We are happy to announce that our PhD student Nabir Mamnun successfully defended his PhD thesis entitled Uncertainty Quantification for Ocean Biogeochemical Models.

Nabir’s PhD investigates the uncertainties associated with ocean Biogeochemical (BGC) models, which are crucial for predicting climate change. The study employs two key methodologies for improving model parameters and reducing uncertainties:

• sensitivity analysis revealing the significance of certain parameters in influencing model outputs in different ocean environments and
• parameter estimation used for ensemble data assimilation.

The research spans from 1-D models at specific ocean stations in the North Atlantic (BATS) and the Mediterranean (DYFAMED) to a global 3-D model, introducing spatial variations in parameter values. Key parameters influencing BGC simulation, such as grazing rate, maximum chlorophyll-to-nitrogen ratio, photosynthesis–irradiance parameters, and chlorophyll degradation rate, are identified. Substituting default values with optimized ones enhances model accuracy in both 1-D and 3-D configurations, as evidenced by reduced errors. The spatial variability of obtained parameter values aligns with observations, suggesting the effectiveness of ocean color data in constraining BGC simulations. The study emphasizes the importance of spatially varying parameter optimization, showcasing potential benefits for regional and global 3-D BGC models. Ultimately, the research contributes to a more comprehensive understanding of the relationship between ocean BGC processes and the carbon cycle, which is particularly important in the view of increasing climate change.

Congratulations, Nabir!