For generations, the inhabitants of the Arctic have counted on seasonal sea ice, which grows and retreats during the year. Polar bears and marine mammals rely on it as a hunting spot and a place to rest; Indigenous people fish from openings in the ice known as polynyas, and use well-known routes across the ice to travel from place to place. But the Arctic air and water has warmed three times faster than the rest of the planet since 1971, according to a May 2021 report by the Arctic Council, and this warming is causing the ice to expand and contract in unpredictable ways.
Some scientists and research firms are now deploying tools powered by artificial intelligence to provide more accurate and timely forecasts of what parts of the Arctic Ocean will be covered with ice, and when. AI algorithms complement existing models that use physics to understand what’s happening at the ocean’s surface, a dynamic zone where cold underwater currents meet harsh winds to create floating rafts of ice. This information is becoming increasingly valuable for tribal members in the Arctic, commercial fishers in places like Alaska, and global shipping companies interested in taking shortcuts through open patches of water.
Leslie Canavera, CEO of Polarctic, a Lorton, Virginia–based scientific consulting firm that has developed AI-based forecast models, says the uncertain pace of climate change means that existing models of sea ice are becoming less accurate. That’s because they are based on environmental processes that are quickly shifting.
“We don’t have a great understanding of climate change and what’s happening in the [Arctic] system,” says Canavera, who is a Yup’ik tribal member and grew up in Alaska. “We have statistical modeling, but then you’re looking at more of the averages. Then you have artificial intelligence, where it’s able to see the trends in the system and learn.”
Existing physics-based models capture hundreds of years of scientific records about ice conditions, current meteorological conditions, the speed and location of the polar jet stream, the amount of cloud cover, and ocean temperature. The models use that data to estimate future ice coverage. But it takes large amounts of computing power to crunch the numbers, and several hours or days to produce a forecast using conventional programs.
While AI also requires complex data and a lot of initial computing power, once an algorithm is trained on the right amount and kind of data, it can detect patterns in climate conditions more quickly than physics-based models, according to Thomas Anderson, a data scientist at the British Antarctic Survey who developed an AI ice forecast called IceNet. “AI methods can just run thousands of times faster, as we found in our model, IceNet,” Anderson says. “And they also learn automatically. AI is not smarter. It’s not replacing physics-based models. I think the future is leveraging both sources of information.”
Anderson and his colleagues published their new sea ice forecast model in August in the journal Nature Communications. IceNet uses a form of AI called deep learning (also used to automate detection of credit card fraud, operate self-driving cars, and run personal digital assistants) to train itself to provide a six-month forecast in each 25-kilometer square grid across the region, based on simulations of the Arctic climate between the years 1850 to 2100 and actual observational data recorded from 1979 to 2011. Once the model was trained and given current meteorological and ocean conditions, IceNet beat a leading physics-based model in making seasonal forecasts about the presence or absence of sea ice in each grid square, particularly for the summer season, when the ice goes through an annual retreat, according to the Nature study.