| Summary: | Ice sheets are an active component of Earth's climate system. Their topography influences atmospheric circulation and changes in their volume alters freshwater fluxes to the oceans, affecting ocean water masses, atmospheric carbon uptake, and global sea level. Sea-level rise has a marked societal impact, and thus ice sheet models are indispensable tools to predict it. To increase confidence on sea-level rise projections, it is necessary that ice sheet models accurately represent the relevant processes governing ice sheet dynamics. Given the fact that ice sheets respond to geological-scale changes in Earth's system, it is necessary that their performance is compared with in-situ data of past geological periods, which are discrete in space and time. One useful constraint used for validating model results is past ice surface elevation, which is reconstructed based on rock samples taken from nunataks (mountain summits that pierce through the ice sheet surface). However, two main problems prevent reliable comparisons of past ice surface elevations between model and empirical results. First, data-model comparisons are hindered by the fact that most large-scale ice sheet models capture neither the timing nor the magnitude of ice thinning reconstructed for the last deglaciation. Second, the complex subglacial topography of regions where nunataks are present is also reflected on the ice sheet surface, through pronounced elevation gradients. As a result, the choice of a reference point on the present-day ice sheet, which can be subjective, is a significant source of uncertainty when computing thickness-change estimates. In this thesis, I aim to reconstruct changes in ice sheet geometry over Dronning Maud Land (DML, East Antarctica) during periods that were warmer and colder than present, and the climate drivers behind such changes. I assess whether the comparison between empirical and model results can be improved by resolving local features in ice sheet models, and by using data and models in an iterative way (using data ...
|
|---|