Advancing Physical Interpretability of Arctic Sea Ice Dynamics through Automatic Feature Selection

Modelling Arctic sea ice dynamics has proven to be a successful application for machine learning, leveraging its ability to generate accurate and computationally efficient forecasts. Nevertheless, prevailing limitations lie in the need for physical interpretability and the inability to unveil the dynamics and interdependencies between relevant ice-related variables and their drivers. In this study, we provide a two-step framework designed to combine the high accuracy and computational efficiency characteristics of machine learning while ensuring high interpretability.

The first step of our framework entails time series clustering to identify subregions that are homogeneous with respect to the spatiotemporal variability in the considered variable and obtain the barycentric time series of each cluster. We then use an advanced feature selection algorithm, the Wrapper for Quasi Equally Informative Subset Selection, that identifies neural predictors, specifically Extreme Learning Machines, to forecast the future evolution of sea ice. It then provides the most relevant set of inputs necessary for accurately describing the evolution for each cluster.

Our investigation focuses on the monthly evolution of sea ice thickness and uses data from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS). Other PIOMAS variables (i.e., sea ice concentration, snow depth, sea surface temperature, and sea surface salinity) as well as observed discharge from five major Arctic rivers (i.e., Ob, Yenisey, and Lena in Asia, Mackenzie and Yukon in North America, provided by the Arctic Great Rivers Observatory discharge dataset) are considered as potential driving factors. 

Our results indicate the pivotal role of past sea ice thickness values, since the forthcoming state of sea ice seems to be influenced by both the current situation and historical trends and periodicity. Sea surface salinity in the open Arctic Ocean is highly persistent, and therefore is not used by the algorithm to explain the sea ice evolution. On the other hand, the Arctic rivers’ flows are more representative of the processes occurring in the clusters along the coast. Finally, the interaction between sea surface temperature and snow depth controls the interplay between ice formation and melting, and therefore plays a significant role in shaping the sea ice evolution in the short term.

Our framework aims to advance our comprehension of the complex physical processes governing sea ice thickness evolution in the Arctic region. Moreover, its effectiveness in uncovering sea ice related processes is expected to further improve with the inclusion of additional input variables and, possibly, of a longer data record.