Machine learning for prediction of sea ice stability

Arctic sea ice, the vast body of frozen water near the North Pole, has been in steady decline since satellite observations began. While state-of-the-art models attempt to project future scenarios, they often show significant discrepancies, even though the sea ice system is generally considered to decline linearly with rising temperatures. Machine learning models, although they may lack the ability to fully explain the underlying physical processes, offer a complementary approach. By training these models on existing data, we can generate plausible future predictions that are less influenced by the biases inherent in traditional modeling methods. In this study, we evaluate several machine learning architectures to identify the most effective ones. Using the best-performing model, we explore the stability and potential hysteresis behaviors of the Arctic sea ice system.