Abstract

Abstract. The Antarctic ice sheet mass balance is a major component of the sea level budget and results from the difference of two fluxes of a similar magnitude: ice flow discharging in the ocean and net snow accumulation on the ice sheet surface, i.e. the surface mass balance (SMB). Separately modelling ice dynamics and SMB is the only way to project future trends. In addition, mass balance studies frequently use regional climate models (RCMs) outputs as an alternative to observed fields because SMB observations are particularly scarce on the ice sheet. Here we evaluate new simulations of the polar RCM MAR forced by three reanalyses, ERA-Interim, JRA-55, and MERRA-2, for the period 1979–2015, and we compare MAR results to the last outputs of the RCM RACMO2 forced by ERA-Interim. We show that MAR and RACMO2 perform similarly well in simulating coast-to-plateau SMB gradients, and we find no significant differences in their simulated SMB when integrated over the ice sheet or its major basins. More importantly, we outline and quantify missing or underestimated processes in both RCMs. Along stake transects, we show that both models accumulate too much snow on crests, and not enough snow in valleys, as a result of drifting snow transport fluxes not included in MAR and probably underestimated in RACMO2 by a factor of 3. Our results tend to confirm that drifting snow transport and sublimation fluxes are much larger than previous model-based estimates and need to be better resolved and constrained in climate models. Sublimation of precipitating particles in low-level atmospheric layers is responsible for the significantly lower snowfall rates in MAR than in RACMO2 in katabatic channels at the ice sheet margins. Atmospheric sublimation in MAR represents 363 Gt yr−1 over the grounded ice sheet for the year 2015, which is 16 % of the simulated snowfall loaded at the ground. This estimate is consistent with a recent study based on precipitation radar observations and is more than twice as much as simulated in RACMO2 because of different time residence of precipitating particles in the atmosphere. The remaining spatial differences in snowfall between MAR and RACMO2 are attributed to differences in advection of precipitation with snowfall particles being likely advected too far inland in MAR.

Highlights

  • Mass loss from the Antarctic ice sheet (AIS) and therewith its contribution to the sea level budget results from the difference of two fluxes of a similar magnitude: ice flow discharging in the ocean (D) and net snow accumulation on the ice sheet surface, i.e. the surface mass balance (SMB)

  • Another noticeable result is that MAR forced by ERA-Interim, JRA-55, and MERRA-2 gives very similar results for the SMB spatial pattern, at the observation locations (Fig. 2) and at the ice sheet scale

  • We find no significant differences in the SMB simulated by MAR and RACMO2 when integrated over the ice sheet or its major basins (Table 2)

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Summary

Introduction

Mass loss from the Antarctic ice sheet (AIS) and therewith its contribution to the sea level budget results from the difference of two fluxes of a similar magnitude: ice flow discharging in the ocean (D) and net snow accumulation on the ice sheet surface, i.e. the surface mass balance (SMB). Interpolation techniques used to interpolate the scarce SMB observations (Vaughan et al, 1999; Arthern et al, 2006) encounter major caveats (Magand et al, 2008; Genthon et al, 2009; Picard et al, 2009) This is why many AIS mass balance studies use output of regional climate models (RCMs) to estimate ice sheet SMB for the recent decades In order to obtain a good agreement with observations, atmospheric models require accurate large-scale circulation patterns together with a proper representation of snow surface processes, clouds, and turbulent fluxes and a relatively high horizontal resolution to properly resolve the complex ice sheet topography at the margins.

Regional atmospheric models
Forcing reanalyses
SMB observations and sectors of strong SMB gradients
Model–observation comparison method
Evaluation of the modelled SMB
Drifting snow transport features
Sublimation of precipitation in low-level atmosphere
Precipitation formation and advection
Discussion and conclusion
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