An abundant amount of different data is required to calibrate soil organic carbon (SOC) models to represent ecosystems at large-scale. However, due to challenges related to model state projections, this calibration becomes very computationally heavy with traditional calibration methods. In this work, we test 4-Dimensional Ensemble Variational data assimilation (4DEnVar) method to parameterize the MEMS v1 SOC model using data from the LUCAS soil sampling network and compare its performance against MCMC calibration. Comparing the total SOC projections from both parameterizations to the validation datasets showed similar improvements even though the produced parameter sets differed. A thorough analysis revealed that the detailed SOC states were not similar to a degree that is meaningful for future predictions, but we also lacked information to determine which parameter set was closer to the truth. Our results here establish 4DEnVar as an applicable calibration method for SOC models but also highlight the need for more nuanced validation methods as well careful examination how different data sets affect the model calibration.