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Lauren Hoffman
Matthew R. Mazloff
Sarah T. Gille
Donata Giglio
Cecilia M. Bitz
Patrick Heimbach
, and
Kayli Matsuyoshi


Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

Significance Statement

We build and evaluate different machine learning (ML) models that make 1-day predictions of Arctic sea ice velocity using present-day wind velocity and previous-day ice concentration and ice velocity. We find that models that incorporate nonlinear relationships between inputs (a neural network) capture important information (i.e., have a higher correlation between observations and predictions than do linear and persistence models). This performance enhancement occurs primarily in deeper regions of the central Arctic where wind speed is the dominant predictor of ice motion. Understanding where these models benefit from increased complexity is important because future work will use ML to elucidate physically meaningful relationships within the data, looking at how the relationship between wind and ice velocity is changing as the ice melts.

Open access