A data-driven U-Net model with residual structures and attention mechanisms for short-term prediction of Arctic sea ice concentration

Abstract. Sea ice is vital in the global climate system, ecological balance and polar navigation. Arctic sea ice concentration (SIC) exhibits significant spatial heterogeneity and complex evolutionary patterns. In response to address these challenges, this study proposes a predictive model named sea ice concentration U-Net (SICUNet). SICUNet is a data-driven U-Net model that integrates attention mechanisms and residual structures for short-term prediction of SIC in the Arctic region. The model enhances the perception of multi-scale features through spatial-channel attention mechanisms. Meanwhile, it integrates residual structures to alleviate the vanishing gradient and improve training stability. SICUNet is trained and validated using SIC data from 1988 to 2020 and evaluated during the testing phase using data from 2021 to 2024. To accurately capture seasonal variations in SIC, each year is divided into a melting season and a freezing season. Model training and prediction are conducted separately for each season. The model input is a 448×304 tensor with 7 channels built from daily SIC data over seven consecutive days. It then predicts SIC for the subsequent 7 days. SICUNet is trained and validated based on this input-output structure, and further applied to recursive prediction of SIC. During the 2021–2024 testing period, SICUNet effectively predicts SIC for the upcoming 7 days and maintains stable and accurate performance across multiple recursive steps. It outperforms traditional U-Net, U²Net and numerical simulation methods, showing robust results under extreme SIC conditions.