Periodic Reporting for period 1 - BENGUP (Climate and marine-ecosystem predictions in the Angola-Benguela Upwelling System)
Reporting period: 2021-09-01 to 2023-08-31
Off the southwestern African coast, the Angola-Benguela Upwelling System (ABUS) is a crucial ecological and economic hotspot. This region, with its robust upwelling patterns, sustains a diverse marine ecosystem and local fishing communities. However, it's been facing challenges in recent decades, with fish stocks fluctuating and some species nearing collapse. Overfishing alone doesn't explain this; it's the interplay of fishing and natural variability.
The ABUS ecosystem is affected by interannual climate events, like Benguela Niño and Niña, with far-reaching consequences for regional climate and the local ecosystem. Dr. Bachèlery's BENGUP project in collaboration with experts from the University of Bergen (UiB) aims to develop a pioneering prediction system for these events, providing seasonal forecasts for ABUS variability.
Why does it matter? This project has global significance. In a world dealing with population growth and environmental changes, the ABUS is a critical yet vulnerable "marine oasis." Developing a prediction model for this region could transform its management, ensuring the long-term sustainability of marine resources, and set an example for similar ecosystems worldwide.
But why have skillful predictions for the ABUS been elusive? It's partly because traditional computer forecasting models used for weather, climate, and ocean conditions have limitations, especially in regions with complex dynamics like the tropical Atlantic and upwelling systems. Additionally, our understanding of what triggers events like Benguela Niño and Niña was incomplete until recently. To predict them, we first needed to grasp their causes.
So, how do we tackle this challenge and predict extreme events in the ABUS? The BENGUP project has three key objectives. Dynamical prediction systems play a crucial role here. They provide vast data and serve as digital laboratories to replicate the environment, aiding our understanding of ocean and atmosphere dynamics. The project starts by evaluating the predictive skills of existing models and understanding what makes a model good at forecasting extreme events. By comparing model predictions with real-world observations, we pinpoint where improvements are needed. Reducing uncertainties in predictions is crucial, and we work on this continuously. Finally, the third objective is to develop a new prediction system using cutting-edge machine learning methods, overcoming obstacles faced by dynamic prediction systems. This project aims to provide more accurate predictions for a vital marine ecosystem, benefiting local communities and global conservation efforts.
The ABUS ecosystem is affected by interannual climate events, like Benguela Niño and Niña, with far-reaching consequences for regional climate and the local ecosystem. Dr. Bachèlery's BENGUP project in collaboration with experts from the University of Bergen (UiB) aims to develop a pioneering prediction system for these events, providing seasonal forecasts for ABUS variability.
Why does it matter? This project has global significance. In a world dealing with population growth and environmental changes, the ABUS is a critical yet vulnerable "marine oasis." Developing a prediction model for this region could transform its management, ensuring the long-term sustainability of marine resources, and set an example for similar ecosystems worldwide.
But why have skillful predictions for the ABUS been elusive? It's partly because traditional computer forecasting models used for weather, climate, and ocean conditions have limitations, especially in regions with complex dynamics like the tropical Atlantic and upwelling systems. Additionally, our understanding of what triggers events like Benguela Niño and Niña was incomplete until recently. To predict them, we first needed to grasp their causes.
So, how do we tackle this challenge and predict extreme events in the ABUS? The BENGUP project has three key objectives. Dynamical prediction systems play a crucial role here. They provide vast data and serve as digital laboratories to replicate the environment, aiding our understanding of ocean and atmosphere dynamics. The project starts by evaluating the predictive skills of existing models and understanding what makes a model good at forecasting extreme events. By comparing model predictions with real-world observations, we pinpoint where improvements are needed. Reducing uncertainties in predictions is crucial, and we work on this continuously. Finally, the third objective is to develop a new prediction system using cutting-edge machine learning methods, overcoming obstacles faced by dynamic prediction systems. This project aims to provide more accurate predictions for a vital marine ecosystem, benefiting local communities and global conservation efforts.
The outcomes! The project was organized into three primary Work Packages (WPs), each contributing to various goals. The first WP (WP1: research and understanding), tends to reach the scientific objectives of the project. Here is a simplified summary of the key outcomes of WP1 and how they are being used and shared.
WP1 has yielded significant results aimed at improving predictions of extreme events in the Angola-Benguela Upwelling System (ABUS). First, the project explored the limits of seasonal climate prediction in the ABUS, examining the existing dynamical prediction models. For this work, we evaluate the prediction skill of 2 state-of-the-art Earth System Models (ESMs), including the EC-EARTH, NorCPM and 7 dynamical predictions models from the North American Multi-Model Ensemble (NMME) project, and pre-operational models from the Copernicus Climate Change Service (C3S). The results showed that, despite significant efforts to enhance forecast quality, all dynamical systems exhibited low skill in predicting critical dynamic and thermodynamic features in the ABUS. These limitations were particularly evident in predicting the Sea Surface Temperature (SST) during the main season of Benguela Niño and Niña events.
