Empirical dynamical modeling for studies of seasonal-to-interannual prediction and predictability

In the past few years, deep learning has revolutionized weather forecasting and can now outperform traditional state-of-the-art numerical weather prediction models. These advancements were made possible because of the availability of millions of data samples from reanalysis products on hourly resolution, which allows the training of large neural networks. While the more limited availability of independent samples on climate time scales has so far proven to be more of a challenge for deep learning methods, data-driven approaches a...