How AI can help forecast how much Arctic sea ice will shrink
In the next week or so, the sea ice floating atop the Arctic Ocean will shrink to its smallest size this year, as summer-warmed waters eat away at the ice’s submerged edges.
Record lows for sea ice levels will probably not be broken this year, scientists say. In 2020, the ice covered 3.74 million square kilometers of the Arctic at its lowest point, coming nail-bitingly close to an all-time record low. Currently, sea ice is present in just under 5 million square kilometers of Arctic waters, putting it on track to become the 10th-lowest extent of sea ice in the area since satellite record keeping began in 1979. It’s an unexpected finish considering that in early summer, sea ice hit a record low for that time of year.
The surprise comes in part because the best current statistical- and physics-based forecasting tools can closely predict sea ice extent only a few weeks in advance, but the accuracy of long-range forecasts falters. Now, a new tool that uses artificial intelligence to create sea ice forecasts promises to boost their accuracy — and can do the analysis relatively quickly, researchers report August 26 in Nature Communications.
IceNet, a sea ice forecasting system developed by the British Antarctic Survey, or BAS, is “95 percent accurate in forecasting sea ice two months ahead — higher than the leading physics-based model SEAS5 — while running 2,000 times faster,” says Tom Andersson, a data scientist with BAS’s Artificial Intelligence lab. Whereas SEAS5 takes about six hours on a supercomputer to produce a forecast, IceNet can do the same in less than 10 seconds on a laptop. The system also shows a surprising ability to predict anomalous ice events — unusual highs or lows — up to four months in advance, Andersson and his colleagues found.
Tracking sea ice is crucial to keeping tabs on the impacts of climate change. While that’s more of a long game, the advanced notice provided by IceNet could have more immediate benefits, too. For instance, it could give scientists the lead time needed to assess, and plan for, the risks of Arctic fires or wildlife-human conflicts, and it could provide data that Indigenous communities need to make economic and environmental decisions.
Arctic sea ice extent has steadily declined in all seasons since satellite records began in 1979 (SN: 9/25/19). Scientists have been trying to improve sea ice forecasts for decades, but success has proved elusive. “Forecasting sea ice is really hard because sea ice interacts in complex ways with the atmosphere above and ocean below,” Andersson says.
Existing forecast tools put the laws of physics into computer code to predict how sea ice will change in the future. But partly due to uncertainties in the physical systems governing sea ice, these models struggle to produce accurate long-range forecasts.
Using a process called deep learning, Andersson and his colleagues loaded observational sea ice data from 1979 to 2011 and climate simulations covering 1850 to 2100 to train IceNet how to predict the state of future sea ice by processing the data from the past.
