SICNet - A Spatiotemporal Deep Neural Network for Arctic Sea Ice Forecasting.pdf
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 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 approaches to study sea ice variations, we propose SICNet – a UNet-based based spatiotemporal deep learning model for forecasting Arctic sea ice concentration (SIC) at greater lead times. The model uses an encoder-decoder architecture with skip connections to regenerate spatial maps at future timesteps. 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 our proposed model provides promising predictive performance for per-pixel SIC forecasting at long lead times. This will substantially improve our ability in predicting future Arctic sea ice changes, which is fundamental for forecasting transportation routes, resource development, coastal erosion, threats to Arctic coastal communities and wildlife. This poster was presented at the July 2022 ESIP Meeting, held in Pittsburgh, PA.