Interrogating Sea Ice Predictability With Gradients

• Published in: IEEE geoscience and remote sensing letters 2024, Vol.21, p.1-5
• Main Authors: Joakimsen, Harald L., Martinsen, Iver, Luppino, Luigi T., McDonald, Andrew, Hosking, Scott, Jenssen, Robert
• Format: Article
• Language: English
• Subjects: Analysis; Arctic; Climate; Climate models; Climatic analysis; Concentration gradient; Deep learning; Dynamic models; explainability; feature importance; gradients; IceNet; Input variables; Meteorology; Northern Hemisphere; Prediction models; Predictive models; Sea ice; sea ice concentration (SIC); Sea ice concentrations; Sea measurements
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2024.3366308

Predicting sea ice concentration (SIC) 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 SIC for the next 6 months over a spatial grid over the northern hemisphere. The model has proven to be particularly good at predicting extreme sea ice events compared with 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 letter, 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 SIC, linear trends, and seasonal components when making predictions. In our analysis, we identify which input features are most influential for the prediction and also which spatial location these measurements are particularly influential.