Calibration of the MEMS v1 model over a continental soil inventory: a comparison of MCMC and 4DEnVar methods

Abstract

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. Here, we test 4-Dimensional Ensemble Variational data assimilation (4DEnVar) method to parameterize the MEMS v1 SOC model using data from the LUCAS network and compare its performance against MCMC calibration. Additionally, we performed an experiment where we adjusted the litter input calculation to see if the two calibration methods react differently to the change. The total SOC projections from both parameterizations showed similar improvements though the produced parameter sets differed. A thorough analysis revealed that the detailed SOC states differed from each other, but we also lacked information to determine which parameter set was closer to the truth. Furthermore, changing the litter input partition highlighted how much that assumption affects the calibration results with both methods. 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 as careful examination on how different data sets affect the model calibration.