of wood production, overestimates the managed forest expansion required to meet the future demand, and consequently, potentially introduces uncertainties into relevant analyses, e.g., carbon cycle and biodiversity. Thus, we advocate to better account for the impacts of forest management in future LULCC projections.

Land use and land cover change (LULCC) represents a key process of human-Earth system interaction and has profound impacts on ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regionally LULCC projections by integrated assessment models. The uncertainty associated with different spatial downscaling methods and its impacts on subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on the impacts on the carbon cycle over ABoVE domain. Specifically, we used the Future Land Use Simulation model (FLUS) and Demeter model to generate 0.25-degree gridded LULCC data with the same input of regional LULCC projections from Global Change Analysis Model, under SSP126 and SSP585. The two sets of downscaled LULCC were used to drive CLM5 to prognostically simulate terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial-temporal differences between two LULCC datasets under both SSP126 and SSP585. The LULCC differences further lead to large discrepancies in the spatial patterns of projected carbon cycle variables, which are more than 79% of the contributions of LULCC in 2100. Besides, the difference for LULCC and carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by spatial downscaling process in future LULCC projections and carbon cycle simulations.