Among the most relevant processes in the Earth system for human habitability are quasi-periodic, ocean-driven multi-year events whose dynamics are currently incompletely characterized by physical models, and hence poorly predictable. This work aims at showing how 1) data-driven, stochastic machine learning approaches provide an affordable yet flexible means to forecast these processes; 2) the associated uncertainty can be properly calibrated with fast ensemble-based approaches. While the methodology introduced and discussed in this work pertains to synoptic scale events, the principle of augmenting incomplete or highly sensitive physical systems with data-driven models to improve predictability is far more general and can be extended to environmental problems of any scale in time or space.

翻译：在地球系统中，对人类宜居性影响最显著的过程之一是准周期性的、由海洋驱动的多年事件，其动力学目前尚未被物理模型完全表征，因此可预测性较差。本文旨在说明：1）数据驱动的随机机器学习方法为预测这些过程提供了一种经济且灵活的手段；2）通过基于快速集成的方法可对相关不确定性进行适当校准。本文介绍并讨论的方法论涉及天气尺度事件，但通过数据驱动模型增强不完整或高敏感物理系统以提升可预测性的原理具有更广泛的普适性，可推广至任何时空尺度的环境问题。