Decadal temperature prediction provides crucial information for quantifying the expected effects of future climate changes and thus informs strategic planning and decision-making in various domains. However, such long-term predictions are extremely challenging, due to the chaotic nature of temperature variations. Moreover, the usefulness of existing simulation-based and machine learning-based methods for this task is limited because initial simulation or prediction errors increase exponentially over time. To address this challenging task, we devise a novel prediction method involving an information tracking mechanism that aims to track and adapt to changes in temperature dynamics during the prediction phase by providing probabilistic feedback on the prediction error of the next step based on the current prediction. We integrate this information tracking mechanism, which can be considered as a model calibrator, into the objective function of our method to obtain the corrections needed to avoid error accumulation. Our results show the ability of our method to accurately predict global land-surface temperatures over a decadal range. Furthermore, we demonstrate that our results are meaningful in a real-world context: the temperatures predicted using our method are consistent with and can be used to explain the well-known teleconnections within and between different continents.

翻译：年代际温度预测为量化未来气候变化的预期影响提供了关键信息，进而指导各领域的战略规划与决策制定。然而，由于温度变化的混沌特性，此类长期预测极具挑战性。此外，现有基于模拟和机器学习的方法在此任务中的有效性受到限制，因为初始模拟或预测误差会随时间呈指数级增长。为应对这一挑战，我们提出了一种新颖的预测方法，该方法包含信息追踪机制，旨在通过基于当前预测提供下一步预测误差的概率性反馈，在预测阶段追踪并适应温度动态的变化。我们将这一可视为模型校准器的信息追踪机制整合到所提方法的目标函数中，以获得避免误差累积所需的修正。结果表明，我们的方法能够准确预测全球陆地表面温度的年代际变化。此外，我们证明结果在实际情境中具有意义：使用我们的方法预测的温度与众所周知的大陆内部及跨大陆遥相关现象一致，并可用于解释这些现象。