Abstract. Changes in Arctic sea ice affect atmospheric circulation,
ocean current, and polar ecosystems. There have been unprecedented decreases
in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC)
prediction model is proposed, with eight predictors using a deep-learning
approach, convolutional neural networks (CNNs). This monthly SIC prediction
model based on CNNs is shown to perform better predictions (mean absolute
error – MAE – of 2.28 %, anomaly correlation coefficient – ACC – of 0.98, root-mean-square error – RMSE – of 5.76 %, normalized RMSE – nRMSE – of 16.15 %,
and NSE – Nash–Sutcliffe efficiency – of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC
of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the
persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95,
RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast
validations. The spatio-temporal analysis also confirmed the superiority of
the CNN model. The CNN model showed good SIC prediction results in extreme
cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less
than 5.0 %. This study also examined the importance of the input
variables through a sensitivity analysis. In both the CNN and RF models, the
variables of past SICs were identified as the most sensitive factor in
predicting SICs. For both models, the SIC-related variables generally
contributed more to predict SICs over ice-covered areas, while other
meteorological and oceanographic variables were more sensitive to the
prediction of SICs in marginal ice zones. The proposed 1-month SIC
prediction model provides valuable information which can be used in various
applications, such as Arctic shipping-route planning, management of the fishing
industry, and long-term sea ice forecasting and dynamics.
Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks