Artificial intelligence to help predict Arctic sea ice loss

Date:
August 26, 2021
Source:
British Antarctic Survey
Summary:
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.



FULL STORY ==========================================================================
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.


========================================================================== Published this week (Thursday 26 August) in the journal Nature
Communications, an international team of researchers led by British
Antarctic Survey (BAS) and The Alan Turing Institute describe how the
AI system, IceNet, addresses the challenge of producing accurate Arctic
sea ice forecasts for the season ahead - - something that has eluded
scientists for decades.

Sea ice, a vast layer of frozen sea water that appears at the North and
South poles, is notoriously difficult to forecast because of its complex relationship with the atmosphere above and ocean below. The sensitivity
of sea ice to increasing temperatures has caused the summer Arctic sea
ice area to halve over the past four decades, equivalent to the loss of
an area around 25 times the size of Great Britain. These accelerating
changes have dramatic consequences for our climate, for Arctic ecosystems,
and Indigenous and local communities whose livelihoods are tied to the
seasonal sea ice cycle.

IceNet, the AI predictive tool, is almost 95% accurate in predicting
whether sea ice will be present two months ahead -- better than the
leading physics- based model.

Lead author Tom Andersson, Data Scientist at the BAS AI Lab and funded
by The Alan Turing Institute, explains: "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-leader of the BAS AI Lab and Senior Research Fellow 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 forecasting systems that attempt to model the laws of physics directly, the authors designed IceNet based on a concept called
deep learning. Through this approach, the model 'learns' how sea ice
changes from thousands of years of climate simulation data, along with
decades of observational data to predict the extent of Arctic sea ice
months into 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." ========================================================================== Story Source: Materials provided by British_Antarctic_Survey. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Tom R. Andersson, J. Scott Hosking, Mari'a Pe'rez-Ortiz, Brooks
Paige,
Andrew Elliott, Chris Russell, Stephen Law, Daniel C. Jones, Jeremy
Wilkinson, Tony Phillips, James Byrne, Steffen Tietsche, Beena
Balan Sarojini, Eduardo Blanchard-Wrigglesworth, Yevgeny Aksenov,
Rod Downie, Emily Shuckburgh. Seasonal Arctic sea ice forecasting
with probabilistic deep learning. Nature Communications, 2021; 12
(1) DOI: 10.1038/s41467- 021-25257-4 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210826081706.htm

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