GWSM4C: A Global Wave Surrogate Model for Climate Simulation Based on a Convolutional Architecture

Ocean waves are a critical component of the Earth system, yet directly coupling wave models in climate simulations is computationally expensive. We introduce GWSM4C, a Global Wave Surrogate Model designed for climate simulations using a convolutional neural network architecture. GWSM4C efficiently emulates the behavior of numerical wave models, predicting global wave parameters with high accuracy and a fraction of the computational cost. Validation against numerical model outputs confirms that GWSM4C captures the global wave climate patterns, seasonal variability, and extreme wave events, making it a viable alternative for long-term climate studies requiring wave-atmosphere-ocean interactions.

Ocean Engineering