Search

During the Quaternary, ice sheets experienced several retreat-advanced cycles, strongly influencing climate patterns. In order to properly simulate these phenomena, it is preferable to use physics-based models instead of parameterizations to estimate surface mass balance (SMB) which has a strong influence on the ice sheet evolution. To further investigate the potential of these SMB models, this work evaluates BESSI (BErgen Snow Simulator), a multi-layer snow model with high computational efficiency, as an alternative to providing SMB for paleo studies. First, we validate the snow model using the regional climate model MAR (Modεave;le Atmosphérique Régional) as forcing and reference for the present-day climate over Greenland and Antarctic Ice Sheets. The evolution of SMB over the Last Interglacial period (LIG) (130–116 kaBP) is computed by forcing BESSI with transient climate forcing obtained from an Earth system model i LOVECLIM for both ice sheets. For present-day climate conditions, BESSI exhibits good performance compared to MAR despite a much simpler model set-up. The model also captures well the variation of SMB and its components during the LIG. Compared to the current simple melt estimation scheme of i LOVECLIM (ITM), BESSI is able to capture different SMB patterns for two particular ice sheet climate conditions thanks to its higher physical constraints while ITM displays a strong sensitivity to its parameters and input fields (temperature). The findings suggest that BESSI can provide more reliable SMB estimations for the i LOVECLIM framework to improve the model simulations of the ice sheet evolution and interactions with climate. [ABSTRACT FROM AUTHOR]

Polar amplification will result in amplified temperature changes in the Arctic with respect to the rest of the globe, making the Greenland ice sheet particularly vulnerable to global warming. While the ice sheet has been showing an increased mass loss in the past decades, its contribution to global sea level rise in the future is of primary importance since it is at present the largest single-source contribution after the thermosteric contribution. The question of the fate of the Greenland and Antarctic ice sheets for the next century has recently gathered various ice sheet models in a common framework within the Ice Sheet Model Intercomparison Project for the Coupled Model Intercomparison Project – phase 6 (ISMIP6). While in a companion paper we present the GRISLI-LSCE (Grenoble Ice Sheet and Land Ice model of the Laboratoire des Sciences du Climat et de l'Environnement) contribution to ISMIP6-Antarctica, we present here the GRISLI-LSCE contribution to ISMIP6-Greenland. We show an important spread in the simulated Greenland ice loss in the future depending on the climate forcing used. The contribution of the ice sheet to global sea level rise in 2100 can thus be from as low as 20 mm sea level equivalent (SLE) to as high as 160 mm SLE. Amongst the models tested in ISMIP6, the Coupled Model Intercomparison Project – phase 6 (CMIP6) models produce larger ice sheet retreat than their CMIP5 counterparts. Low-emission scenarios in the future drastically reduce the ice mass loss. The oceanic forcing contributes to about 10 mm SLE in 2100 in our simulations. In addition, the dynamical contribution to ice thickness change is small compared to the impact of surface mass balance. This suggests that mass loss is mostly driven by atmospheric warming and associated ablation at the ice sheet margin. With additional sensitivity experiments we also show that the spread in mass loss is only weakly affected by the choice of the ice sheet model mechanical parameters. [ABSTRACT FROM AUTHOR]

Providing reliable projections of the ice sheet contribution to future sea-level rise has become one of the main challenges of the ice sheet modelling community. To increase confidence in future projections, a good knowledge of the present-day state of ice flow dynamics, which is critically dependent on basal conditions, is strongly needed. The main difficulty is tied to the scarcity of observations at the ice–bed interface at the scale of the whole ice sheet, resulting in poorly constrained parameterisations in ice sheet models. To circumvent this drawback, inverse modelling approaches can be developed to infer initial conditions for ice sheet models that best reproduce available data. Most often such approaches allow for a good representation of the mean present-day state of the ice sheet but are accompanied with unphysical trends. Here, we present an initialisation method for the Greenland ice sheet using the thermo-mechanical hybrid GRISLI (GRenoble Ice Shelf and Land Ice) ice sheet model. Our approach is based on the adjustment of the basal drag coefficient that relates the sliding velocities at the ice–bed interface to basal shear stress in unfrozen bed areas. This method relies on an iterative process in which the basal drag is periodically adjusted in such a way that the simulated ice thickness matches the observed one. The quality of the method is assessed by computing the root mean square errors in ice thickness changes. Because the method is based on an adjustment of the sliding velocities only, the results are discussed in terms of varying ice flow enhancement factors that control the deformation rates. We show that this factor has a strong impact on the minimisation of ice thickness errors and has to be chosen as a function of the internal thermal state of the ice sheet (e.g. a low enhancement factor for a warm ice sheet). While the method performance slightly increases with the duration of the minimisation procedure, an ice thickness root mean square error (RMSE) of 50.3 m is obtained in only 1320 model years. This highlights a rapid convergence and demonstrates that the method can be used for computationally expensive ice sheet models. [ABSTRACT FROM AUTHOR]

Providing reliable projections of the ice-sheet contribution to future sea-level rise has become one of the main challenges of the ice-sheet modelling community. To increase confidence in future projections, a good knowledge of the present-day state of the ice flow dynamics, which is critically dependent on basal conditions, is strongly needed. The main difficulty is tied to the scarcity of observations at the ice-bed interface at the scale of the whole ice sheet, resulting in poorly constrained parameterisations in ice-sheet models. To circumvent this drawback, inverse modelling approaches can be developed and validated against available data to infer reliable initial conditions of the ice sheet. Here, we present a spin-up method for the Greenland ice sheet using the thermo-mechanical hybrid GRISLI ice-sheet model. Our approach is based on the adjustment of the basal drag coefficient that relates the sliding velocities at the ice-bed interface to basal shear stress in unfrozen bed areas. This method relies on an iterative process in which the basal drag is periodically adjusted in such as way that the simulated ice thickness matches the observed one. The process depends on three parameters controlling the duration and the number of iterations. The best spin-up parameters are chosen according to two criteria to minimize errors in sea-level projections: the final difference between the simulated and the observed Greenland ice volume as well as the final ice volume trend which must both be as low as possible. To increase confidence in the inferred parameters, we also make sure that the final ice thickness root mean square error from the observations is not greater than a few tens of meters. Our best results are obtained after only 420 years of simulation, highlighting a rapid convergence and demonstrating that our method can be used for computationally expensive ice sheet models. [ABSTRACT FROM AUTHOR]