London - A novel Artificial Intelligence (AI) tool that can help accurately forecast Arctic sea ice conditions has been developed.
The AI system, IceNet, addresses the challenge of producing accurate Arctic sea ice forecasts for the season ahead -- something that has eluded scientists for decades.
A team of researchers led by British Antarctic Survey (BAS) and The Alan Turing Institute have described IceNet in the journal Nature Communications.
Sea ice, a vast layer of frozen sea water that appears at the North and South poles, is notoriously difficult to forecast because of its complex relationship with the atmosphere above and ocean below.
These accelerating changes have dramatic consequences for our climate, for Arctic ecosystems, and indigenous and local communities whose livelihoods are tied to the seasonal sea ice cycle.
IceNet -- the AI predictive tool -- is almost 95 per cent accurate in predicting whether sea ice will be present two months ahead.
"The Arctic is a region on the frontline of climate change and has seen substantial warming over the last 40 years. 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," said lead author Tom Andersson, Data Scientist at the BAS AI Lab.
"Our new sea ice forecasting framework fuses data from satellite sensors with the output of climate models in ways traditional systems simply couldn't achieve," said Dr Scott Hosking, senior Research Fellow at The Alan Turing Institute.
Unlike conventional forecasting systems that attempt to model the laws of physics directly, IceNet has been designed based on a concept called deep learning. Through this approach, the model 'learns' 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. This could operate as an early warning system for risks associated with rapid sea ice loss," Andersson said.