Chapter 3 - AI for sea ice forecasting

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 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 demonstrate three approaches, namely, traditional machine learning, typical deep learning, and ensemble learning to predict monthly Pan-Arctic Sea ice extent up to 3 months ahead. Using 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 ensemble methods provide promising predictive performance with long lead time. This will substantially improve our ability in predicting the future Arctic Sea ice changes, which is fundamental for forecasting transporting routes, resource development, coastal erosion, and threats to Arctic coastal communities and wildlife.