What is the surface mass balance of Antarctica? An intercomparison of regional climate model estimates

Abstract

Abstract. We compare the performance of five different regional climate models (RCMs) (COSMO-CLM2, HIRHAM5, MAR3.10, MetUM, and RACMO2.3p2), forced by ERA-Interim reanalysis, in simulating the near-surface climate and surface mass balance (SMB) of Antarctica. All models simulate Antarctic climate well when compared with daily observed temperature and pressure, with nudged models matching daily observations slightly better than free-running models. The ensemble mean annual SMB over the Antarctic ice sheet (AIS) including ice shelves is 2329±94 Gt yr−1 over the common 1987–2015 period covered by all models. There is large interannual variability, consistent between models due to variability in the driving ERA-Interim reanalysis. Mean annual SMB is sensitive to the chosen period; over our 30-year climatological mean period (1980 to 2010), the ensemble mean is 2483 Gt yr−1. However, individual model estimates vary from 1961±70 to 2519±118 Gt yr−1. The largest spatial differences between model SMB estimates are in West Antarctica, the Antarctic Peninsula, and around the Transantarctic Mountains. We find no significant trend in Antarctic SMB over either period. Antarctic ice sheet (AIS) mass loss is currently equivalent to around 0.5 mm yr−1 of global mean sea level rise (Shepherd et al., 2020), but our results indicate some uncertainty in the SMB contribution based on RCMs. We compare modelled SMB with a large dataset of observations, which, though biased by undersampling, indicates that many of the biases in SMB are common between models. A drifting-snow scheme improves modelled SMB on ice sheet surface slopes with an elevation between 1000 and 2000 m, where strong katabatic winds form. Different ice masks have a substantial impact on the integrated total SMB and along with model resolution are factored into our analysis. Targeting undersampled regions with high precipitation for observational campaigns will be key to improving future estimates of SMB in Antarctica.

Downscaled surface mass balance in Antarctica: impacts of subsurface processes and large-scale atmospheric circulation

“…Furthermore, RCMs also improve the physical representations of specific processes over polar areas (Lenaerts et al, 2019). Mottram et al (2021) evaluated Antarctic SMB calculated from the outputs from five different RCM simulations driven by ERA-Interim (1987-2017. These five models showed mean annual SMB ranging from 1961 ± 70 to 2519 ± 118 Gt yr −1 .…”

“…The overall model spread in SMB models corresponds to approximately 2 mm of sea level change per year. Mottram et al (2021) also showed that when compared to in situ observation from both automatic weather stations and glaciological stake measurements, the data availability proved insufficient to distinguish between better-performing model estimates. Fettweis et al (2020) found similar conclusions for Greenland, where the RCMs displayed different strengths and weaknesses when evaluated both spatially and temporally.…”

“…Fettweis et al (2020) found similar conclusions for Greenland, where the RCMs displayed different strengths and weaknesses when evaluated both spatially and temporally. Mottram et al (2021) and Verjans et al (2021) furthermore showed that subsurface processes that drive melt and refreezing are extremely important when estimating the SMB. Hence, we here include firn processes by forcing a newly developed full-subsurface SMB model for Antarctica with the RCM HIRHAM5 (Christensen et al, 2007(Christensen et al, ) over 1979(Christensen et al, -2017 assess the effects of firn processes on estimates of ice sheet SMB.…”

“…Acknowledging that it might be challenging to judge the performance of the SMB model against in situ observations (Mottram et al, 2021), we also compare our modelled SMB results with a GRACE gravimetry estimate of the mass balance to determine any systematic biases. Finally, studies have shown that precipitation is not only the largest contributor to Antarctic SMB (Krinner et al, 2007;Agosta et al, 2019), but it also has a spatial heterogeneous distribution varying over time, which affects the SMB (Fyke et al, 2017).…”

“…Different representations of these may explain differences between modelled SMB (e.g. Mottram et al, 2021) as well as discrepancies between the GRACE mass balance and SMB − D solutions. Our study therefore also quantifies how regional climate indices affect SMB on a basin scale.…”