Marine Heatwaves in the Mediterranean Sea: A Convolutional Neural Network study for extreme event prediction

Abstract. In recent decades, the Mediterranean Sea has experienced a notable rise in the occurrence and intensity of extreme warm temperature events, referred to as Marine Heatwaves (MHWs). Hence, the ability to forecast Mediterranean MHWs in the short term is an area of ongoing research. Here, we introduce a novel machine learning (ML) approach, specifically tailored for short-term predictions of MHWs in the basin, using an Attention U-Net Convolutional Neural Network. Trained on daily Sea Surface Temperature anomalies and gridded fields of MHW presence and absence between 1982–2017, our model generates a spatiotemporal forecast of MHW occurrence up to 7 days in advance. To ensure robust performance, we explore various configurations, including different forecast horizons and U-net architectures, number of input days, features, and different subset splits of train-test datasets. Comparative analysis against a Persistence benchmark reveals that our model outperforms the benchmark across both forecast horizons. For the 7-day forecast, the model achieves a 15 % improvement in forecasting accuracy of MHW presence over the Persistence, while for the 3-day forecast, this improvement percentage drops to 4.5 %. Notably, the discrepancy between our model and the benchmark narrows for shorter horizons, as the Persistence method also achieves high accuracy in the 3-day forecast. Our proposed ML methodology offers a data-driven alternative for MHWs prediction with reduced computational requirements, which can be applied across different regions of the global ocean, providing relevant stakeholders and management authorities with essential lead time for implementing effective mitigation strategies.