Sea Ice Extent Prediction with Machine Learning Methods and Subregional Analysis in the Arctic
The decline of sea ice in the Arctic region is a critical indicator of rapid global warming and can also influence the feedback processes in the Arctic, so the prediction of sea ice extent and thickness plays an important role in climate modeling and prediction. This paper uses machine learning meth...
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Online Access: | https://doi.org/10.3390/atmos14061023 |
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ftmdpi:oai:mdpi.com:/2073-4433/14/6/1023/ 2023-08-20T04:03:59+02:00 Sea Ice Extent Prediction with Machine Learning Methods and Subregional Analysis in the Arctic Siwen Chen Kehan Li Hongpeng Fu Ying Cheng Wu Yiyi Huang agris 2023-06-14 application/pdf https://doi.org/10.3390/atmos14061023 EN eng Multidisciplinary Digital Publishing Institute Climatology https://dx.doi.org/10.3390/atmos14061023 https://creativecommons.org/licenses/by/4.0/ Atmosphere; Volume 14; Issue 6; Pages: 1023 sea ice extent machine learning subregional analysis climate modeling global warming Text 2023 ftmdpi https://doi.org/10.3390/atmos14061023 2023-08-01T10:28:42Z The decline of sea ice in the Arctic region is a critical indicator of rapid global warming and can also influence the feedback processes in the Arctic, so the prediction of sea ice extent and thickness plays an important role in climate modeling and prediction. This paper uses machine learning methods to predict the sea ice extent, and by adjusting the methods and factors, which include the climate variables, the past sea ice extent, and the simple linear-regression-simulated sea ice extent, then we found the best combination to give the result with the highest R2 score. We noticed that with longer periods of past sea ice extent data and shorter periods of climate data, the results appeared to be better. This might be related to the difference in climate and ocean memory. The sub-region sea ice extent prediction shows that the regions with whole-year ice cover are easier to predict and that those regions with sudden weather changes and significant seasonal variability appear to have lower R2 scores in the sea ice extent prediction. Text Arctic Global warming Sea ice MDPI Open Access Publishing Arctic Atmosphere 14 6 1023 |
institution |
Open Polar |
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MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
sea ice extent machine learning subregional analysis climate modeling global warming |
spellingShingle |
sea ice extent machine learning subregional analysis climate modeling global warming Siwen Chen Kehan Li Hongpeng Fu Ying Cheng Wu Yiyi Huang Sea Ice Extent Prediction with Machine Learning Methods and Subregional Analysis in the Arctic |
topic_facet |
sea ice extent machine learning subregional analysis climate modeling global warming |
description |
The decline of sea ice in the Arctic region is a critical indicator of rapid global warming and can also influence the feedback processes in the Arctic, so the prediction of sea ice extent and thickness plays an important role in climate modeling and prediction. This paper uses machine learning methods to predict the sea ice extent, and by adjusting the methods and factors, which include the climate variables, the past sea ice extent, and the simple linear-regression-simulated sea ice extent, then we found the best combination to give the result with the highest R2 score. We noticed that with longer periods of past sea ice extent data and shorter periods of climate data, the results appeared to be better. This might be related to the difference in climate and ocean memory. The sub-region sea ice extent prediction shows that the regions with whole-year ice cover are easier to predict and that those regions with sudden weather changes and significant seasonal variability appear to have lower R2 scores in the sea ice extent prediction. |
format |
Text |
author |
Siwen Chen Kehan Li Hongpeng Fu Ying Cheng Wu Yiyi Huang |
author_facet |
Siwen Chen Kehan Li Hongpeng Fu Ying Cheng Wu Yiyi Huang |
author_sort |
Siwen Chen |
title |
Sea Ice Extent Prediction with Machine Learning Methods and Subregional Analysis in the Arctic |
title_short |
Sea Ice Extent Prediction with Machine Learning Methods and Subregional Analysis in the Arctic |
title_full |
Sea Ice Extent Prediction with Machine Learning Methods and Subregional Analysis in the Arctic |
title_fullStr |
Sea Ice Extent Prediction with Machine Learning Methods and Subregional Analysis in the Arctic |
title_full_unstemmed |
Sea Ice Extent Prediction with Machine Learning Methods and Subregional Analysis in the Arctic |
title_sort |
sea ice extent prediction with machine learning methods and subregional analysis in the arctic |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/atmos14061023 |
op_coverage |
agris |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Global warming Sea ice |
genre_facet |
Arctic Global warming Sea ice |
op_source |
Atmosphere; Volume 14; Issue 6; Pages: 1023 |
op_relation |
Climatology https://dx.doi.org/10.3390/atmos14061023 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/atmos14061023 |
container_title |
Atmosphere |
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14 |
container_issue |
6 |
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1023 |
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1774714427111636992 |