Through an ambitious project led by scientists at University College London (UCL), the National Oceanography Centre (NOC), and the École Normale Supérieure (ENS) Paris-Saclay in France, scientists will use novel observations of coastal wave breaking with advanced modeling and machine learning to work out the importance of this notoriously complex and hard to measure phenomenon.
Specifically, the project, called WAVECLIM, will look to fill a gap in understanding the role these often dramatic coastal processes play in global climate modeling.
This innovative project is one of the first opportunity seeds in the UK’s Advanced Research and Invention Agency’s (ARIA) Scoping Our Planet opportunity space, announced in October last year. ARIA’s seed funding supports ambitious research that can challenge assumptions, open up new research paths, and provide steps toward new capabilities
“It is well known that ocean wave breaking at the coast plays a big role in air-sea exchanges, sediment transport, and coastal erosion,” explains Professor Christine Gommenginger, who leads the project at NOC. “But these complex coastal processes are largely absent from current climate models.”
“While waves in the open ocean are starting to be included in some climate models, coastal wave breaking is still disregarded,” adds Frederic Dias from ENS Paris-Saclay. “This is a critical gap in our understanding of how coastal seas influence and impact the global climate system.”
The WAVECLIM project will change this using advanced sensor technology and machine learning to capture and integrate coastal wave-breaking dynamics into predictive models.
State-of-the-art monitoring equipment, including LIDAR, drones, and stereoscopic cameras, will be deployed to provide unprecedented data on coastal wave breaking under diverse conditions.
Machine learning models trained on these observations will be integrated into climate models, addressing biases and enhancing the accuracy of future climate predictions.
“This pioneering approach builds on recent successes in embedding machine learning into climate modeling, promising more realistic projections at a fraction of the computational cost,” says Serge Guillas, Principal Investigator for WAVECLIM, from UCL.
“The work is expected to yield transformative insights into how coastal processes influence global climate systems, especially in the face of rising sea levels and increased storm activity.”
Through this collaboration, the partners hope to address critical knowledge gaps, paving the way for improved representation of coastal sea complexities in the next generation of climate models.
