Decadal Temperature Prediction via Chaotic Behavior Tracking

TL;DR

This paper introduces a novel chaotic behavior tracking method for decadal temperature prediction, effectively addressing error accumulation and capturing global temperature dynamics over ten-year periods.

Contribution

The paper presents a new probabilistic feedback-based prediction method that adapts to temperature dynamics, improving long-term climate prediction accuracy.

Findings

Accurately predicts global land-surface temperatures over a decade.

Effectively tracks and adapts to chaotic temperature variations.

Predicts temperature teleconnections across continents.

Abstract

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.