Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, Los Alamos, NM, USA
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Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 204 (including HTML, PDF, and XML)
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198
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6
204
0
0
HTML: 198
PDF: 0
XML: 6
Total: 204
BibTeX: 0
EndNote: 0
Views and downloads (calculated since 09 Dec 2024)
Cumulative views and downloads
(calculated since 09 Dec 2024)
Total article views: 204 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
198
0
6
204
0
0
HTML: 198
PDF: 0
XML: 6
Total: 204
BibTeX: 0
EndNote: 0
Views and downloads (calculated since 09 Dec 2024)
Cumulative views and downloads
(calculated since 09 Dec 2024)
Viewed (geographical distribution)
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 204 (including HTML, PDF, and XML)
Thereof 204 with geography defined
and 0 with unknown origin.
Total article views: 204 (including HTML, PDF, and XML)
Thereof 204 with geography defined
and 0 with unknown origin.
Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train fast statistical models called Gaussian process (GP) emulators to accelerate subglacial drainage modelling by ~ 1000 times. We use the fast emulator predictions to show that three of the model parameters are responsible for > 90 % of the variance in model outputs. The fast GP emulators will enable future uncertainty quantification and calibration of these models.
Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train...