Preprints
https://doi.org/10.5194/tc-2022-95
https://doi.org/10.5194/tc-2022-95
 
19 May 2022
19 May 2022
Status: this preprint is currently under review for the journal TC.

Inter-comparison and evaluation of Arctic sea ice type products

Yufang Ye1, Yanbing Luo1, Yan Sun1, Mohammed Shokr2, Signe Aaboe3, Fanny Girard-Ardhuin4, Fengming Hui1, Xiao Cheng1, and Zhuoqi Chen1 Yufang Ye et al.
  • 1School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
  • 2Meteorological Research Division, Environment and Climate Change Canada, Toronto M3H5T4, Canada
  • 3Department of Remote Sensing and Data Management, Norwegian Meteorological Institute, Tromso, Norway
  • 4Laboratoire d'Océanographie Physique et Spatiale (LOPS), Ifremer-Univ. Brest-CNRS-IRD, IUEM, F-29280, Plouzané, France

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.

Yufang Ye et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-95', Anonymous Referee #1, 17 Jun 2022
  • RC2: 'Comment on tc-2022-95', Anonymous Referee #2, 21 Jun 2022
  • RC3: 'Comment on tc-2022-95', Anonymous Referee #3, 22 Jun 2022

Yufang Ye et al.

Yufang Ye et al.

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Short summary
Arctic sea ice type (SIT) variation is a sensitive indicator of climate change. This study gives systematic inter-comparison and evaluation of nine SIT products. Main results include: 1) Differences of various SIT products can be significant, with daily Arctic multiyear ice extent up to 4.5 × 106 km2; 2) Ku-band scatterometer SIT productsgenerally perform better; 3) Factors such as satellite inputs, classification methods, training datasets and post-processings highly impact their performances.