Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4677-2023
https://doi.org/10.5194/gmd-16-4677-2023
Model description paper
 | 
18 Aug 2023
Model description paper |  | 18 Aug 2023

IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning

Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang

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Latest update: 29 Feb 2024
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Short summary
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.