A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data

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.

Modeling ringed seal Pusa hispida habitat and lair emergence timing in the eastern Bering and Chukchi Seas

Ringed seals Pusa hispida are reliant on snow and sea ice for denning, and a better understanding of ringed seal habitat selection and timing of emergence from snow dens (also called lairs) is needed to quantify and predict effects of climate change in the Arctic. We used generalized additive models to assess relationships between ringed seal counts, from spring aerial surveys in the Bering Sea (2012 and 2013) and Chukchi Sea (2016), and spatiotemporal covariates including survey date, remotely sensed snow and sea-ice values, and short-term weather data. We produced separate models for total ringed seal counts and for pup counts within each region. Our models showed that in both areas, total ringed seal counts increased over the course of the spring, especially after 15 May, indicating emergence from lairs and/or the onset of basking behavior. For the more northerly Chukchi Sea, we found a substantial unimodal effect of snow melt progression and a positive effect of snow depth on total ringed seal counts. In contrast, Bering Sea total ringed seal counts and pup counts in both regions were affected much more strongly by date than by habitat variables. Overall, our findings demonstrate that snow depth and melt play an important role in the timing of ringed seal den emergence, particularly in the Chukchi Sea, and suggest that ringed seal denning may be affected by continued shifts in melt and snow depth associated with climate change.