Upstream flow effects revealed in the EastGRIP ice core using Monte Carlo inversion of a two-dimensional ice-flow model

Abstract

The Northeast Greenland Ice Stream (NEGIS) is the largest active ice stream on the Greenland Ice Sheet (GrIS) and a crucial contributor to the ice-sheet mass balance. To investigate the ice-stream dynamics and to gain information about the past climate, a deep ice core is drilled in the upstream part of the NEGIS, termed the East Greenland Ice-core Project (EastGRIP). Upstream flow can introduce climatic bias into ice cores through the advection of ice deposited under different conditions further upstream. This is particularly true for EastGRIP due to its location inside an ice stream on the eastern flank of the GrIS. Understanding and ultimately correcting for such effects requires information on the atmospheric conditions at the time and location of snow deposition. We use a two-dimensional Dansgaard–Johnsen model to simulate ice flow along three approximated flow lines between the summit of the ice sheet (GRIP) and EastGRIP. Isochrones are traced in radio-echo-sounding images along these flow lines and dated with the GRIP and EastGRIP ice-core chronologies. The observed depth–age relationship constrains the Monte Carlo method which is used to determine unknown model parameters. We calculate backward-in-time particle trajectories to determine the source location of ice found in the EastGRIP ice core and present estimates of surface elevation and past accumulation rates at the deposition site. Our results indicate that increased snow accumulation with increasing upstream distance is predominantly responsible for the constant annual layer thicknesses observed in the upper part of the ice column at EastGRIP, and the inverted model parameters suggest that basal melting and sliding are important factors determining ice flow in the NEGIS. The results of this study form a basis for applying upstream corrections to a variety of ice-core measurements, and the inverted model parameters are useful constraints for more sophisticated modelling approaches in the future.