Sea Ice Forecasting using Attention-based Ensemble LSTM
This work addresses the need for accurate sea ice predictions to support forecasting of transport routes, resource development, and environmental impacts, representing an incremental improvement in data-driven methods.
The paper tackles the problem of forecasting Arctic sea ice extent up to one month ahead by proposing an attention-based LSTM ensemble method, which outperforms baseline and recent deep learning models using satellite and reanalysis data over 39 years.
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.