标题： DeepSeasons：一种深度学习的尺度选择方法用于季节性预测 显示英文标题

标题： DeepSeasons: a Deep Learning scale-selecting approach to Seasonal Forecasts

摘要： 由于大气动力学的固有混沌特性，季节性预测仍然具有挑战性。 本文介绍了DeepSeasons，一种新的深度学习方法，旨在提高季节性预测的准确性和可靠性。 利用先进的神经网络架构和广泛的历史气候数据集，DeepSeasons能够识别气候变量中的复杂非线性模式和依赖关系，其技能水平与基于GCM的预测方法相似或有所改善，且成本显著降低。 该框架还允许针对特定区域或变量进行定制化应用，而不是整体预测整个大气/海洋系统。 所提出的方法还允许直接预测异常值和时间均值，为长期预测开辟了新途径，并突显了其在气候敏感领域业务部署的潜力。 这种创新方法有望在管理气候相关风险和决策过程方面取得重大改进。 显示英文摘要

摘要： Seasonal forecasting remains challenging due to the inherent chaotic nature of atmospheric dynamics. This paper introduces DeepSeasons, a novel deep learning approach designed to enhance the accuracy and reliability of seasonal forecasts. Leveraging advanced neural network architectures and extensive historical climatic datasets, DeepSeasons identifies complex, nonlinear patterns and dependencies in climate variables with similar or improved skill respcet GCM-based forecasting methods, at a significant lower cost. The framework also allow tailored application to specific regions or variables, rather than the overall problem of predicting the entire atmosphere/ocean system. The proposed methods also allow for direct predictions of anomalies and time-means, opening a new approach to long-term forecasting and highlighting its potential for operational deployment in climate-sensitive sectors. This innovative methodology promises substantial improvements in managing climate-related risks and decision-making processes.