Interrogating Sea Ice Predictability with Gradients

Predicting sea ice concentration is an important task in climate analysis. The recently proposed deep learning system IceNet is the state of the art sea ice prediction model. IceNet takes high-dimensional climate simulations and observational data as input features and forecasts sea ice concentration for the next six months over a spatial grid over the northern hemisphere. The model has proven to be particularly good at predicting extreme sea ice events compared to previous dynamical models, but lacks interpretability. In the original IceNet paper, a permute-and-predict approach was taken for assessing feature importance. However, this approach is not capable of revealing whether a feature contributes positively or negatively to the final prediction, nor can it reveal the importance of features over the spatial grid of predictions. In this paper, we take steps to instead interrogate the effect of the IceNet input feature with a gradient-based analysis, taking advantage of developments within the deep learning literature to open the so-called black box. Our analysis focuses on the unusually large sea ice extent event in September 2013 and indicates that IceNet places a strong emphasis on previous observations of sea ice concentration, linear trends, and seasonal components when making predictions. In our analysis, we identify which input features that are most influential for the prediction, and also at which spatial location these measurements are particularly influential.