Articles | Volume 16, issue 16
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

Data sets

Sea Ice Index, Version 3 Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, and A. K. Windnagel.

Boulder Monthly Means: Snowfall National Oceanic and Atmospheric Administration Physical Sciences Laboratory, Boulder Climate and Weather Information

JRA-55: Japanese 55-year Reanalysis, Monthly Means and Variances Japan Meteorological Agency

Model code and software

IceTFT: 1.0.0 Xiaodan Luo

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.