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.