SICNet<sub>season</sub> V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data
<p>The Arctic sea ice suffers dramatic retreat in summer and fall, which has far-reaching consequences for the global climate and commercial activities. Accurate seasonal sea ice predictions significantly infer climate change and are crucial for planning commercial activities. However, seasona...
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Copernicus Publications
2025-05-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/18/2665/2025/gmd-18-2665-2025.pdf |
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| author | Y. Ren Y. Ren Y. Ren X. Li X. Li X. Li Y. Wang Y. Wang Y. Wang |
| author_facet | Y. Ren Y. Ren Y. Ren X. Li X. Li X. Li Y. Wang Y. Wang Y. Wang |
| author_sort | Y. Ren |
| collection | DOAJ |
| description | <p>The Arctic sea ice suffers dramatic retreat in summer and fall, which has far-reaching consequences for the global climate and commercial activities. Accurate seasonal sea ice predictions significantly infer climate change and are crucial for planning commercial activities. However, seasonal prediction of the summer sea ice encounters a significant obstacle known as the spring predictability barrier (SPB): predictions made later than the date of melt onset (roughly May) demonstrate good skill in predicting summer sea ice, while predictions made during or earlier than May exhibit considerably lower skill. This study develops a transformer-based deep learning model, SICNet<span class="inline-formula"><sub>season</sub></span> (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) test demonstrates that the detrended anomaly correlation coefficient (ACC) of September sea ice extent (sea ice concentration <span class="inline-formula">>15</span> %) predicted by our model during May and April is improved by 7.7 % and 10.61 %, respectively, compared to the ACC predicted by the state-of-the-art dynamic model SEAS5 from the European Centre for Medium-Range Weather Forecasts (ECMWF). Compared with the anomaly persistence benchmark, the mentioned improvement is 41.02 % and 36.33 %. Our deep learning model significantly reduces prediction errors in terms of September's sea ice concentration on seasonal scales compared to SEAS5 and the anomaly persistence model (Persistence). The spring SIT data are key in optimizing the predictions around the SPB, contributing to an enhancement in ACC of more than 20 % in September's sea ice extent (SIE) for 4- to 5-month-lead predictions. Our model achieves good generalizability in predicting the September SIE of 2020–2023.</p> |
| format | Article |
| id | doaj-art-578bc51e007446b6a3089872f072f3f9 |
| institution | OA Journals |
| issn | 1991-959X 1991-9603 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj-art-578bc51e007446b6a3089872f072f3f92025-08-20T01:49:16ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032025-05-01182665267810.5194/gmd-18-2665-2025SICNet<sub>season</sub> V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness dataY. Ren0Y. Ren1Y. Ren2X. Li3X. Li4X. Li5Y. Wang6Y. Wang7Y. Wang8Key Laboratory of Ocean Observation and Forecasting, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, ChinaKey Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, ChinaQingdao Key Laboratory of Artificial Intelligence Oceanography, Qingdao, ChinaKey Laboratory of Ocean Observation and Forecasting, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, ChinaKey Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, ChinaQingdao Key Laboratory of Artificial Intelligence Oceanography, Qingdao, ChinaKey Laboratory of Ocean Observation and Forecasting, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, ChinaKey Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, ChinaQingdao Key Laboratory of Artificial Intelligence Oceanography, Qingdao, China<p>The Arctic sea ice suffers dramatic retreat in summer and fall, which has far-reaching consequences for the global climate and commercial activities. Accurate seasonal sea ice predictions significantly infer climate change and are crucial for planning commercial activities. However, seasonal prediction of the summer sea ice encounters a significant obstacle known as the spring predictability barrier (SPB): predictions made later than the date of melt onset (roughly May) demonstrate good skill in predicting summer sea ice, while predictions made during or earlier than May exhibit considerably lower skill. This study develops a transformer-based deep learning model, SICNet<span class="inline-formula"><sub>season</sub></span> (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) test demonstrates that the detrended anomaly correlation coefficient (ACC) of September sea ice extent (sea ice concentration <span class="inline-formula">>15</span> %) predicted by our model during May and April is improved by 7.7 % and 10.61 %, respectively, compared to the ACC predicted by the state-of-the-art dynamic model SEAS5 from the European Centre for Medium-Range Weather Forecasts (ECMWF). Compared with the anomaly persistence benchmark, the mentioned improvement is 41.02 % and 36.33 %. Our deep learning model significantly reduces prediction errors in terms of September's sea ice concentration on seasonal scales compared to SEAS5 and the anomaly persistence model (Persistence). The spring SIT data are key in optimizing the predictions around the SPB, contributing to an enhancement in ACC of more than 20 % in September's sea ice extent (SIE) for 4- to 5-month-lead predictions. Our model achieves good generalizability in predicting the September SIE of 2020–2023.</p>https://gmd.copernicus.org/articles/18/2665/2025/gmd-18-2665-2025.pdf |
| spellingShingle | Y. Ren Y. Ren Y. Ren X. Li X. Li X. Li Y. Wang Y. Wang Y. Wang SICNet<sub>season</sub> V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data Geoscientific Model Development |
| title | SICNet<sub>season</sub> V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data |
| title_full | SICNet<sub>season</sub> V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data |
| title_fullStr | SICNet<sub>season</sub> V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data |
| title_full_unstemmed | SICNet<sub>season</sub> V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data |
| title_short | SICNet<sub>season</sub> V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data |
| title_sort | sicnet sub season sub v1 0 a transformer based deep learning model for seasonal arctic sea ice prediction by incorporating sea ice thickness data |
| url | https://gmd.copernicus.org/articles/18/2665/2025/gmd-18-2665-2025.pdf |
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