Zhaoqing Dong

Polar sea ice profoundly affects atmospheric and oceanic circulation and plays a significant role in climate change. Sea ice concentration (SIC) is a key geophysical parameter used to quantify these changes. In this study, we determined SIC products for the Arctic and Antarctic from 2019 to 2021 using data from the Chinese marine satellite Haiyang 2B (HY-2B) with an improved bootstrap algorithm. Then the results were compared with similar operational SIC products and ship-based data. Our findings demonstrate the effectiveness of the improved algorithm for accurately determining SIC in polar regions. Additionally, the results of the study demonstrate that the SIC product obtained through the improved bootstrap algorithm has a high correlation with other similar SIC products. The daily average SIC of the different products showed similar inter-annual trends for both the Arctic and Antarctic regions. Comparison of the different SIC products showed that the Arctic BT-SMR SIC was slightly lower than the BT-SSMIS and BT-AMSR2 SIC products, while the difference between Antarctic SIC products was more pronounced. The lowest MAE was between the BT-SSMIS SIC and BT-SMR SIC in both regions, while the largest MAE was between the NT-SMR and BT-SMR in the Arctic, and between the NT-SSMIS and BT-SMR in the Antarctic. The SIE and SIA time series showed consistent trends, with a greater difference in SIA than SIC and a slight difference in SIA between the BT-AMSR2 and BT-SMR in the Arctic. Evaluation of the different SIC products using ship-based observation data showed a high correlation between the BT-SMR SIC and the ship-based SIC of approximately 0.85 in the Arctic and 0.88 in the Antarctic. The time series of dynamic tie-points better reflected the seasonal variation in sea ice radiation characteristics. This study lays the foundation for the release of long-term SIC product series from the Chinese autonomous HY-2B satellite, which will ensure the continuity of polar sea ice records over the past 40 years despite potential interruptions.

Arctic sea ice and snow affect the energy balance of the global climate system through the radiation budget. Accurate determination of the snow cover over Arctic sea ice is significant for the retrieval of the sea ice thickness (SIT). In this study, we developed a new snow depth retrieval method over Arctic sea ice with a long short-term memory (LSTM) deep learning algorithm based on Operation IceBridge (OIB) snow depth data and brightness temperature data of AMSR-2 passive microwave radiometers. We compared climatology products (modified W99 and AWI), altimeter products (Kwok) and microwave radiometer products (Bremen, Neural Network and LSTM). The climatology products and altimeter products are completely independent of the OIB data used for training, while microwave radiometer products are not completely independent of the OIB data. We also compared the SITs retrieved from the above different snow depth products based on Cryosat-2 radar altimeter data. First, the snow depth spatial patterns for all products are in broad agreement, but the temporal evolution patterns are distinct. Snow products of microwave radiometers, such as Bremen, Neural Network and LSTM snow depth products, show thicker snow in early winter with respect to the climatology snow depth products and the altimeter snow depth product, especially in the multiyear ice (MYI) region. In addition, the differences in all snow depth products are relatively large in the early winter and relatively small in spring. Compared with the OIB and IceBird observation data (April 2019), the snow depth retrieved by the LSTM algorithm is better than that retrieved by the other algorithms in terms of accuracy, with a correlation of 0.55 (0.90), a root mean square error (RMSE) of 0.06 m (0.05 m) and a mean absolute error (MAE) of 0.05 m (0.04 m). The spatial pattern and seasonal variation of the SITs retrieved from different snow depths are basically consistent. The total sea ice decreases first and then thickens as the seasons change. Compared with the OIB SIT in April 2019, the SIT retrieved by the LSTM snow depth is superior to that retrieved by the other SIT products in terms of accuracy, with the highest correlation of 0.46, the lowest RMSE of 0.59 m and the lowest MAE of 0.44 m. In general, it is promising to retrieve Arctic snow depth using the LSTM algorithm, but the retrieval of snow depth over MYI still needs to be verified with more measured data, especially in early winter.