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 (SSTAs) and gridded fields of MHW presence and absence between 1982–2017, our model generates a spatiotemporal forecast of MHW occurrence up to 7 d 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 an improvement of 15 % in forecasting accuracy of MHW presence for a 7 d forecast horizon. We also demonstrate an improvement of MHW prediction accuracy as the forecast horizon decreases, albeit with a smaller discrepancy between the persistence benchmark, which also results in high accuracy for the 3 d forecasts. Our proposed ML methodology offers a data-driven prediction of MHWs 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.