<p>Accurate airflow modeling is essential for understanding airflow patterns and for designing effective ventilation systems. However, calibration of airflow models remains challenging due to complex inter-zonal interactions, limited availability of airflow measurements, and uncertainty in input parameters. Additionally, few studies have focused on multizone airflow calibration. In this study, a novel calibration framework is proposed that combines global sensitivity analysis (GSA) using the Sobol method with the Ensemble Kalman Filter (EnKF) for multizone airflow modeling by CONTAM. A Python tool is developed to perform Sobol analysis, and a Java interface is applied to automate the coupling between simulation and calibration workflows. The framework is applied to a 16-story institutional building where CO₂ tracer gas experiments were conducted to characterize both inter-zone and inter-floor airflow behavior. A detailed CONTAM model representing three floors is developed and calibrated using the EnKF approach. GSA is applied to identify the most influential parameters affecting peak CO₂ concentrations, enabling a reduction in calibration variables. Results indicate that in the source room, when the air-conditioning (AC) system is turned off, the initial indoor CO₂ concentration and generation rate account for 94% of the variance in peak CO₂ levels. When the AC is enabled, their contribution decreases to 62%, while the influence of door opening and undercut increases to 25% and 11%, respectively. In adjacent rooms, initial concentration and generation rate also dominate. The EnKF-based calibration substantially improved the prediction accuracy with up to 6% CVRMSE of multizone airflow modeling, achieving reliable agreement with measurements for both room-to-room and floor-to-floor tests.</p>