Uncertainty quantification for ocean biogeochemical models ...

• Main Author: Mamnun, Nabir
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Universität Bremen 2024
• Subjects: Ensemble Data Assimilation; Parameter Estimation; Ocean Ecosystem Model; Marine Primary Production; Ocean Color; 550; North Atlantic
• Online Access: https://dx.doi.org/10.26092/elib/2923; https://media.suub.uni-bremen.de/handle/elib/7841

Predicting climate change necessitates a thorough understanding of marine biogeochemical (BGC) processes and the coupling between marine ecosystems and the global carbon cycle. Ocean BGC models are tools employed for this purpose. However, current ocean models used to simulate and thus better understand the ocean BGC processes are highly uncertain in their parameterization. This work delves into research to quantify uncertainties that arise in ocean BGC models and obtain improved parameters to reduce those uncertainties utilizing the BGC ocean model Regulated Ecosystem Model Version 2. A Global Sensitivity Analysis (GSA) is performed to identify which parameters most influence the uncertainty of model outputs in a one-dimensional (1-D) configuration at two ocean sites in the North Atlantic (BATS) and the Mediterranean Sea (DYFAMED). This work finds that the grazing parameter, the maximum chlorophyll-to-nitrogen ratio, the photosynthesis parameters, and the chlorophyll degradation rate are significant for BGC ...