Shiliang Wang

Arctic sea ice concentration forecasting has been limited by the difficulty of fusing multi-timescale information, with statistical models missing nonlinear relationships and numerical models depending on initial conditions. STICEnet replaces the U-Net encoder-decoder path with strictly unidirectional temporal causal and transposed convolutions and embeds channel and spatial attention in the skip connections, trained on NSIDC sea ice concentration and ERA5 atmospheric and oceanic variables. Across one- to six-month leads its RMSE, MAE and R2 all beat CNN, ConvLSTM, IceNet and MT-IceNet, reaching 7.51% RMSE and 0.925 R2 at month six.

Arctic sea ice concentration forecasting has been limited by the difficulty of fusing multi-timescale information, with statistical models missing nonlinear relationships and numerical models depending on initial conditions. STICEnet replaces the U-Net encoder-decoder path with strictly unidirectional temporal causal and transposed convolutions and embeds channel and spatial attention in the skip connections, trained on NSIDC sea ice concentration and ERA5 atmospheric and oceanic variables. Across one- to six-month leads its RMSE, MAE and R2 all beat CNN, ConvLSTM, IceNet and MT-IceNet, reaching 7.51% RMSE and 0.925 R2 at month six.