IceBench-S2S：一种深度学习用于挑战性亚季节至季节每日北极海冰预测的基准
IceBench-S2S: A Benchmark of Deep Learning for Challenging Subseasonal-to-Seasonal Daily Arctic Sea Ice Forecasting in Deep Latent Space
AI总结：
IceBench-S2S通过深度学习框架提升北极海冰预测的季节尺度能力，为极地环境监测提供统一的训练和评估流程。
摘要：
北极海冰在调节地球气候系统中起着关键作用，显著影响极地生态稳定性和沿海地区的人类活动。最近的人工智能进展促进了技能全面的极地海冰预测系统的发展，数据驱动的方法在准确性、计算效率和预测提前量方面展现出巨大的潜力，优于传统基于物理的数值模型。尽管深度学习（DL）预测模型取得了最新进展，但大多数模型的技能预测提前量仍局限于每日亚季节尺度和最多六个月的月平均值，这严重阻碍了它们在现实世界中的应用，例如北极运输和科学调查的航运计划。将每日预测从亚季节扩展到季节（S2S）尺度在操作应用中具有科学重要性。为弥补当前DL模型预测提前量与显著每日S2S尺度之间的差距，我们引入了IceBench-S2S，这是首个全面评估DL方法以缓解连续180天北极海冰浓度预测挑战的基准。它提出了一种通用框架，首先将每日海冰数据的空间特征压缩到深度潜在空间。随后，时间连接的深度特征通过DL基于的预测骨干模型进行建模，以预测S2S尺度的海冰变化。IceBench-S2S为不同骨干模型提供了一个统一的训练和评估流程，同时为极地环境监测任务中的模型选择提供了实用指导。
英文摘要：
Arctic sea ice plays a critical role in regulating Earth's climate system, significantly influencing polar ecological stability and human activities in coastal regions. Recent advances in artificial intelligence have facilitated the development of skillful pan-Arctic sea ice forecasting systems, where data-driven approaches showcase tremendous potential to outperform conventional physics-based numerical models in terms of accuracy, computational efficiency and forecasting lead times. Despite the latest progress made by deep learning (DL) forecasting models, most of their skillful forecasting lead times are confined to daily subseasonal scale and monthly averaged values for up to six months, which drastically hinders their deployment for real-world applications, e.g., maritime routine planning for Arctic transportation and scientific investigation. Extending daily forecasts from subseasonal to seasonal (S2S) scale is scientifically crucial for operational applications. To bridge the gap between the forecasting lead time of current DL models and the significant daily S2S scale, we introduce IceBench-S2S, the first comprehensive benchmark for evaluating DL approaches in mitigating the challenge of forecasting Arctic sea ice concentration in successive 180-day periods. It proposes a generalized framework that first compresses spatial features of daily sea ice data into a deep latent space. The temporally concatenated deep features are subsequently modeled by DL-based forecasting backbones to predict the sea ice variation at S2S scale. IceBench-S2S provides a unified training and evaluation pipeline for different backbones, along with practical guidance for model selection in polar environmental monitoring tasks.
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