用状态空间模型提升北极海冰季节预报精度
Seasonal Forecasting of Pan-Arctic Sea Ice with State Space Model
- 融合注意力机制的状态空间模型IceMamba
- RMSE和ACC均优于25种对比模型
- 适合气候适应与极地生态研究者参考
人为气候变化导致的北极海冰快速消退，对原住民社区、生态系统和全球气候系统构成重大风险，亟需高精度的季节性海冰预测。虽然动力模型在短期预报中表现良好，但在长期预测中受限且计算成本高；深度学习模型虽更高效，却难以处理复杂的季节变化和不确定性。本研究提出IceMamba，一种将先进注意力机制融入状态空间模型的深度学习架构。通过与25种知名模型（包括动力、统计和深度学习方法）的对比分析，实验表明IceMamba在泛北极海冰浓度的季节性预报中表现优异：平均均方根误差（RMSE）和异常相关系数（ACC）均优于所有测试模型，集成冰缘误差（IIEE）排名第二。该方法显著提升了对海冰变化的预判能力，为应对气候变化提供关键支持。
原文摘要 · Abstract (English)
The rapid decline of Arctic sea ice resulting from anthropogenic climate change poses significant risks to indigenous communities, ecosystems, and the global climate system. This situation emphasizes the immediate necessity for precise seasonal sea ice forecasts. While dynamical models perform well for short-term forecasts, they encounter limitations in long-term forecasts and are computationally intensive. Deep learning models, while more computationally efficient, often have difficulty managing seasonal variations and uncertainties when dealing with complex sea ice dynamics. In this research, we introduce IceMamba, a deep learning architecture that integrates sophisticated attention mechanisms within the state space model. Through comparative analysis of 25 renowned forecast models, including dynamical, statistical, and deep learning approaches, our experimental results indicate that IceMamba delivers excellent seasonal forecasting capabilities for Pan-Arctic sea ice concentration. Specifically, IceMamba outperforms all tested models regarding average RMSE and anomaly correlation coefficient (ACC) and ranks second in Integrated Ice Edge Error (IIEE). This innovative approach enhances our ability to foresee and alleviate the effects of sea ice variability, offering essential insights for strategies aimed at climate adaptation.
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