The advent of big data observations, innovations in experimental techniques, and advances in theory and modeling have revolutionized our understanding of terrestrial ecosystems and their responses to climate change. However, projecting land carbon cycle dynamics with high accuracy remain a grand challenge, as substantial uncertainties persist in simulating land carbon storage, fluxes, and their responses to climate change across Earth system models. This session seeks to address these challenges by showcasing cutting-edge research that advances land carbon cycle science. We invite contributions that span theoretical, experimental, data-driven, and modeling approaches, including: (1) Insights into the mechanisms regulating land carbon storage, fluxes, and their responses to climate change; (2) Innovations in process-based or data-driven modeling approaches; (3) Development of tools and methodologies for integrating diverse data streams into models; (4) Applications of AI to predict and understand land carbon cycle dynamics; and (5) Experimental studies that inform or validate model development.

• GC - Global Environmental Change