Short- and Mid-Term Forecasting of Pan-Arctic Sea Ice Volume Using Variational Mode Decomposition and Bidirectional Long Short-Term Memory

• Published in: Geosciences
• Main Authors: Aymane Ahajjam, Jaakko Putkonen, Timothy J. Pasch, Xun Zhu
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: pan-Arctic sea ice volume forecasting; multi-horizon forecasting; time-series forecasting; short-term forecasting; mid-term forecasting; deep learning; Geology; QE1-996.5; Arctic; Climate change; Global warming; Sea ice
• Online Access: https://doi.org/10.3390/geosciences13120370; https://doaj.org/article/5f6a0241f1c64dd6a7afd35dd7d60cb6

The well-documented decrease in the annual minimum Arctic sea ice extent over the past few decades is an alarming indicator of current climate change. However, much less is known about the thickness of the Arctic sea ice. Developing accurate forecasting models is critical to better predict its changes and monitor the impacts of global warming on the total Arctic sea ice volume (SIV). Significant improvements in forecasting performance are possible with the advances in signal processing and deep learning. Accordingly, here, we set out to utilize the recent advances in machine learning to develop non-physics-based techniques for forecasting the sea ice volume with low computational costs. In particular, this paper aims to provide a step-wise decision process required to develop a more accurate forecasting model over short- and mid-term horizons. This work integrates variational mode decomposition (VMD) and bidirectional long short-term memory (BiLSTM) for multi-input multi-output pan-Arctic SIV forecasting. Different experiments are conducted to identify the impact of several aspects, including multivariate inputs, signal decomposition, and deep learning, on forecasting performance. The empirical results indicate that (i) the proposed hybrid model is consistently effective in time-series processing and forecasting, with average improvements of up to 60% compared with the case of no decomposition and over 40% compared with other deep learning models in both forecasting horizons and seasons; (ii) the optimization of the VMD level is essential for optimal performance; and (iii) the use of the proposed technique with a divide-and-conquer strategy demonstrates superior forecasting performance.