Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 16
Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4677-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4677-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Viewed

Total article views: 1,719 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,181 484 54 1,719 38 36
  • HTML: 1,181
  • PDF: 484
  • XML: 54
  • Total: 1,719
  • BibTeX: 38
  • EndNote: 36
Views and downloads (calculated since 16 Jan 2023)
Cumulative views and downloads (calculated since 16 Jan 2023)

Viewed (geographical distribution)

Total article views: 1,719 (including HTML, PDF, and XML) Thereof 1,645 with geography defined and 74 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 07 Sep 2024
Download
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.
Read more