  Sea ice motions play an important role in the polar climate system by
transporting pollutants, heat, water and salt as well as changing the ice
cover. Numerous physics-based models have been constructed to represent the sea
ice dynamical interaction with the atmosphere and ocean. In this study, we
propose a new data-driven deep-learning approach that utilizes a convolutional
neural network (CNN) to model how Arctic sea ice moves in response to surface
winds given its initial ice velocity and concentration a day earlier. Results
show that CNN computes the sea ice response with a correlation of 0.82 on
average with respect to reality, which surpasses a set of local point-wise
predictions and a leading thermodynamic-dynamical model, CICE5. The superior
predictive skill of CNN suggests the important role played by the connective
patterns of the predictors of the sea ice motion.
