BENGUP

Benguela Upwelling Prediction

The problem!

Off the shore of the southwestern African coasts, lies a crucial ecological and economic hub known as the Angola-Benguela Upwelling System (ABUS). This unique region, characterized by strong upwelling dynamics, supports a diverse marine ecosystem, and sustains the livelihoods of local fishing communities. However, in recent decades, this ecosystem has faced unprecedented challenges, with fish stocks fluctuating and some species nearing collapse. Overfishing alone cannot account for these variations; the interplay between fishing pressure and natural variability holds the key. 

The ABUS ecosystem is susceptible to interannual climate events, notably the occasional emergence of warm and cold events, known as Benguela Niño and Niña. These events have far-reaching consequences, affecting regional climate (rainfall patterns) and the local ecosystem (coastal upwelling intensity, distribution and the abundance of marine resources). The BENGUP project, for “BENGuela Upwelling Prediction”, spearheaded by Dr. Bachèlery, in collaboration with experts from the University of Bergen (UiB), aims to develop a groundbreaking prediction system for Benguela Niño and Niña events and their associated impacts, offering seasonal forecasts for variability in the ABUS.

 

Why does it Matters?

The importance of this project extends beyond the local context. In a world facing population growth and environmental change, the ABUS represents a critical and vulnerable "marine oasis." The successful development of a prediction model for this region holds the potential to revolutionize the management of this marine oasis and ensure the long-term sustainability of living marine resources. It can also serve as a template for similar ecosystems worldwide.

 

How can we do this?

Although predictions are increasingly needed in the ABUS, skilled forecasts that accurately predict the occurrence and variability of extreme events in the region are still lacking. There are several reasons why they have been hard to come by. Let’s break it down in simpler terms:

Why Skillful Predictions for the ABUS Sector Have Been Lacking? Computer (dynamical) forecasting models have been traditionally utilized to predict weather, climate, and ocean conditions. But these models are not perfect, and they might lead to errors in regions with complex dynamics. This is the case for the tropical Atlantic Ocean and upwelling systems that as today remain a challenge for climate prediction. A second reason for this is our lack of understanding! Until recently, we did not fully understand how certain events in this area, like Benguela Niño and Niña, were triggered. These events can have a big impact on the ecosystem. To predict them, we first needed to figure out what causes them.

How to break the challenge and predict Extreme Events?

To tackle the challenge of predicting extreme events in the ABUS, the BENGUP project has defined three objectives that lie in understanding the complex web of factors at play. Dynamical prediction systems play a crucial role in this process. They not only provide a large amount of data but also are like powerful digital laboratories that replicate the environment and help us understand the ocean and atmosphere dynamics. The project firstly aimed at evaluating the capacity (or prediction skills) of the existing forecasting models and secondly, understanding what makes a model good at forecasting extreme events; By comparing these model predictions with real-world observations, we can see where our models are accurate and where they need improvement. Identifying uncertainties in our predictions is crucial, and we work to reduce these uncertainties over time. Finally, the third objective aims at developing a new prediction system that captures the predictability of ABUS events and their impact on the marine ecosystem. To accomplish this, innovative and cutting-edge machine learning methods can be used to overcome the obstacles faced by dynamic prediction systems!

 

Summary of BENGUP objectives!

Objective 1: Assess the predictability of key ocean drivers of interannual variability in the ABA (Angola-Benguela Area) within existing dynamical systems.

Objective 2: Investigate the factors that make a forecast model capable of predicting extreme events in the ABA. This objective aims to improve our understanding of the processes that drive abrupt environmental events such as Benguela Niño and Niña.

Objective 3: Develop a new forecasting system capable of capturing the predictability of ABA events, including Benguela Niño and Niña. This objective focuses on creating a reliable forecasting tool that can support local ecosystem management and ensure the long-term sustainability of marine resources.