A new Hybrid ensemble data assimilation system is implemented with a Massachusetts Institute of Technology general circulation model (MITgcm) of the Red Sea. The system is based on the Data Assimilation Research Testbed (DART) and combines a time-varying ensemble generated by the Ensemble Adjustment Kalman filter (EAKF) with a pre-selected quasi-static (monthly varying) ensemble as used in an Ensemble Optimal Interpolation (EnOI) scheme. The goal is to develop an efficient system that enhances the state estimate and model forecasting skill in the Red Sea with reduced computational load compared to the EAKF. Observations of satellite sea surface temperature (SST), altimeter sea surface height (SSH), and in situ temperature and salinity profiles are assimilated to evaluate the new system. The performance of the Hybrid scheme (here after Hybrid-EAKF) is assessed with respect to the EnOI and the EAKF results. The comparisons are based on the daily averaged forecasts against satellite SST and SSH measurements and independent in situ temperature and salinity profiles. Hybrid-EAKF yields significant improvements in terms of ocean state estimates compared to both EnOI and EAKF, in particular mitigating for dynamical imbalances that affects EnOI. Hybrid-EAKF improves the estimation of SST and SSH root-mean-square-differences by up to 20% compared to EAKF. High-resolution mesoscale eddy features, which dominate the Red Sea circulation, are further better represented in Hybrid-EAKF. Important reduction, by about 75%, in computational cost is also achieved with the Hybrid-EAKF system compared to the EAKF. These significant improvements were obtained with the Hybrid-EAKF after accounting for uncertainties in the atmospheric forcing and internal model physics in the time-varying ensemble.