标题：Investigating the sensitivity of numerical model simulations of the modern state of the Greenland ice-sheet and its future response to climate change
摘要：Ice thickness and bedrock topography are essential boundary conditions for numerical modelling of the evolution of the Greenland ice-sheet (GrIS). The datasets currently in use by the majority of GrIS modelling studies are over two decades old and based on data collected from the 1970s and 80s. We use a newer, high-resolution Digital Elevation Model of the GrIS and new temperature and precipitation forcings to drive the Glimmer ice-sheet model offline under steady state, present day climatic conditions. Comparisons are made of ice-sheet geometry between these new datasets and older ones used in the EISMINT-3 exercise. We find that changing to the newer bedrock and ice thickness makes the greatest difference to Greenland ice volume and ice surface extent. When all boundary conditions and forcings are simultaneously changed to the newer datasets the ice-sheet is 33% larger in volume compared with observation and 17% larger than that modelled by EISMINT-3. We performed a tuning exercise to improve the modelled present day ice-sheet. Several solutions were chosen in order to represent improvement in different aspects of the GrIS geometry: ice thickness, ice volume and ice surface extent. We applied these new parameter sets for Glimmer to several future climate scenarios where atmospheric CO2 concentration was elevated to 400, 560 and 1120 ppmv (compared with 280 ppmv in the control) using a fully coupled General Circulation Model. Collapse of the ice-sheet was found to occur between 400 and 560 ppmv, a threshold substantially lower than previously modelled using the standard EISMINT-3 setup. This work highlights the need to assess carefully boundary conditions and forcings required by ice-sheet models, particularly in terms of the abstractions required for large-scale ice-sheet models, and the implications that these can have on predictions of ice-sheet geometry under past and future climate scenarios.
