Ali, SaharaHuang, YiyiHuang, XinWang, Jianwu2022-09-262022-09-26"Ali, Sahara et al. Forecasting Sea Ice Concentrations using Attention-based Ensemble LSTM. ICML 2021 Workshop Tackling Climate Change with Machine Learning. https://www.climatechange.ai/papers/icml2021/50."http://hdl.handle.net/11603/25884ICML 2021 Workshop Tackling Climate Change with Machine LearningAccurately forecasting Arctic sea ice from sub-seasonal 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.en-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.decline in sea iceArctic sea ice forecastingattention-based LongShort Term Memory (LSTM) ensemble methodUMBC Big Data Analytics LabMoving Image