Estimation of the Antarctic surface mass balance using the regional climate model MAR (1979–2015) and identification of dominant processes

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.

Evaluating a Regional Climate Model Simulation of Greenland Ice Sheet Snow and Firn Density for Improved Surface Mass Balance Estimates

“…When combining snow and firn model density estimates with remote sensing-derived thickness change measurements, care should be taken to verify the accuracy of near-surface density values, and the values should be corrected using observations (as done by Koenig et al, 2016). Adjustments to the minimum initial falling snow density (e.g., as done by Agosta et al (2019) for MAR over Antarctica) are likely sufficient to improve agreement with observations.…”

“…For the Greenland ice sheet, firn density can also influence the storage of meltwater in firn aquifers (e.g., Forster et al, 2013;Harper et al, 2012;Koenig et al, 2014). Higher snow and firn density enhances surface runoff by preventing percolation of new meltwater into the snow and firn (Machguth et al, 2016;Noël et al, 2017), and surface snow density can affect the wind-driven transport of snow, particularly over Antarctica (Agosta et al, 2019;Lenaerts et al, 2012), and near-surface temperature variability in winter (Fréville et al, 2014).…”

Evaluation of Regional Climate Models Using Regionally Optimized GRACE Mascons in the Amery and Getz Ice Shelves Basins, Antarctica

“…RACMO2.3p1 is available from 1979 to 2015. The model estimates SMB at a spatial resolution of 35 km for 1979 to 2017 (Agosta et al, 2019). MAR3.6.41 is a coupled surface-atmosphere RCM that uses the surface scheme Soil Ice Snow Vegetation Atmosphere Transfer (De Ridder and Gallée, 1998), which uses the CROCUS snow model (Brun et al, 1992).…”

“…RACMO2.3p2 is available from 1979 to 2016. We use the version of the model forced by the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis (Dee et al, 2011) at the boundary to be consistent with RACMO2 (Agosta et al, 2019;Van Wessem et al, 2014). The model estimates SMB at a spatial resolution of 35 km for 1979 to 2017 (Agosta et al, 2019).…”

“…Agosta et al, 2019). RACMO2, developed by the Institute for Marine and Atmospheric Research Utrecht at Utrecht University, uses the physics package of the Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts along with the High Resolution Limited Area Model…”