CoAuthors

Chaofang Zhao (Department of Marine Technology, Ocean University of China, Qingdao 266100, China, China); Christian Haas (Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany, Germany); Stefanie Arndt (Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany, Germany); Xiaochun Zhai (National Satellite Meteorological Center, Beijing 100081, China, China); Ge Chen (Department of Marine Technology, Ocean University of China, Qingdao 266100, China, )

Sea ice is an important factor in climate and weather systems, moderating the exchange of energy, momentum, and gases between the ocean and atmosphere. As an integral component of the sea ice system, the snow on the sea ice becomes a major factor affecting the variability of sea ice. In the Arctic, first-year ice (FYI) and multi-year ice (MYI) are the two most common ice types in the Arctic, effecting the climate change in the Arctic. In the Antarctic, the role of snow on sea ice is particularly important as the snow typically survive throughout the summer. Therefore, based on CFOSCAT data, we first classified the FYI and MYI over the Arctic region in the winter of 2019/2020 and 2020/2021 by combining the data of the scatterometer on the Chinese-French Oceanography Satellite (CFOSCAT) and the data of Advanced Microwave Scanning Radiometer-2 (AMSR-2). The CFOSCAT/AMSR ice type classification results are validated by using Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice type products and Canadian Ice Service (CIS) ice charts. The results showed that the overall MYI extent change trend retrieved from CFOSCAT/AMSR was consistent with the OSI SAF product with a correlation coefficient of 0.89 for winter of 2019/2020 and 0.88 for 2020/2021. By validating the results based on CIS charts, it was found that CFOSCAT/AMSR could identify more MYI pixels when the MYI concentration is relatively low. We also used CFOSCAT data only to retrieve ice type and found that the active and passive microwave data fusion could capture more MYI pixels located near the boundary of MYI and FYI main body, and the introduction of AMSR-2 data in ice type classification could reduce the error caused by the abnormal values of CFOSCAT parameters. In addition, we used CFOSCAT and C-band Advanced Scatterometer (ASCAT) data to observe the snowmelt in the Antarctic perennial sea ice region. The onset dates of two snowmelt stages from 2019/2020 to 2021/2022, namely pre-melt and snowmelt are retrieved. Results show that the average pre-melt and snowmelt onset dates for the pan-Antarctic perennial ice region are Nov 28 and Dec 15 respectively. Significant differences in retrieval results between Ku-band CFOSCAT and C-band ASCAT were also observed, with CFOSCAT detecting a snowmelt onset date of Dec 14, 2 days earlier than the ASCAT results, which proves that Ku-band scatterometer is more sensitive to snowmelt process on Antarctic sea ice. It suggests that combining multiband scatterometers can help to observe the variation of snow properties across the vertical snow column on perennial sea ice in Antarctic.