Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling
The Ice-sheet and Sea-level System Model (ISSM) provides numerical solutions for ice sheet dynamics using finite element and fine mesh adaption. However, considering ISSM is compatible only with central processing units (CPUs), it has limitations in economizing computational time to explore the link...
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Cambridge University Press
2025-01-01
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| Series: | Journal of Glaciology |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S0022143024000935/type/journal_article |
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| author | Younghyun Koo Maryam Rahnemoonfar |
| author_facet | Younghyun Koo Maryam Rahnemoonfar |
| author_sort | Younghyun Koo |
| collection | DOAJ |
| description | The Ice-sheet and Sea-level System Model (ISSM) provides numerical solutions for ice sheet dynamics using finite element and fine mesh adaption. However, considering ISSM is compatible only with central processing units (CPUs), it has limitations in economizing computational time to explore the linkage between climate forcings and ice dynamics. Although several deep learning emulators using graphic processing units (GPUs) have been proposed to accelerate ice sheet modeling, most of them rely on convolutional neural networks (CNNs) designed for regular grids. Since they are not appropriate for the irregular meshes of ISSM, we use a graph convolutional network (GCN) to replicate the adapted mesh structures of the ISSM. When applied to transient simulations of the Pine Island Glacier (PIG), Antarctica, the GCN successfully reproduces ice thickness and velocity with a correlation coefficient of approximately 0.997, outperforming non-graph models, including fully convolutional network (FCN) and multi-layer perceptron (MLP). Compared to the fixed-resolution approach of the FCN, the flexible-resolution structure of the GCN accurately captures detailed ice dynamics in fast-ice regions. By leveraging 60–100 times faster computational time of the GPU-based GCN emulator, we efficiently examine the impacts of basal melting rates on the ice sheet dynamics in the PIG. |
| format | Article |
| id | doaj-art-e2c9eb2c52044a23b069ad41da1a8f72 |
| institution | Kabale University |
| issn | 0022-1430 1727-5652 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Journal of Glaciology |
| spelling | doaj-art-e2c9eb2c52044a23b069ad41da1a8f722025-01-16T21:46:56ZengCambridge University PressJournal of Glaciology0022-14301727-56522025-01-017110.1017/jog.2024.93Graph convolutional network as a fast statistical emulator for numerical ice sheet modelingYounghyun Koo0https://orcid.org/0000-0001-9235-5009Maryam Rahnemoonfar1Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA, USADepartment of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA, USAThe Ice-sheet and Sea-level System Model (ISSM) provides numerical solutions for ice sheet dynamics using finite element and fine mesh adaption. However, considering ISSM is compatible only with central processing units (CPUs), it has limitations in economizing computational time to explore the linkage between climate forcings and ice dynamics. Although several deep learning emulators using graphic processing units (GPUs) have been proposed to accelerate ice sheet modeling, most of them rely on convolutional neural networks (CNNs) designed for regular grids. Since they are not appropriate for the irregular meshes of ISSM, we use a graph convolutional network (GCN) to replicate the adapted mesh structures of the ISSM. When applied to transient simulations of the Pine Island Glacier (PIG), Antarctica, the GCN successfully reproduces ice thickness and velocity with a correlation coefficient of approximately 0.997, outperforming non-graph models, including fully convolutional network (FCN) and multi-layer perceptron (MLP). Compared to the fixed-resolution approach of the FCN, the flexible-resolution structure of the GCN accurately captures detailed ice dynamics in fast-ice regions. By leveraging 60–100 times faster computational time of the GPU-based GCN emulator, we efficiently examine the impacts of basal melting rates on the ice sheet dynamics in the PIG.https://www.cambridge.org/core/product/identifier/S0022143024000935/type/journal_articleAntarctic glaciologyclimate changeglacier flowglacier modelingice dynamics |
| spellingShingle | Younghyun Koo Maryam Rahnemoonfar Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling Journal of Glaciology Antarctic glaciology climate change glacier flow glacier modeling ice dynamics |
| title | Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling |
| title_full | Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling |
| title_fullStr | Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling |
| title_full_unstemmed | Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling |
| title_short | Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling |
| title_sort | graph convolutional network as a fast statistical emulator for numerical ice sheet modeling |
| topic | Antarctic glaciology climate change glacier flow glacier modeling ice dynamics |
| url | https://www.cambridge.org/core/product/identifier/S0022143024000935/type/journal_article |
| work_keys_str_mv | AT younghyunkoo graphconvolutionalnetworkasafaststatisticalemulatorfornumericalicesheetmodeling AT maryamrahnemoonfar graphconvolutionalnetworkasafaststatisticalemulatorfornumericalicesheetmodeling |