![]() Although there has been increasing evidence of a negative Antarctic mass balance in the past decades ( Allison et al. However, the magnitude and sign of Antarctic ice sheet mass balance has long been unclear ( Bentley 1993), due to the inherent uncertainties of the methods including observed surface elevation, satellite gravimetry, and the input–output method, that is, quantifying the difference between ice discharge and surface mass balance (SMB). ![]() Therefore, an accurate quantification of Antarctic mass balance is pivotal for detecting the current state of the ice sheet, predicting its potential contribution to sea level, and for understanding the global climate and hydrological cycle. Giant ice in the Antarctic Ice Sheet has the potential to raise global sea level by about 58.3 m if it all melted ( IPCC 2013), indicating that even minor changes in its volume will have significant impacts on atmospheric circulation, the global hydrological cycle, sea surface temperature, seawater salinity, and the thermohaline circulation. Snow falling each year on the Antarctic Ice Sheet is equivalent to 6 mm of global mean sea level ( Church et al. This suggests that ERA-Interim exhibits the highest performance of interannual variability in the observed precipitation. ERA-Interim shows a significant correlation with interannual variability of observed snow accumulation measurements at 28 of 29 locations, whereas fewer than 20 site observations significantly correlate with simulations by the other models. Although precipitation seasonality over the whole ice sheet is common for all products, ERA-Interim provides an unrealistic estimate of precipitation seasonality on the East Antarctic plateau, with high precipitation strongly peaking in summer. However, because of changes in the observing system, especially the dramatically increased satellite observations for data assimilation, JRA-55 presents a marked jump in snow accumulation around 1979 and a large increase after the late 1990s. Despite underestimated precipitation by the three reanalyses and RACMO2.1, this feature is clearly improved in JRA-55. In terms of the absolute amount of observed snow accumulation in interior Antarctica, RACMO2.3 fits best, while the other models either underestimate (JRA-55, MERRA, ERA-Interim, and RACMO2.1) or overestimate (PMM5) the accumulation. All products qualitatively capture the macroscale spatial variability of observed SMB, but it is not possible to rank their relative performance because of the sparse observations at coastal regions with an elevation range from 200 to 1000 m. Simulated precipitation seasonality is also evaluated using three in situ observations and model intercomparison. In this study, 3265 multiyear averaged in situ observations and 29 observational records at annual time scale are used to examine the performance of recent reanalysis and regional atmospheric climate model products for their spatial and interannual variability of Antarctic surface mass balance (SMB), respectively.
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