MT-IceNet -- A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting

TL;DR

This paper introduces MT-IceNet, a deep learning model that leverages multi-temporal satellite and reanalysis data to improve Arctic sea ice concentration forecasts, significantly reducing prediction errors over six months.

Contribution

The paper presents a novel UNet-based multi-temporal deep learning model specifically designed for Arctic sea ice forecasting, outperforming existing models in accuracy.

Findings

Up to 60% reduction in prediction error for 6-month lead forecasts.

Effective use of multi-temporal satellite and reanalysis data.

Promising results for pixel-level sea ice concentration prediction.

Abstract

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.