MT-IceNet -- A Spatial and Multi-Temporal Deep Learning Model for Arctic
  Sea Ice Forecasting
Sahara Ali Jianwu Wang
Arctic amplification has altered the climate patterns both regionally and
globally, resulting in more frequent and more intense extreme weather events in
the past few decades. The essential part of Arctic amplification is the
unprecedented sea ice loss as demonstrated by satellite observations.
Accurately forecasting Arctic sea ice from sub-seasonal to seasonal scales has
been a major research question 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 approaches to study sea ice variations, we propose
MT-IceNet - a UNet based spatial and multi-temporal (MT) deep learning model
for forecasting Arctic sea ice concentration (SIC). The model uses an
encoder-decoder architecture with skip connections and processes multi-temporal
input streams to regenerate spatial maps at future timesteps. Using bi-monthly
and monthly satellite retrieved sea ice data from NSIDC as well as atmospheric
and oceanic variables from ERA5 reanalysis product during 1979-2021, we show
that our proposed model provides promising predictive performance for per-pixel
SIC forecasting with up to 60% decrease in prediction error for a lead time of
6 months as compared to its state-of-the-art counterparts.