Key Expoitable Results (KERs)

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.

Revealing the ongoing changes in ocean dynamics and their impact on marine ecosystems requires the joint analysis of multiple variables. However, global observational records only cover a few decades, posing a challenge in the separation of climatic trends from internal dynamical modes. Here, we apply an empirical stochastic model to identify the emergent patterns of trends in six fundamental components of upper ocean physics. We analyze a data-driven reconstruction of the ocean state covering the 1993–2018 period. We found that including temporal derivatives in the state vector enhances the description of the ocean’s dynamical system. Once Pacific oscillations are properly accounted for, averaged surface warming appears >60% faster, and a deep reshaping of the seascape is revealed. A clustering of the trend patterns identifies the main factors that drive observed trends in chlorophyll a concentration. This data-driven approach provides a wider framework for empirical climate modeling.

The physical environment both above and below the ocean surface has changed dramatically during the last century. Changes in the marine environment induced by increased release of greenhouse gases and direct exploitation of resources include increased ocean temperature, decreased salinity and pH, and removal of apex predators. The risk of ecological regime shifts occurring has similarly increased. A variety of methodologies to identify regime shifts have already been used in the North Sea, which has become an important case study for the analysis of regime shifts in a semi-enclosed waterbody. The North Sea is regarded as a case study in part due to the operation of the continuous plankton recorder, which has provided detailed abundance records of phyto- and zooplankton for over 60 years. Here, we propose a new methodology to calculate regime shift likelihood for every month between 1958 and 2020. This unique model produces a single time series of regime shift likelihood, using sequential abundance data of more than 300 plankton species. We show the model's ability to identify when regime shifts occurred in the past by comparing it to previous less automated methodologies. We have validated the model for use in the North Sea by estimating how often false positives and false negatives are generated. Results from the model indicate evidence for three periods of high regime shift likelihood in various parts of the North Sea: between 1962 and 1972, between 1989 and 1999, and from 2002 until 2015. We show that these periods are consistent with previous estimates of North Sea regime shifts, and discuss possible applications of the model's output of a single time series.