Incorporating Causality with Deep Learning in Predicting Short-Term and Seasonal Sea Ice

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https://ams.confex.com/ams/104ANNUAL/webprogram/Paper437004.html

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http://hdl.handle.net/11603/40702

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UMBC Student Collection
iHARP: NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions
UMBC Information Systems Department

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Author/Creator ORCID

https://orcid.org/0000-0002-6422-1895

Date

2024-01-31

Type of Work

conference papers and proceedings
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Program

Citation of Original Publication

Hossain, Emam. “Incorporating Causality with Deep Learning in Predicting Short-Term and Seasonal Sea Ice.” Paper presented at 104th AMS Annual Meeting. AMS, January 31, 2024. https://ams.confex.com/ams/104ANNUAL/webprogram/Paper437004.html.

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Subjects

UMBC Causal Artificial Intelligence Lab (CAIL)

Abstract

Arctic sea ice (ASI) is playing a pivotal role in keeping global warming under control. However, the recently amplified decreasing sea ice trend has become a major concern. Since satellites started monitoring the ASI in 1979, every decade the Arctic has lost 13.1% of sea ice and the Arctic’s September Sea Ice Extent (SIE) is now almost half compared to 1979. If this trend continues, it will be ice-free by 2050. Due to its wide-ranging effects, forecasting Arctic sea ice extent is of utmost importance. Accurate forecasts are essential to understanding the effects of global climate change, protecting the polar ecosystem, determining marine shipping routes, assisting indigenous communities, etc. In a world that is rapidly changing, foreseeing sea ice changes enables proactive environmental, economic, and social responses.