Ice Mass Balance in Liaodong Bay: Modeling and Observations

During the winters of 2009/2010 and 2020/2021, observations were carried out at an eastern port of Liaodong Bay to examine the variations in sea ice thickness and atmospheric conditions. The daily ice thickness (DIT) and the cumulative ice thickness (CIT) are the two main observation items related t...

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Published in:Water
Main Authors: Yuxian Ma, Dewen Ding, Ning Xu, Shuai Yuan, Wenqi Shi
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2023
Subjects:
Online Access:https://doi.org/10.3390/w15050943
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spelling ftmdpi:oai:mdpi.com:/2073-4441/15/5/943/ 2023-08-20T04:09:43+02:00 Ice Mass Balance in Liaodong Bay: Modeling and Observations Yuxian Ma Dewen Ding Ning Xu Shuai Yuan Wenqi Shi agris 2023-03-01 application/pdf https://doi.org/10.3390/w15050943 EN eng Multidisciplinary Digital Publishing Institute New Sensors, New Technologies and Machine Learning in Water Sciences https://dx.doi.org/10.3390/w15050943 https://creativecommons.org/licenses/by/4.0/ Water; Volume 15; Issue 5; Pages: 943 Liaodong Bay sea ice thickness Stefan’s law HIGHTSI oceanic heat flux Text 2023 ftmdpi https://doi.org/10.3390/w15050943 2023-08-01T09:03:23Z During the winters of 2009/2010 and 2020/2021, observations were carried out at an eastern port of Liaodong Bay to examine the variations in sea ice thickness and atmospheric conditions. The daily ice thickness (DIT) and the cumulative ice thickness (CIT) are the two main observation items related to the thickness of sea ice. For DIT, the sea ice thickness gradually decreases as the temperature increases, and the freezing rate a is 1.48 cm/(°C·d)1/2. For CIT, when the temperature is −12 °C, the maximum growth rate of ice thickness decreases from 3.5 cm/d to 1.5 cm/d as the ice thickness increases from 0 to 20 cm. The residual method was applied to calculate the oceanic heat flux, which is an important parameter of ice modeling, and both the analytic model (Stefan’s law) and numerical model (high-resolution thermodynamic snow-and-ice model) were utilized in this work. It was found that the accuracy of the simulation results was high when the growth coefficient of the analytic mode was 2.3 cm/(°C·d)1/2. With an oceanic heat flux of 2 W·m−2, the maximum error of the numerical model approached 60% in 2010 and 3.7% in 2021. However, using the oceanic heat flux calculated in this work, the maximum error can be significantly reduced to 4.2% in the winter of 2009/2010 and 1.5% in 2020/2021. Additionally, the oceanic heat flux in Liaodong Bay showed a decreasing trend with the increase in ice thickness and air temperature. Text Sea ice MDPI Open Access Publishing Water 15 5 943
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic Liaodong Bay
sea ice thickness
Stefan’s law
HIGHTSI
oceanic heat flux
spellingShingle Liaodong Bay
sea ice thickness
Stefan’s law
HIGHTSI
oceanic heat flux
Yuxian Ma
Dewen Ding
Ning Xu
Shuai Yuan
Wenqi Shi
Ice Mass Balance in Liaodong Bay: Modeling and Observations
topic_facet Liaodong Bay
sea ice thickness
Stefan’s law
HIGHTSI
oceanic heat flux
description During the winters of 2009/2010 and 2020/2021, observations were carried out at an eastern port of Liaodong Bay to examine the variations in sea ice thickness and atmospheric conditions. The daily ice thickness (DIT) and the cumulative ice thickness (CIT) are the two main observation items related to the thickness of sea ice. For DIT, the sea ice thickness gradually decreases as the temperature increases, and the freezing rate a is 1.48 cm/(°C·d)1/2. For CIT, when the temperature is −12 °C, the maximum growth rate of ice thickness decreases from 3.5 cm/d to 1.5 cm/d as the ice thickness increases from 0 to 20 cm. The residual method was applied to calculate the oceanic heat flux, which is an important parameter of ice modeling, and both the analytic model (Stefan’s law) and numerical model (high-resolution thermodynamic snow-and-ice model) were utilized in this work. It was found that the accuracy of the simulation results was high when the growth coefficient of the analytic mode was 2.3 cm/(°C·d)1/2. With an oceanic heat flux of 2 W·m−2, the maximum error of the numerical model approached 60% in 2010 and 3.7% in 2021. However, using the oceanic heat flux calculated in this work, the maximum error can be significantly reduced to 4.2% in the winter of 2009/2010 and 1.5% in 2020/2021. Additionally, the oceanic heat flux in Liaodong Bay showed a decreasing trend with the increase in ice thickness and air temperature.
format Text
author Yuxian Ma
Dewen Ding
Ning Xu
Shuai Yuan
Wenqi Shi
author_facet Yuxian Ma
Dewen Ding
Ning Xu
Shuai Yuan
Wenqi Shi
author_sort Yuxian Ma
title Ice Mass Balance in Liaodong Bay: Modeling and Observations
title_short Ice Mass Balance in Liaodong Bay: Modeling and Observations
title_full Ice Mass Balance in Liaodong Bay: Modeling and Observations
title_fullStr Ice Mass Balance in Liaodong Bay: Modeling and Observations
title_full_unstemmed Ice Mass Balance in Liaodong Bay: Modeling and Observations
title_sort ice mass balance in liaodong bay: modeling and observations
publisher Multidisciplinary Digital Publishing Institute
publishDate 2023
url https://doi.org/10.3390/w15050943
op_coverage agris
genre Sea ice
genre_facet Sea ice
op_source Water; Volume 15; Issue 5; Pages: 943
op_relation New Sensors, New Technologies and Machine Learning in Water Sciences
https://dx.doi.org/10.3390/w15050943
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/w15050943
container_title Water
container_volume 15
container_issue 5
container_start_page 943
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