A coupled physical-biogeochemical modeling approach to investigate the dynamics of the Benguela Upwelling System - Salama, Amr Talaat; Lovato, Tomas; Butenschön, Momme; Zavatarelli, Marco (Frontiers in Marine Science, 2025)
The Benguela Upwelling System is one of the most productive marine coastal ecosystems globally, driven by wind-induced upwelling of cold, nutrient-rich deep waters. However, the system’s complexity, combined with data scarcity, has left its dynamics and long-term response to a warming climate insufficiently understood. This study employs a high-resolution coupled physical-biogeochemical modeling system, using a two-way nesting strategy, to investigate the dynamics of the Benguela Upwelling System over a four-decade period (1980–2020). The physical model component, the Nucleus for European Modeling of the Ocean (NEMO), is coupled with the Biogeochemical Flux Model (BFM) to reproduce both physical and biogeochemical dynamics within a high-resolution Benguela domain. The physical component demonstrates good skill in replicating observational annual and seasonal climatologies of seawater temperature, salinity, and near-surface currents. The simulated biogeochemical fields satisfactorily compare with observational datasets available in the Benguela region for inorganic nutrients, dissolved oxygen, and upper ocean Chlorophyll-a (Chl-a) concentrations. Model outcomes were then used to investigate the long-term sea surface temperature and Chl-a trends by focusing on the upwelling zone, where a cooling trend was detected in both the northern and southern Benguela subregions, suggesting the occurrence of an upwelling intensification in recent decades. Although a positive Chl-a trend was observed in both subregions, the loose correspondence in either location or timing with the surface temperature signal indicates that algal growth is only partly influenced by the upwelling intensity. This coupled modeling framework provides valuable insights into the Benguela Upwelling System and could serve as a basis for improving our understanding of the variability in physical and ecological processes over recent decades.