A new artificial intelligence (AI) tool is set to enable scientists to more accurately forecast Arctic sea ice conditions months into the future.
The improved predictions could underpin new early-warning systems that protect Arctic wildlife and coastal communities from the impacts of sea ice loss.
Published this week in Nature Communications, an international team of researchers led by British Antarctic Survey (BAS) and The Alan Turing Institute describe how the AI system, IceNet, addresses the challenge of producing accurate Arctic sea ice forecasts for the season ahead.
Sea ice that appears at the North and South poles is difficult to forecast because of its complex relationship with the atmosphere above and ocean below. The summer Arctic sea ice area has halved over the past four decades due to its sensitivity to increasing temperatures.
IceNet is reportedly almost 95% accurate in predicting whether sea ice will be present two months ahead – better than the leading physics-based model, according to lead author Tom Andersson, data scientist at the BAS AI Lab.
“The Arctic is a region on the frontline of climate change and has seen substantial warming over the last 40 years,” he said. “IceNet has the potential to fill an urgent gap in forecasting sea ice for Arctic sustainability efforts and runs thousands of times faster than traditional methods.”
Unlike conventional forecasting systems that attempt to model the laws of physics directly, IceNet’s design is based on deep learning, which enables it to ‘learn’ how sea ice changes from thousands of years of climate simulation data, along with decades of observational data to predict the extent of Arctic sea ice months into the future.
“Now we’ve demonstrated that AI can accurately forecast sea ice, our next goal is to develop a daily version of the model and have it running publicly in real-time, just like weather forecasts,” said Andersson. “This could operate as an early warning system for risks associated with rapid sea ice loss.”
