Advancing Sea Ice Thickness Hindcast with Deep Learning: A WGAN-LSTM Approach

The thickness of the Arctic sea ice constitutes one of the crucial indicators of global climate change, and while deep learning has shown promise in predicting sea ice thickness (SIT), the field continues to grapple with the challenge of limited data availability. In this study, we introduce a Wasserstein Generative Adversarial Network–Long Short-Term Memory (WGAN-LSTM) model, which leverages the data generation capabilities of WGAN and the temporal prediction strengths of LSTM to perform single-step SIT prediction. During model training, the mean square error (MSE) and a novel comprehensive index, the Distance between Indices of Simulation and Observation (DISO), are used as two metrics of the loss function to compare. To thoroughly assess the model’s performance, we integrate the WGAN-LSTM model with the Monte Carlo (MC) dropout uncertainty estimation method, thereby validating the model’s enhanced generalization capabilities. Experimental results demonstrate that the WGAN-LSTM model, utilizing MSE and DISO as loss functions, improves comprehensive performance by 51.9% and 75.2%, respectively, compared to the traditional LSTM model. Furthermore, the MC estimates of the WGAN-LSTM model align with the distribution of actual observations. These findings indicate that the WGAN-LSTM model effectively captures nonlinear changes and surpasses the traditional LSTM model in prediction accuracy. The demonstrated effectiveness and reliability of the WGAN-LSTM model significantly advance short-term SIT prediction research in the Arctic region, particularly under conditions of data scarcity. Additionally, this model offers an innovative approach for identifying other physical features in the sea ice field based on sparse data.