Ayush Prasad, Sanxchep Sharma, Harshvardhan Agarwal

Recently, severe warm-water episodes have occurred frequently against a background trend of global ocean warming. Sea Surface Temperature anomalies have an impact on the integrity of marine ecosystems which is an important part of the Earth’s climate system. The drastic effects of Marine Heatwaves on aquatic life have been on a steady incline in the recent years, damaging aquatic ecosystems resulting in enormous loss of marine life. The study of Marine Heatwaves has arisen
as a fast-rising topic of inquiry. Operational forecasting and early warning systems that can predict such events can help in proactive planning and better mitigation strategies. In this study, the potential of machine learning models, namely Random Forest and N-BEATS, was evaluated to predict sea surface temperature on a seasonal scale using the NOAA OISST v2.1 dataset. The predicted sea surface temperature data was then used to forecast the occurrence of Marine Heatwaves up to a year in advance. The proposed models were tested across four historical Marine Heatwave events around the world. The results showed that the models were able to capture the onset, trend, and extent of the extreme events accurately.