Predicting the Geothermal Heat Flux in Greenland: A Machine Learning Approach

Geothermal heat flux (GHF) is a crucial boundary condition for making accurate predictions of ice sheet mass loss, yet it is poorly known in Greenland due to inaccessibility of the bedrock. Here we use a machine learning algorithm on a large collection of relevant geologic features and global GHF me...

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Bibliographic Details
Published in:Geophysical Research Letters
Main Authors: Rezvanbehbahani, Soroush, Stearns, Leigh A., Kadivar, Amir, Walker, J. Douglas, van der Veen, Cornelis J.
Format: Article in Journal/Newspaper
Language:unknown
Published: American Geophysical Union 2018
Subjects:
Greenland ice sheet
Geothermal heat flux
Machine learning
Greenland
Jakobshavn Isbræ
Nioghalvfjerdsfjorden
Petermann Gletscher
Stripe
East Greenland
glacier
ice core
Ice Sheet
Jakobshavn
Jakobshavn isbræ
North Greenland
Petermann gletscher
Online Access:http://hdl.handle.net/1808/27385
https://doi.org/10.1002/2017GL075661
Description
Summary:Geothermal heat flux (GHF) is a crucial boundary condition for making accurate predictions of ice sheet mass loss, yet it is poorly known in Greenland due to inaccessibility of the bedrock. Here we use a machine learning algorithm on a large collection of relevant geologic features and global GHF measurements and produce a GHF map of Greenland that we argue is within ∼15% accuracy. The main features of our predicted GHF map include a large region with high GHF in central‐north Greenland surrounding the NorthGRIP ice core site, and hot spots in the Jakobshavn Isbræ catchment, upstream of Petermann Gletscher, and near the terminus of Nioghalvfjerdsfjorden glacier. Our model also captures the trajectory of Greenland movement over the Icelandic plume by predicting a stripe of elevated GHF in central‐east Greenland. Finally, we show that our model can produce substantially more accurate predictions if additional measurements of GHF in Greenland are provided.

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