Abstract

Abstract. Over the last two decades, the Greenland ice sheet (GrIS) has been losing mass at an increasing rate, enhancing its contribution to sea-level rise (SLR). The recent increases in ice loss appear to be due to changes in both the surface mass balance of the ice sheet and ice discharge (ice flux to the ocean). Rapid ice flow directly affects the discharge, but also alters ice-sheet geometry and so affects climate and surface mass balance. Present-day ice-sheet models only represent rapid ice flow in an approximate fashion and, as a consequence, have never explicitly addressed the role of ice discharge on the total GrIS mass balance, especially at the scale of individual outlet glaciers. Here, we present a new-generation prognostic ice-sheet model which reproduces the current patterns of rapid ice flow. This requires three essential developments: the complete solution of the full system of equations governing ice deformation; a variable resolution unstructured mesh to resolve outlet glaciers and the use of inverse methods to better constrain poorly known parameters using observations. The modelled ice discharge is in good agreement with observations on the continental scale and for individual outlets. From this initial state, we investigate possible bounds for the next century ice-sheet mass loss. We run sensitivity experiments of the GrIS dynamical response to perturbations in climate and basal lubrication, assuming a fixed position of the marine termini. We find that increasing ablation tends to reduce outflow and thus decreases the ice-sheet imbalance. In our experiments, the GrIS initial mass (im)balance is preserved throughout the whole century in the absence of reinforced forcing, allowing us to estimate a lower bound of 75 mm for the GrIS contribution to SLR by 2100. In one experiment, we show that the current increase in the rate of ice loss can be reproduced and maintained throughout the whole century. However, this requires a very unlikely perturbation of basal lubrication. From this result we are able to estimate an upper bound of 140 mm from dynamics only for the GrIS contribution to SLR by 2100.

Highlights

  • The currently observed acceleration of mass loss from the Greenland ice sheet (GrIS, Rignot et al, 2011; Schrama and Wouters, 2011; van den Broeke et al, 2009; Wouters et al, 2008) is a concern when considering its possible contribution to future sea-level rise (SLR)

  • Several studies have revealed a dynamic response of the ice sheet, in which acceleration and thinning of most outlet glaciers are shown to be responsible for a substantial increase in ice discharge

  • We evaluate the results of the perturbations by considering ice-flow velocities (Fig. 1), the ice-sheet total mass balance (Fig. 8), discharge values from the main outlet glaciers (Table 3) and free-surface-elevation rate-of-change (Fig. 9)

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Summary

Introduction

The currently observed acceleration of mass loss from the Greenland ice sheet (GrIS, Rignot et al, 2011; Schrama and Wouters, 2011; van den Broeke et al, 2009; Wouters et al, 2008) is a concern when considering its possible contribution to future sea-level rise (SLR). 2010; Moon et al, 2012) These studies show a high spatial and temporal variability in glacier acceleration, suggesting that simple extrapolation of the recent observed trends cannot be justified, and realistic projections of the contribution of GrIS to SLR on decadal to century time scales must be derived from the forecasts of verified ice-flow models driven by the most reliable projections of climatic (atmosphere and ocean) forcing. For the construction of the initial state, we use two inverse methods (Arthern and Gudmundsson, 2010; Morlighem et al, 2010) to constrain the basal friction coefficient field from observed present-day geometry and surface velocities We discuss how these results can be interpreted in terms of possible bounds for the future ice-sheet mass loss and contribution to SLR by 2100

Equations
Mesh construction
Initial state
Inverse methods
Robin inverse method
Control inverse method
Regularisation
Minimisation
Results
Relaxation
7.33 Robin Inverse method Control inverse method
Set up
Conclusions

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