延迟观测数据能显著提升厄尔尼诺预测，但复杂模型效果不增。
Predictability of El Niño from Delayed Observations
- 用延迟观测数据找最优预测时滞，结合多种模型验证非线性复杂度作用。
- 延迟观测使6个月前的预测优于基准方法，但更复杂模型无提升。
- 简单显式模型表现最优，说明过去信息的表示比模型复杂度更重要。
基于2026年7月前的月度Niño-3.4异常数据，研究延迟观测中蕴含的预测信息。岭回归识别出有效时滞，多层感知机与稀疏非线性动力学（SINDy）模型检验非线性复杂度是否带来直接预报优势；门控循环单元（GRU）与长短期记忆（LSTM）网络则通过内部学习时间表征提供互补测试。延迟观测在长达六个月的预报提前期上显著优于持续性与气候学基准，但增加模型复杂度并未系统提升性能。历史递归实验支持一种简单的显式SINDy递推形式，并偏好浅层递归结构，内部时间表征学习无明显收益。结果表明，Niño-3.4演化存在紧凑的预测表示，过去信息的表达比模型复杂度更具决定性。作为前瞻性应用，所选模型用于预测2026年正在发展的事件，其预测轨迹与已完成的历史厄尔尼诺事件进行对比。
原文摘要 · Abstract (English)
Using monthly Niño-3.4 anomalies through July 2026, we investigate how much predictive information is contained in delayed observations of the index. Ridge regression identifies informative delays, while multilayer perceptron and sparse identification of nonlinear dynamics (SINDy) models test whether nonlinear complexity provides additional direct forecast skill; gated recurrent unit (GRU) and long short-term memory (LSTM) networks provide a complementary test in which the temporal representation is learned internally. Delayed observations substantially improve forecasts over persistence and climatology at leads of up to six months, but increasing model complexity provides no systematic improvement. Historical recursive experiments favor a simple explicit SINDy recurrence and select shallow recurrent architectures, with no appreciable gain from learning the temporal representation internally. These results support a compact predictive representation of Niño-3.4 evolution in which the representation of past information is more consequential than model complexity. As a prospective application, the selected models are used to forecast the developing 2026 event beyond the last available observation and to compare its predicted evolution with completed historical El Niño events.
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