SICNet_season V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by integrating sea ice thickness data

Abstract. The Arctic sea ice is suffering dramatic retreating in summer and fall, which has far-reaching consequences on the global climate and commercial activities. Accurate seasonal sea ice predictions are significant in inferring climate change and planning commercial activities. However, seasonally predicting the summer sea ice encounters a significant obstacle known as the spring predictability barrier (SPB): predictions made later than May demonstrate good skill in predicting summer sea ice, while predictions made on or earlier than May exhibit considerably lower skill. This study develops a transformer-based deep-learning model, SICNet_season (V1.0), to predict the Arctic sea ice concentration on a seasonal scale. Including spring sea ice thickness (SIT) data in the model significantly improves the prediction skill at the SPB point. A 20-year (2000–2019) testing demonstrates that the detrended anomaly correlation coefficient (ACC) of Sep. sea ice extent (sea ice concentration > 15 %) predicted by our model at May/Apr. is improved by 7.7 %/10.61 % over the ACC predicted by the state-of-the-art dynamic model from the European Centre for Medium-Range Weather Forecasts (ECMWF). Compared with the anomaly persistence benchmark, the mentioned improvement is 41.02 %/36.33 %. Our deep learning model significantly reduces prediction errors of Sep.'s sea ice concentration on seasonal scales compared to ECMWF and Persistence. The spring SIT data is key in optimizing the SPB, contributing to a more than 20 % ACC enhancement in Sep.'s SIE at four to five months lead predictions. Our model achieves good generalization in predicting the Sep. SIE of 2020–2023.