Inter-comparison and evaluation of Arctic sea ice type products
Arctic sea ice type (SIT) variation is a sensitive indicator of climate change. However, systematic inter-comparison and analysis for SIT products are lacking. This study analyzed nine SIT products from five SIT retrieval approaches covering the winters from 1999 to 2018. These SIT products were int...
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ftcopernicus:oai:publications.copernicus.org:tcd102910 2023-05-15T14:55:52+02:00 Inter-comparison and evaluation of Arctic sea ice type products Ye, Yufang Luo, Yanbing Sun, Yan Shokr, Mohammed Aaboe, Signe Girard-Ardhuin, Fanny Hui, Fengming Cheng, Xiao Chen, Zhuoqi 2022-05-19 application/pdf https://doi.org/10.5194/tc-2022-95 https://tc.copernicus.org/preprints/tc-2022-95/ eng eng doi:10.5194/tc-2022-95 https://tc.copernicus.org/preprints/tc-2022-95/ eISSN: 1994-0424 Text 2022 ftcopernicus https://doi.org/10.5194/tc-2022-95 2022-05-23T16:22:30Z Arctic sea ice type (SIT) variation is a sensitive indicator of climate change. However, systematic inter-comparison and analysis for SIT products are lacking. This study analyzed nine SIT products from five SIT retrieval approaches covering the winters from 1999 to 2018. These SIT products were inter-compared towards sea ice age product and evaluated with Synthetic Aperture Radar images. Among all, the largest daily Arctic multiyear ice (MYI) extent difference reaches 4.5× 10 6 km 2 , while that in monthly data varies between 0.6× 10 3 km 2 and 3.6× 10 6 km 2 . Overall speaking, the Zhang- and KNMI-SIT products based on Ku-band scatterometer perform the best. However, when using C-band scatterometer, KNMI-SIT shows overestimation of MYI in the early winter, and Zhang-SIT shows underestimation with anomalous fluctuations. C3S- and OSISAF-SIT show large daily variability. IFREMER-SIT generally underestimates MYI. Factors that could impact their performances are analyzed and summarized: (1) Ku-band scatterometer generally performs better than C-band scatterometer on SIT discrimination, while the latter sometimes identifies first-year ice (FYI) more accurately, especially when FYI and MYI are highly mixed. (2) Simple combination of scatterometer and radiometer data is not always beneficial, e.g. under circumstances with strong atmospheric influence on microwave signatures. (3) The representativeness of training data and efficiency of classification are crucial for SIT classification. Spatial and temporal variation of characteristic training dataset should be well accounted in the SIT method. Additionally, the change of separation pattern of microwave data could influence the adaptive classification method. (4) Post-processing corrections play important roles and should be considered with caution. Text Arctic Climate change Sea ice Copernicus Publications: E-Journals Arctic |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
Arctic sea ice type (SIT) variation is a sensitive indicator of climate change. However, systematic inter-comparison and analysis for SIT products are lacking. This study analyzed nine SIT products from five SIT retrieval approaches covering the winters from 1999 to 2018. These SIT products were inter-compared towards sea ice age product and evaluated with Synthetic Aperture Radar images. Among all, the largest daily Arctic multiyear ice (MYI) extent difference reaches 4.5× 10 6 km 2 , while that in monthly data varies between 0.6× 10 3 km 2 and 3.6× 10 6 km 2 . Overall speaking, the Zhang- and KNMI-SIT products based on Ku-band scatterometer perform the best. However, when using C-band scatterometer, KNMI-SIT shows overestimation of MYI in the early winter, and Zhang-SIT shows underestimation with anomalous fluctuations. C3S- and OSISAF-SIT show large daily variability. IFREMER-SIT generally underestimates MYI. Factors that could impact their performances are analyzed and summarized: (1) Ku-band scatterometer generally performs better than C-band scatterometer on SIT discrimination, while the latter sometimes identifies first-year ice (FYI) more accurately, especially when FYI and MYI are highly mixed. (2) Simple combination of scatterometer and radiometer data is not always beneficial, e.g. under circumstances with strong atmospheric influence on microwave signatures. (3) The representativeness of training data and efficiency of classification are crucial for SIT classification. Spatial and temporal variation of characteristic training dataset should be well accounted in the SIT method. Additionally, the change of separation pattern of microwave data could influence the adaptive classification method. (4) Post-processing corrections play important roles and should be considered with caution. |
format |
Text |
author |
Ye, Yufang Luo, Yanbing Sun, Yan Shokr, Mohammed Aaboe, Signe Girard-Ardhuin, Fanny Hui, Fengming Cheng, Xiao Chen, Zhuoqi |
spellingShingle |
Ye, Yufang Luo, Yanbing Sun, Yan Shokr, Mohammed Aaboe, Signe Girard-Ardhuin, Fanny Hui, Fengming Cheng, Xiao Chen, Zhuoqi Inter-comparison and evaluation of Arctic sea ice type products |
author_facet |
Ye, Yufang Luo, Yanbing Sun, Yan Shokr, Mohammed Aaboe, Signe Girard-Ardhuin, Fanny Hui, Fengming Cheng, Xiao Chen, Zhuoqi |
author_sort |
Ye, Yufang |
title |
Inter-comparison and evaluation of Arctic sea ice type products |
title_short |
Inter-comparison and evaluation of Arctic sea ice type products |
title_full |
Inter-comparison and evaluation of Arctic sea ice type products |
title_fullStr |
Inter-comparison and evaluation of Arctic sea ice type products |
title_full_unstemmed |
Inter-comparison and evaluation of Arctic sea ice type products |
title_sort |
inter-comparison and evaluation of arctic sea ice type products |
publishDate |
2022 |
url |
https://doi.org/10.5194/tc-2022-95 https://tc.copernicus.org/preprints/tc-2022-95/ |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change Sea ice |
genre_facet |
Arctic Climate change Sea ice |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-2022-95 https://tc.copernicus.org/preprints/tc-2022-95/ |
op_doi |
https://doi.org/10.5194/tc-2022-95 |
_version_ |
1766327885599080448 |