After identifying the limitations of ESMs, the project assessed the source of error in historical Coupled Model Intercomparison Project Phase 6 (CMIP6) hindcasts model outputs. The goal was to understand why ESMs fail in simulating the variability in the ABUS and especially the extreme warm and cold Benguela events. The achievements included the identification of the key physical precursor's mechanisms leading to a wrong development of the events. This is valuable outputs for the scientific community to improve their capabilities and therefore has been shared with the climate prediction scientific community during a conference in 2022 and have been summarized in a forthcoming paper in a scientific Journal. In response to the challenges faced in predicting Benguela events, the project explored the potential of machine learning-based prediction models. A deep learning model was developed to predict Benguela events. Remarkably, the this model outperformed dynamical forecasting systems and demonstrated great capacities in predicting the peak-season of Benguela events, offering accurate forecasts several months in advance. These findings were presented to the research community in 2023 and will be showcased in a future international conference in 2024. A paper summarizing these results will be released soon! These results represent significant advancements in our ability to predict extreme events in the ABUS, offering promising avenues for improved forecasting and furthering our understanding of this critical marine ecosystem.
What's Next? The significant advancement in forecasting ABA variability and Benguela Niño-Niña events, achieved through deep learning models, has attracted considerable interest within the scientific community. Stemming from this interest, there is an initiative to create a user-friendly web-based warning system, designed to provide forecasting of extreme occurring events. Once launched, this innovative platform will grant open access to researchers, stakeholders, policymakers, and the broader public, offering the latest data and predictions. The website involves providing direct forecasts of Benguela events in the ABA, generated from the most recent satellite data available through Copernicus.
WP1 has yielded significant results aimed at improving predictions of extreme events in the Angola-Benguela Upwelling System (ABUS). First, the project explored the limits of seasonal climate prediction in the ABUS, examining the existing dynamical prediction models. For this work, we evaluate the prediction skill of 2 state-of-the-art Earth System Models (ESMs), including the EC-EARTH, NorCPM and 7 dynamical predictions models from the North American Multi-Model Ensemble (NMME) project, and pre-operational models from the Copernicus Climate Change Service (C3S). The results showed that, despite significant efforts to enhance forecast quality, all dynamical systems exhibited low skill in predicting critical dynamic and thermodynamic features in the ABUS. These limitations were particularly evident in predicting the Sea Surface Temperature (SST) during the main season of Benguela Niño and Niña events.
After identifying the limitations of ESMs, the project assessed the source of error in historical Coupled Model Intercomparison Project Phase 6 (CMIP6) hindcasts model outputs. The goal was to understand why ESMs fail in simulating the variability in the ABUS and especially the extreme warm and cold Benguela events. The achievements included the identification of the key physical precursor's mechanisms leading to a wrong development of the events. This is valuable outputs for the scientific community to improve their capabilities and therefore has been shared with the climate prediction scientific community during a conference in 2022 and have been summarized in a forthcoming paper in a scientific Journal. In response to the challenges faced in predicting Benguela events, the project explored the potential of machine learning-based prediction models. A deep learning model was developed to predict Benguela events. Remarkably, the this model outperformed dynamical forecasting systems and demonstrated great capacities in predicting the peak-season of Benguela events, offering accurate forecasts several months in advance. These findings were presented to the research community in 2023 and will be showcased in a future international conference in 2024. A paper summarizing these results will be released soon! These results represent significant advancements in our ability to predict extreme events in the ABUS, offering promising avenues for improved forecasting and furthering our understanding of this critical marine ecosystem.
What's Next? The significant advancement in forecasting ABA variability and Benguela Niño-Niña events, achieved through deep learning models, has attracted considerable interest within the scientific community. Stemming from this interest, there is an initiative to create a user-friendly web-based warning system, designed to provide forecasting of extreme occurring events. Once launched, this innovative platform will grant open access to researchers, stakeholders, policymakers, and the broader public, offering the latest data and predictions. The website involves providing direct forecasts of Benguela events in the ABA, generated from the most recent satellite data available through Copernicus.
While the project addresses fundamental scientific questions, it will also generate relevant information and new data useful to the scientific community and the Angolan-Namibian local stakeholders (marine natural resources managers, fishery industry and decision makers). The findings regarding the predictability of Benguela events and their sources will enhance climate prediction models in the Tropical Atlantic, benefiting climate and ocean operational services. Furthermore, the successful development of extreme Benguela events predictions is invaluable for local marine ecosystem managers, the fishing industry, and decision makers. It offers vital resources for monitoring the ecological impact of climate variability and adapting fisheries management strategies in these dynamic marine environments. This information will aid policy makers in addressing climate-related challenges, contributing to the sustainable development of ABUS. Information will be accessible to interested communities via a web page, facilitating collaboration and knowledge transfer!