Arctic and Antarctic Sea Ice Extent Forecasting using hybrid LSTM Techniques

Abstract

Recent changes in global climate patterns have triggered the melting of polar sea ice, especially in Arctic regions. A decrease in the extent of sea ice is observed at a much faster rate than previously expected. The Antarctic region, on the other hand, has shown a stable sea ice pattern throughout the last few decades. However, the southern polar region is not completely unaffected. Recent studies of the Bellingshausen and Amundsen Seas have shown a downward trend in sea ice. The extent of sea ice is crucial for regulating global climate patterns, ocean circulation and human activities, including shipping and fishing. Hence, forecasting sea ice extent is vital for global economy planning and climatology studies. In this paper, time-series forecasting of 5 Antarctic and Arctic regions is evaluated using a hybrid convolutional long short-term memory (ConvLSTM) and a bidirectional long short-term memory (LSTM) and compared with a standalone long short-term memory (LSTM). This study uses regional sea ice extent data rather than considering the extent across entire hemispheres. Evaluation metrics such as the root mean squared error (RMSE) and mean absolute error (MAE) are used to compare the performances of the models. With lower RMSE scores across all lead times, the proposed hybrid models show better performance in regional sea ice forecasting than does the standalone LSTM. The study also indicated that the climatic conditions of a particular region play a crucial role in forecasting efficiency, especially at longer lead times.