Deep learning-based forecasting of sea surface temperature in the interim future: application over the Aegean, Ionian, and Cretan Seas (NE Mediterranean Sea)

Abstract

Sea surface temperature (SST) is a key indicator of the global climate system and is directly related to marine and coastal ecosystems, weather conditions, and atmospheric events. Marine heat waves (MHWs), characterized by prolonged periods of high SST, affect significantly the oceanic water quality and thus, the local ecosystem, and marine and coastal activities. Given the anticipated increase of MHWs occurrences due to climate change, developing targeted strategies is needed to mitigate their impact. Accurate SST forecasting can significantly contribute to this cause and thus it comprises a crucial, yet challenging, task for the scientific community. Despite the wide variety of existing methods in the literature, the majority of them focus either on providing near-future SST forecasts (a few days until 1 month) or long-term predictions (decades to century) in climate scales based on hypothetical scenarios that need to be proven. In this work, we introduce a robust deep learning-based method for efficient SST forecasting of the interim future (1 year ahead) using high-resolution satellite-derived SST data. Our approach processes daily SST sequences lasting 1 year, along with five other relevant atmospheric variables, to predict the corresponding daily SST timeseries for the subsequent year. The novel method was deployed to accurately forecast SST over the northeastern Mediterranean Seas (Aegean, Ionian, Cretan Seas: AICS). Utilizing the effectiveness of well-established deep learning architectures, our method can provide accurate spatiotemporal predictions for multiple areas at once, without the need to be deployed separately at each sub-region. The modular design of the framework allows customization for different spatial and temporal resolutions according to use case requirements. The proposed model was trained and evaluated using available data from the AICS region over a 15-year time period (2008–2022). The results demonstrate the efficiency of our method in predicting SST variability, even for previously unseen data that are over 2 years in advance, in respect to the training set. The proposed methodology is a valuable tool that also can contribute to MHWs prediction.