Inter-comparison and evaluation of Arctic sea ice type products

Type Article
Acceptance Date 2022-01 IN PRESS
Language English
Author(s) Ye YufangORCID1, Luo YanbingORCID1, Sun YanORCID1, Shokr Mohammed2, Aaboe SigneORCID3, Girard-Ardhuin FannyORCID4, Hui Fengming1, Cheng Xiao1, Chen Zhuoqi1
Affiliation(s) 1 : School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
2 : Meteorological Research Division, Environment and Climate Change Canada, Toronto M3H5T4, Canada
3 : Department of Remote Sensing and Data Management, Norwegian Meteorological Institute, Tromso, Norway
4 : Laboratoire d'Océanographie Physique et Spatiale (LOPS), Ifremer-Univ. Brest-CNRS-IRD, IUEM, F-29280, Plouzané, France
Source The Cryosphere (1994-0424) (Copernicus GmbH) In Press
DOI 10.5194/tc-2022-95
Abstract

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× 106 km2, while that in monthly data varies between 0.6× 103 km2 and 3.6× 106 km2. 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.

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