USING NEURAL NETWORKS TO MODEL SEA SURFACE TEMPERATURES
Here, we use a commercially-available machine-learning architecture generator, the NeuroGenetic Optimizer 3.6, to train and test regionally- and seasonally-resolved models for SST prediction. Our work parallels that of Kucera et al., 2005, but employs a narrower range of model architectures and is more regionally targeted. We use our results to investigate model performance in the tropical Atlantic, to compare our model results with geochemical reconstructions of core top SSTs using the Mg/Ca and Uk’37 geochemical proxies, and to predict Last Glacial Maximum SSTs. We also share progress in developing new open source PyTorch code to replicate the NGO’s artificial neural network design, with the goal of increasing the accessibility of neural network modeling techniques to the paleoclimate community.