基于延迟观测的厄尔尼诺可预测性

中文摘要 AI 辅助

利用截至2026年7月的逐月Niño-3.4指数距平，本研究探究该指数的延迟观测中包含多少预测信息。岭回归用于识别具有信息价值的延迟项，多层感知机、非线性动力学稀疏识别（SINDy）模型则用于测试非线性复杂性是否能提供额外的直接预报技能；门控循环单元（GRU）和长短期记忆（LSTM）网络则提供补充测试，其中时间表示由模型内部学习。延迟观测在长达6个月的预报时效内，相较于持续性预报和气候态预报显著提升了预报效果，但增加模型复杂度并未带来系统性改进。历史递归实验倾向于采用简单的显式SINDy递推和浅层循环架构，从内部学习时间表示并未带来显著增益。这些结果支持Niño-3.4演变的紧凑预测表示，其中过去信息的表示比模型复杂度更为关键。作为潜在应用，所选模型被用于在最后一次可用观测之外预报正在发展的2026年厄尔尼诺事件，并将其预测演变与已完成的历史厄尔尼诺事件进行比较。

英文摘要

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.

发表机构

• University of Miami（迈阿密大学）
• Rosenstiel School of Marine, Atmospheric & Earth Science（罗森斯蒂尔海洋、大气与地球科学学院）
• Department of Atmospheric Sciences（大气科学系）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Submitted to JGR Machine Learning and Computation; comments welcome