A new AI (artificial intelligence) 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.
An international team of researchers led by the British Antarctic Survey (BAS) and the Alan Turing Institute, published this week (Thursday, August 26) in Nature Communications, describes how the IceNet, artificial intelligence system is taking on the challenge of making accurate predictions of the arctic sea ice for the next season, something that scientists have missed for decades.
Sea ice, a huge layer of frozen seawater that occurs at the North and South Poles, is notoriously difficult to predict because of its complex relationship with the atmosphere above and the ocean below. The area of sea ice will be reduced by half in the last four decades, the equivalent of losing an area roughly 25 times the size of the UK. These accelerating changes have dramatic consequences for our climate, arctic ecosystems, and indigenous and local communities whose livelihoods are tied to the seasonal sea ice cycle.
IceNet, the artificial intelligence prediction tool, is nearly 95% accurate than the leading physics-based model in predicting whether sea ice will be two months earlier. Lead author Tom Andersson, a data scientist in the Artificial Intelligence Laboratory at BAS and funded by the Alan Turing Institute, states, “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.”
Dr. Scott Hosking, Principal Investigator, Co-Director of the Artificial Intelligence Laboratory at BAS and Principal Investigator at the Alan Turing Institute, says,”I’m excited to see how AI is making us rethink how we undertake environmental research. 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.”
Unlike conventional prediction systems that attempt to model the laws of physics directly, the authors developed IceNet based on a concept called deep learning. It is through this approach that the model“ learns ” how sea ice changes from thousands of years of climate simulation data along with decades of observational data to predict Arctic sea ice extent months in the future.
Tom Andersson concludes,
“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.”
