Ice sheet properties inferred by combining numerical modeling and remote sensing data
Ice sheets are amongst the main contributors to sea level rise. They are dynamic systems; they gain mass by snow accumulation, and lose it by melting at the ice-ocean interface, surface melting and iceberg calving at the margins. Observations over the last three decades have shown that the Greenland...
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| Other Authors: | , , , |
| Format: | Doctoral or Postdoctoral Thesis |
| Language: | English |
| Published: |
HAL CCSD
2011
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| Subjects: | |
| Online Access: | https://tel.archives-ouvertes.fr/tel-00697004 https://tel.archives-ouvertes.fr/tel-00697004/document https://tel.archives-ouvertes.fr/tel-00697004/file/Morlighem_final.pdf |
| Summary: | Ice sheets are amongst the main contributors to sea level rise. They are dynamic systems; they gain mass by snow accumulation, and lose it by melting at the ice-ocean interface, surface melting and iceberg calving at the margins. Observations over the last three decades have shown that the Greenland and Antarctic ice sheets have been losing more mass than they gain. How the ice sheets respond to this negative mass imbalance has become today one of the most urgent questions in understanding the implications of global climate change. The Intergovernmental Panel on Climate Change (IPCC) has indeed identified the contribution of the ice sheets as a key uncertainty in sea level rise projections. Numerical modeling is the only effective way of addressing this problem. Yet, modeling ice sheet flow at the scale of Greenland and Antarctica remains scientifically and technically very challenging. This thesis focuses on two major aspects of improving ice sheet numerical models. The first consists of determining non-observable ice properties using inverse methods. Some parameters, such as basal friction or ice shelf hardness, are difficult to measure and must be inferred from remote sensing observations. Inversions are developed here for three ice flow models of increasing complexity: MacAyeal/Morland’s shelfy-stream model, Blatter/Pattyn’s higher order model and the full-Stokes model. The inferred parameters are then used to initialize large-scale ice sheet models and to determine the minimum level of complexity required to capture ice dynamics correctly. The second aspect addressed in this work is the improvement of dataset consistency for ice sheet modeling. Available datasets are often collected at different epochs and at varying spatial resolutions, making them not readily usable for numerical simulations. We devise here an algorithm based on the conservation of mass principle and inverse methods to construct ice thicknesses that are consistent with velocity measurements. This approach therefore avoids the artificial ... |
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