  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.
