用机器学习提升次季节预报精度，显著延长关键气候信号预测时间。
AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales
- 采用24小时自回归机制与高层大气数据，减少误差累积。
- 对厄尔尼诺-南方涛动等气候现象的预测提前8天，且偏差更小。
- 能效比传统模型高200倍，适合大规模实时预报应用。
数据驱动模型在中短期天气预报中已接近数值预报水平，但扩展至次季节尺度面临长期自回归误差积累、系统性偏差随预报时长增长以及需多年数据独立验证等挑战。本文提出AIFS-SUBS，基于欧洲中期天气预报中心（ECMWF）的AIFS-CRPS模型，采用24小时自回归时间步长，引入平流层层和地表以上热辐射作为输入变量，并将2007–2011年设为独立验证窗口。评估两种配置：基于业务分析数据微调的AIFS-SUBS，以及仅用ERA5数据训练的AIFS-SUBS-ERA5。在第2–6周的预报中，AIFS-SUBS在概率技能上与运行中的集成预报系统（IFS）相当，同时降低系统性偏差。对于莫恩-朱利安振荡（MJO）的对流分量（OLR），AIFS-SUBS将相关系数高于0.5的技能预报提前8天；在全变量的多变量RMM指数上，表现持平或优于IFS。该模型还准确再现了MJO对热带气旋活动的调制效应。平流层预报性能优异，成功复现急变平流层变冷（SSW）频率及其地表影响。在AI Weather Quest竞赛中，AIFS-SUBS-ERA5在第3、4周的变量平均排名概率技能得分略胜于IFS。推理阶段能耗仅为IFS的约1/200，为实现更大规模实时集合预报提供了可能。AIFS-SUBS是ECMWF首个面向次季节尺度的机器学习预报模型。
原文摘要 · Abstract (English)
Data-driven models now rival numerical weather prediction in the medium range, but extending them to sub-seasonal lead times raises challenges absent at shorter horizons. Errors accumulate over long autoregressive rollouts, systematic biases grow with lead time, and several years of data must be held out for independent verification, even though machine-learning models otherwise benefit from longer training records. To address these challenges, we adapt ECMWF's AIFS-CRPS medium-range model. AIFS-SUBS adopts a 24h autoregressive time step to reduce error accumulation, adds stratospheric levels and top-of-atmosphere thermal radiation as predictors, and reserves 2007--2011 as an independent verification window. We evaluate two config-durations: AIFS-SUBS, fine-tuned on operational analyses, and AIFS-SUBS-ERA5, trained on ERA5 alone. Across weeks 2--6, AIFS-SUBS matches the operational Integrated Forecasting System (IFS) in probabilistic skill while reducing systematic biases. For the convective (OLR) component of the Madden--Julian Oscillation (MJO), AIFS-SUBS extends skilful forecasts (correlation > 0.5) by eight days relative to the IFS, while matching or exceeding the IFS for the full multivariate RMM index. AIFS-SUBS also reproduces the observed MJO modulation of tropical cyclone activity comparably. Stratospheric skill is particularly strong with AIFS-SUBS reproducing sudden stratospheric warming (SSW) frequency and surface impact. In the AI Weather Quest, AIFS-SUBS-ERA5 attains a variable-averaged ranked probability skill score slightly ahead of the IFS at weeks 3 and 4. At inference, AIFS-SUBS uses about 200 times less energy than the IFS, opening the door to much larger real-time ensembles. AIFS-SUBS is ECMWF's first machine-learning model targeted at sub-seasonal time-scales.
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