  Sea surface temperature (SST) forecasts help with managing the marine
ecosystem and the aquaculture impacted by anthropogenic climate change.
Numerical dynamical models are resource intensive for SST forecasts; machine
learning (ML) models could reduce high computational requirements and have been
in the focus of the research community recently. ML models normally require a
large amount of data for training. Environmental data are collected on
regularly-spaced grids, so early work mainly used grid-based deep learning (DL)
for prediction. However, both grid data and the corresponding DL approaches
have inherent problems. As geometric DL has emerged, graphs as a more
generalized data structure and graph neural networks (GNNs) have been
introduced to the spatiotemporal domains. In this work, we preliminarily
explored graph re-sampling and GNNs for global SST forecasts, and GNNs show
better one month ahead SST prediction than the persistence model in most oceans
in terms of root mean square errors.
