This overview paper details the findings from the Diving Deep: Forecasting Sea Surface Temperatures and Anomalies Challenge at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2024. The challenge focused on the data-driven predictability of global sea surface temperatures (SSTs), a key factor in climate forecasting, ecosystem management, fisheries management, and climate change monitoring. The challenge involved forecasting SST anomalies (SSTAs) three months in advance using historical data and included a special task of predicting SSTAs nine months ahead for the Baltic Sea. Participants utilized various machine learning approaches to tackle the task, leveraging data from ERA5. This paper discusses the methodologies employed, the results obtained, and the lessons learned, offering insights into the future of climate-related predictive modeling.

翻译：本综述论文详细阐述了2024年欧洲机器学习与数据库知识发现原理与实践会议（ECML PKDD）“深度探索：海表温度及异常预测挑战赛”的研究成果。该挑战赛聚焦于全球海表温度的数据驱动可预测性，这是气候预测、生态系统管理、渔业管理和气候变化监测的关键因素。挑战任务要求利用历史数据提前三个月预测海表温度异常，并包含一项针对波罗的海提前九个月预测海表温度异常的特殊任务。参赛者利用ERA5数据，采用了多种机器学习方法来解决该任务。本文讨论了所采用的方法、获得的结果以及汲取的经验教训，为未来气候相关预测建模的发展提供了见解。