Current operational sea ice forecasting systems, based on deterministic coupled atmosphere-ice-ocean models, are often no better than simple statistical forecasts at seasonal lead times of two months and beyond 16, 17. Producing accurate Arctic sea ice forecasting systems has been a major scientific effort with fundamental challenges at play. Although the existence of such teleconnections are still in debate 14, improved forecasts of Arctic sea ice have the potential to improve predictions of mid-latitude weather 15. For example, it may provoke wetter European summers through a southerly perturbation of the jet stream 11, as well as extreme Northern Hemisphere winters by weakening the stratospheric polar vortex 12, 13. Evidence is mounting that Arctic sea ice loss influences weather and climate beyond the Arctic region. Such unprecedented sea ice loss has profound local and regional consequences: it is the greatest threat to polar bear populations 8 it has increased the intensity and frequency of algal blooms that propagate toxins throughout the food web 9 and it poses significant challenges for Indigenous Peoples, with impacts ranging from food security 9 to loss of culture 10.Īrctic sea ice is also a crucial component of the global climate system. Other studies put this date as early as the 2030s 7.
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Climate simulations project the Arctic to be ice free in the summer by 2050 6. This downward trend will continue, even in optimistic greenhouse gas emission reduction scenarios 5. Rising temperatures have played a key role in reducing Arctic sea ice, with September sea ice extent now around half that of 1979 when satellite measurements of the Arctic began 4. Near-surface air temperatures in the Arctic have increased at two to three times the rate of the global average, a phenomenon known as ‘Arctic amplification’, caused by a number of positive feedbacks 1, 2, 3. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We present a probabilistic, deep learning sea ice forecasting system, IceNet.
While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent.
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