Antarctic sea-ice extent (SIE) in 2023 hit the record low for both seasonal minimum and maximum, largely due to an exceptionally rapid decrease in November–December 2022 (periods 1 and 2) and exceptionally slow recovery in April–July 2023 (periods 3 and 4). This study assesses the capability of current operational subseasonal to seasonal (S2S) models to predict the extremely anomalous SIE during the four periods and analyze potential sources of biases and the influence of synoptic atmospheric circulation. Further, we examine the Antarctic sea-ice edge errors of S2S models during four periods. In periods 1 and 2, all S2S models fail to capture the exceptionally rapid decline, though two models perform comparably to climatology, which still deviates significantly from the verifying sea-ice analysis. Most models simulate a much-delayed rapid decrease and an increase in underestimated sea-ice edge error. In periods 3 and 4, more models show predictive skill comparable to damped anomaly persistence, and only two models outperform damped persistence and close to the verifying analysis. Further analysis shows that the cumulative SIE bias predicted by S2S models is strongly correlated with its initial bias, initial averaged sea surface temperature and the sea level pressure gradient bias.

Marine heat waves (MHWs) are prolonged periods of elevated ocean temperatures that can devastate marine ecosystems, fisheries, and coastal communities. Skillfully predicting these events with sufficient lead time is crucial for mitigating their adverse effects. This study presents a probabilistic subseasonal MHW forecast tool using a U-Net-based neural network architecture, with a focus on the Northern Indian Ocean and the Arabian Sea. The model was trained using sea surface temperature and sea surface height reanalysis data. The U-Net-based forecast tool demonstrated significant predictive skill up to 10 weeks in advance across various deterministic and probabilistic skill metrics. The model outperformed persistence and climatology-based benchmarks, especially in the tropical warm pool. Future applications of explainable artificial intelligence (XAI) methods have the potential to identify the sources of predictive skill, inform understanding of underlying dynamics, and improve dynamic subseasonal to seasonal forecast models.