Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012
Extensive sea ice over Arctic regions is largely involved in heat, moisture, and momentum exchanges between the atmosphere and ocean. Some previous studies have been conducted to develop statistical models for the status of Arctic sea ice and showed considerable possibilities to explain the impacts...
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ftmdpi:oai:mdpi.com:/2072-4292/6/6/5520/ 2023-08-20T04:04:22+02:00 Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012 Jihye Ahn Sungwook Hong Jaeil Cho Yang-Won Lee Hosang Lee agris 2014-06-16 application/pdf https://doi.org/10.3390/rs6065520 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs6065520 https://creativecommons.org/licenses/by/3.0/ Remote Sensing; Volume 6; Issue 6; Pages: 5520-5540 sea ice concentration climate reanalysis statistical model time series Text 2014 ftmdpi https://doi.org/10.3390/rs6065520 2023-07-31T20:37:49Z Extensive sea ice over Arctic regions is largely involved in heat, moisture, and momentum exchanges between the atmosphere and ocean. Some previous studies have been conducted to develop statistical models for the status of Arctic sea ice and showed considerable possibilities to explain the impacts of climate changes on the sea ice extent. However, the statistical models require improvements to achieve better predictions by incorporating techniques that can deal with temporal variation of the relationships between sea ice concentration and climate factors. In this paper, we describe the statistical approaches by ordinary least squares (OLS) regression and a time-series method for modeling sea ice concentration using satellite imagery and climate reanalysis data for the Barents and Kara Seas during 1979–2012. The OLS regression model could summarize the overall climatological characteristics in the relationships between sea ice concentration and climate variables. We also introduced autoregressive integrated moving average (ARIMA) models because the sea ice concentration is such a long-range dataset that the relationships may not be explained by a single equation of the OLS regression. Temporally varying relationships between sea ice concentration and the climate factors such as skin temperature, sea surface temperature, total column liquid water, total column water vapor, instantaneous moisture flux, and low cloud cover were modeled by the ARIMA method, which considerably improved the prediction accuracies. Our method may also be worth consideration when forecasting future sea ice concentration by using the climate data provided by general circulation models (GCM). Text Arctic Sea ice MDPI Open Access Publishing Arctic Remote Sensing 6 6 5520 5540 |
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MDPI Open Access Publishing |
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English |
topic |
sea ice concentration climate reanalysis statistical model time series |
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sea ice concentration climate reanalysis statistical model time series Jihye Ahn Sungwook Hong Jaeil Cho Yang-Won Lee Hosang Lee Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012 |
topic_facet |
sea ice concentration climate reanalysis statistical model time series |
description |
Extensive sea ice over Arctic regions is largely involved in heat, moisture, and momentum exchanges between the atmosphere and ocean. Some previous studies have been conducted to develop statistical models for the status of Arctic sea ice and showed considerable possibilities to explain the impacts of climate changes on the sea ice extent. However, the statistical models require improvements to achieve better predictions by incorporating techniques that can deal with temporal variation of the relationships between sea ice concentration and climate factors. In this paper, we describe the statistical approaches by ordinary least squares (OLS) regression and a time-series method for modeling sea ice concentration using satellite imagery and climate reanalysis data for the Barents and Kara Seas during 1979–2012. The OLS regression model could summarize the overall climatological characteristics in the relationships between sea ice concentration and climate variables. We also introduced autoregressive integrated moving average (ARIMA) models because the sea ice concentration is such a long-range dataset that the relationships may not be explained by a single equation of the OLS regression. Temporally varying relationships between sea ice concentration and the climate factors such as skin temperature, sea surface temperature, total column liquid water, total column water vapor, instantaneous moisture flux, and low cloud cover were modeled by the ARIMA method, which considerably improved the prediction accuracies. Our method may also be worth consideration when forecasting future sea ice concentration by using the climate data provided by general circulation models (GCM). |
format |
Text |
author |
Jihye Ahn Sungwook Hong Jaeil Cho Yang-Won Lee Hosang Lee |
author_facet |
Jihye Ahn Sungwook Hong Jaeil Cho Yang-Won Lee Hosang Lee |
author_sort |
Jihye Ahn |
title |
Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012 |
title_short |
Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012 |
title_full |
Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012 |
title_fullStr |
Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012 |
title_full_unstemmed |
Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012 |
title_sort |
statistical modeling of sea ice concentration using satellite imagery and climate reanalysis data in the barents and kara seas, 1979–2012 |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2014 |
url |
https://doi.org/10.3390/rs6065520 |
op_coverage |
agris |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing; Volume 6; Issue 6; Pages: 5520-5540 |
op_relation |
https://dx.doi.org/10.3390/rs6065520 |
op_rights |
https://creativecommons.org/licenses/by/3.0/ |
op_doi |
https://doi.org/10.3390/rs6065520 |
container_title |
Remote Sensing |
container_volume |
6 |
container_issue |
6 |
container_start_page |
5520 |
op_container_end_page |
5540 |
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1774714764054757376 |