From: University of Kansas
Published January 11, 2018 11:35 AM

Machine learning predicts new details of geothermal heat flux beneath the Greenland Ice Sheet

paper appearing in Geophysical Research Letters uses machine learning to craft an improved model for understanding geothermal heat flux — heat emanating from the Earth’s interior — below the Greenland Ice Sheet. It’s a research approach new to glaciology that could lead to more accurate predictions for ice-mass loss and global sea-level rise.

Greenland has an anomalously high heat flux in a relatively large northern region spreading from the interior to the east and west.

Southern Greenland has relatively low geothermal heat flux, corresponding with the extent of the North Atlantic Craton, a stable portion of one of the oldest extant continental crusts on the planet.

The research model predicts slightly elevated heat flux upstream of several fast-flowing glaciers in Greenland, including Jakobshavn Isbræ in the central-west, the fastest moving glacier on Earth.

Continue reading at University of Kansas

Image: This graphic shows geothermal heat flux predictions for Greenland.  CREDIT: University of Kansas

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