Sea Ice Forecasting using Attention-based Ensemble LSTM

• Main Authors: Ali, Sahara, Huang, Yiyi, Huang, Xin, Wang, Jianwu
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2021
• Subjects: Atmospheric and Oceanic Physics physics.ao-ph; Artificial Intelligence cs.AI; Machine Learning cs.LG; FOS Physical sciences; FOS Computer and information sciences; Arctic; Climate change; Sea ice
• Online Access: https://dx.doi.org/10.48550/arxiv.2108.00853; https://arxiv.org/abs/2108.00853

Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables from ERA5 reanalysis product for 39 years, we show that our multi-temporal ensemble method outperforms several baseline and recently proposed deep learning models. This will substantially improve our ability in predicting future Arctic sea ice changes, which is fundamental for forecasting transporting routes, resource development, coastal erosion, threats to Arctic coastal communities and wildlife. : Accepted by the Tackling Climate Change with Machine Learning Workshop at the 2021 International Conference on Machine Learning (ICML 2021)