Seasonal Prediction of Arctic Summer Sea Ice Concentration from a Partial Least Squares Regression Model
The past decade has witnessed a rapid decline in the Arctic sea ice and therefore has raised a rising demand for sea ice forecasts. In this study, based on an analysis of long-term Arctic summer sea ice concentration (SIC) and global sea surface temperature (SST) datasets, a physical–empirical (PE)...
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ftdoajarticles:oai:doaj.org/article:5c18a9923cb64888aac8410e19d5e61a 2024-01-07T09:40:55+01:00 Seasonal Prediction of Arctic Summer Sea Ice Concentration from a Partial Least Squares Regression Model Xiaochen Ye Zhiwei Wu 2021-02-01T00:00:00Z https://doi.org/10.3390/atmos12020230 https://doaj.org/article/5c18a9923cb64888aac8410e19d5e61a EN eng MDPI AG https://www.mdpi.com/2073-4433/12/2/230 https://doaj.org/toc/2073-4433 doi:10.3390/atmos12020230 2073-4433 https://doaj.org/article/5c18a9923cb64888aac8410e19d5e61a Atmosphere, Vol 12, Iss 2, p 230 (2021) Arctic summer sea ice variability seasonal prediction partial least squares regression (PLSR) model Meteorology. Climatology QC851-999 article 2021 ftdoajarticles https://doi.org/10.3390/atmos12020230 2023-12-10T01:46:52Z The past decade has witnessed a rapid decline in the Arctic sea ice and therefore has raised a rising demand for sea ice forecasts. In this study, based on an analysis of long-term Arctic summer sea ice concentration (SIC) and global sea surface temperature (SST) datasets, a physical–empirical (PE) partial least squares regression (PLSR) model is presented in order to predict the summer SIC variability around the key areas of the Arctic shipping route. First, the main SST modes closely associated with sea ice anomalies are found by the PLSR method. Then, a prediction model is reasonably established on the basis of these PLSR modes. We investigate the performance of the PE PLSR model by examining its reproducibility of the seasonal SIC variability. Results show that the proposed model turns out promising prediction reliability and accuracy for Arctic summer SIC change, thus providing a reference for the further study of Arctic SIC variability and global climate change. Article in Journal/Newspaper Arctic Climate change Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Atmosphere 12 2 230 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic summer sea ice variability seasonal prediction partial least squares regression (PLSR) model Meteorology. Climatology QC851-999 |
spellingShingle |
Arctic summer sea ice variability seasonal prediction partial least squares regression (PLSR) model Meteorology. Climatology QC851-999 Xiaochen Ye Zhiwei Wu Seasonal Prediction of Arctic Summer Sea Ice Concentration from a Partial Least Squares Regression Model |
topic_facet |
Arctic summer sea ice variability seasonal prediction partial least squares regression (PLSR) model Meteorology. Climatology QC851-999 |
description |
The past decade has witnessed a rapid decline in the Arctic sea ice and therefore has raised a rising demand for sea ice forecasts. In this study, based on an analysis of long-term Arctic summer sea ice concentration (SIC) and global sea surface temperature (SST) datasets, a physical–empirical (PE) partial least squares regression (PLSR) model is presented in order to predict the summer SIC variability around the key areas of the Arctic shipping route. First, the main SST modes closely associated with sea ice anomalies are found by the PLSR method. Then, a prediction model is reasonably established on the basis of these PLSR modes. We investigate the performance of the PE PLSR model by examining its reproducibility of the seasonal SIC variability. Results show that the proposed model turns out promising prediction reliability and accuracy for Arctic summer SIC change, thus providing a reference for the further study of Arctic SIC variability and global climate change. |
format |
Article in Journal/Newspaper |
author |
Xiaochen Ye Zhiwei Wu |
author_facet |
Xiaochen Ye Zhiwei Wu |
author_sort |
Xiaochen Ye |
title |
Seasonal Prediction of Arctic Summer Sea Ice Concentration from a Partial Least Squares Regression Model |
title_short |
Seasonal Prediction of Arctic Summer Sea Ice Concentration from a Partial Least Squares Regression Model |
title_full |
Seasonal Prediction of Arctic Summer Sea Ice Concentration from a Partial Least Squares Regression Model |
title_fullStr |
Seasonal Prediction of Arctic Summer Sea Ice Concentration from a Partial Least Squares Regression Model |
title_full_unstemmed |
Seasonal Prediction of Arctic Summer Sea Ice Concentration from a Partial Least Squares Regression Model |
title_sort |
seasonal prediction of arctic summer sea ice concentration from a partial least squares regression model |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/atmos12020230 https://doaj.org/article/5c18a9923cb64888aac8410e19d5e61a |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change Sea ice |
genre_facet |
Arctic Climate change Sea ice |
op_source |
Atmosphere, Vol 12, Iss 2, p 230 (2021) |
op_relation |
https://www.mdpi.com/2073-4433/12/2/230 https://doaj.org/toc/2073-4433 doi:10.3390/atmos12020230 2073-4433 https://doaj.org/article/5c18a9923cb64888aac8410e19d5e61a |
op_doi |
https://doi.org/10.3390/atmos12020230 |
container_title |
Atmosphere |
container_volume |
12 |
container_issue |
2 |
container_start_page |
230 |
_version_ |
1787421692280176640 |