Combining modelling and field-based reconstructions of changing vertical ice extent across the Dronning Maud Land sector of the East Antarctic Ice Sheet.

Numerical ice sheet models constrained by theory and refined by comparisons with observational data are a central component of work to address the interactions between the cryosphere and changing climate, at a wide range of scales. Although there continue to be significant advances in modelling, major challenges still exist, in particular in terms of downscaling global climate model output to estimate regional and local climate patterns that are critical controls for the dynamics of glaciers and ice sheets. Ice sheet models are tested and refined by comparing model predictions of past ice geometries with field-based reconstructions from geological, geomorphological, and ice core data. However, on the East Antarctic Ice sheet, there is a critical gap in the empirical data required to reconstruct changes in ice sheet geometry in the Dronning Maud Land (DML) region. In addition, there is poor control on the regional climate history of the ice sheet margin, because ice core locations, where detailed reconstructions of climate history exist, are located on high inland domes. This leaves numerical models of regional glaciation history in this near-coastal area largely unconstrained. MAGIC-DML is an ongoing Swedish-US-Norwegian-German-UK collaboration with a focus on improving ice sheet models by combining advances in modeling with filling critical data gaps that exist in our knowledge of the timing and pattern of ice surface changes on the western Dronning Maud Land margin. A combination of geomorphological mapping using remote sensing data, field investigations, cosmogenic nuclide surface exposure dating, and numerical ice-sheet modelling are being used in an iterative manner to produce a comprehensive reconstruction of the glacial history of western Dronning Maud Land. We present an overview of the project, as well as results of the initial mapping and modelling that has been used to identify high potential sites for field sampling in 2016/17 and 2017/18.