Influence of oceanic boundary conditions in simulations of Antarctic climate and surface mass balance change during the coming century

This work reports on high-resolution (60 km) atmospheric general circulation model simulations of the Antarctic climate for the periods 1981-2000 and 2081-2100. Our analysis focuses on the surface mass balance change, one of the components of the total ice sheet mass balance, and its impact on global eustatic sea level. Contrary to previous simulations, in which we directly used sea surface boundary conditions produced by a coupled ocean-atmosphere model for the last decades of both centuries, we applied an anomaly method here in which the present-day simulations use observed sea surface conditions, while the simulations for the end of the 21st century use the change in sea surface conditions taken from the coupled simulations superimposed on the present-day observations. We show that the use of observed oceanic boundary conditions clearly improves the simulation of the present-day Antarctic climate, compared to model runs using boundary conditions from a coupled climate model. Moreover, although the spatial patterns of the simulated climate change are similar, the two methods yield significantly different estimates of the amplitude of the future climate and surface mass balance change over the Antarctic continent. These differences are of similar magnitude as the inter-model dispersion in the current IPCC exercise: Selecting a method for generating boundary conditions for a high- resolution model model may be just as important as selecting the climate model itself. Using the anomaly method, the simulated mean surface mass balance change over the grounded ice sheet from 1981-2000 to 2081-2100 is 43 mm water equivalent per year, corresponding to an eustatic sea-level decrease of 1.5 mm/year. A further result of this work is that future continental-mean surface mass balance changes are dominated by the coastal regions, and that high-resolution models, which better resolve coastal processes, tend to predict stronger precipitation changes than models with lower spatial resolution.