  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.
