Physics captured by data-based methods in El Niño prediction

On average once every four years, the Tropical Pacific warms considerably during events called El Nino, leading to weather disruptions over many regions on Earth. Recent machine-learning approaches to El Nino prediction, in particular, Convolutional Neural Networks (CNNs), have shown a surprisingly high skill at relatively long lead times. In an attempt to understand this high skill, we here use data from distorted physics simulations with the intermediate-complexity Zebiak-Cane model to determine what aspects of El Nino physics are represented in a specific CNN-based classification method. We find that the CNN can adequately correct for distortions in the ocean adjustment processes, but that the machine-learning method has far more trouble in dealing with distortions in upwelling feedback strength.