推进海洋热浪预报：一种集成深度学习方法
Advancing Marine Heatwave Forecasts: An Integrated Deep Learning Approach
AI总结：
本文提出融合图表示、不平衡回归与时间扩散的全球海洋热浪深度学习预报框架，构建公开图数据集并证明其最长六个月预报优于传统数值模型。
摘要：
海洋热浪（MHWs）是一种极端气候现象，对海洋生态系统和相关产业构成重大挑战，并且受气候变化影响，其发生频率和强度正在不断增加。本研究提出一种集成深度学习方法，用于在全球范围内预报短期至长期的MHWs。该方法结合了用于建模气候数据空间属性的图表示、用于处理偏斜数据分布的不平衡回归，以及用于提升不同提前预报时间准确性的时间扩散。据我们所知，这是首个综合三种时空异常方法来预测MHWs的研究。此外，我们提出了一种可避免孤立节点的图构建方法，并提供了一个新的、公开可用的海表温度异常图数据集。我们考察了MHWs研究中损失函数与评估指标选择之间的权衡。我们通过聚焦历史热点区域分析全球MHWs可预报性的空间模式；结果表明，与传统数值模型相比，本方法在中南太平洋、非洲附近赤道大西洋、南大西洋以及高纬度印度洋等区域表现更优。我们强调时间扩散有潜力替代传统滑动窗口方法进行长期预报，可将预测能力提升至最长提前六个月。这些洞见不仅为机器学习在MHWs预报中的应用建立了基准，也增进了对通用气候预报方法的理解。
英文摘要：
Marine heatwaves (MHWs), an extreme climate phenomenon, pose significant challenges to marine ecosystems and industries, with their frequency and intensity increasing due to climate change. This study introduces an integrated deep learning approach to forecast short-to-long-term MHWs on a global scale. The approach combines graph representation for modeling spatial properties in climate data, imbalanced regression to handle skewed data distributions, and temporal diffusion to enhance forecast accuracy across various lead times. To the best of our knowledge, this is the first study that synthesizes three spatiotemporal anomaly methodologies to predict MHWs. Additionally, we introduce a method for constructing graphs that avoids isolated nodes and provide a new publicly available sea surface temperature anomaly graph dataset. We examine the trade-offs in the selection of loss functions and evaluation metrics for MHWs. We analyze spatial patterns in global MHW predictability by focusing on historical hotspots, and our approach demonstrates better performance compared to traditional numerical models in regions such as the middle south Pacific, equatorial Atlantic near Africa, south Atlantic, and high-latitude Indian Ocean. We highlight the potential of temporal diffusion to replace the conventional sliding window approach for long-term forecasts, achieving improved prediction up to six months in advance. These insights not only establish benchmarks for machine learning applications in MHW forecasting but also enhance understanding of general climate forecasting methodologies.
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