Rising CO₂ levels are projected to surpass 550 ppm by 2050. However, uncertainties remain about how elevated CO₂ (eCO₂) will affect forest structural dynamics and carbon sequestration, hampering the accuracy of climate models. Free-air CO₂ enrichment (FACE) experiments simulate future CO₂ levels and offer critical data to address these uncertainties. However, our ability to fully leverage data from FACE experiments is limited by the challenges associated with accurately measuring forest structure and carbon storage over time. This project uses 3D terrestrial laser scanning (TLS), a cutting-edge, non-destructive method for quantifying forest structure with unprecedented precision. Multi-temporal TLS-derived metrics can provide insights into forest structural dynamics at high temporal and spatial resolution. Integrating these data into simulation models, such as the TROLL 4.0 model, will refine predictions of carbon storage and forest dynamics under eCO₂. The project will: (OBJ.1) Quantify forest structural dynamics at EucFACE using multi-temporal TLS data. (OBJ.2) Improve forest dynamics models by integrating TLS-derived metrics to enhance carbon stock predictions. (OBJ.3) Expand these methods to diverse FACE sites globally, such as AmazonFACE and BIFoR FACE. By combining innovative TLS technology with FACE data, this research will reduce uncertainties in forest carbon dynamics and inform effective climate solutions.