Abstract
The performance of recent reanalysis products (i.e., ERA-Interim, NCEP2, MERRA, CFSR, and JRA-55) was evaluated based on in situ observations from nine automatic weather stations and one stake network to investigate the monthly and seasonal variability of the surface mass balance in Antarctica. Synoptic precipitation simulations were also evaluated by an investigation of high precipitation events. The seasonal variations showed large fluctuations and were inconsistent at each station, probably owing to the large interannual variability of snow accumulation based on the short temporal coverage of the data. The ERA-Interim and JRA-55 datasets revealed better simulated precision, with the other three models presenting similar simulations at monthly and seasonal timescales. The JRA-55 dataset captured a greater number of synoptic high precipitation events at four of the nine stations. Such events at the other five stations were mainly captured by ERA and CFSR. The NCEP2 dataset was more weakly correlated with each station on all timescales. These results indicate that significant monthly or seasonal correlations between in situ observations and the models had little effect on the capability of the reanalyses to capture high precipitation events. The precision of the five reanalysis datasets widely fluctuated in specific regions or at specific stations at different timescales. Great caution is needed when using a single reanalysis dataset to assess the surface mass balance over all of Antarctica.
Highlights
Ice sheets are important contributors to global sea levels in the context of global warming [1].The Antarctic ice sheet is the largest in the world, and global sea levels could increase by >56 m if it completely melted [2]
These results indicate that significant monthly or seasonal correlations between in situ observations and the models had little effect on the capability of the reanalyses to capture high precipitation events
The correlations were considered significant at a p-value ≤ 0.05, which was computed by transforming the correlation to create a t-statistic with n-2 degrees of freedom, where n is the number of inputs
Summary
The Antarctic ice sheet is the largest in the world, and global sea levels could increase by >56 m if it completely melted [2]. (2015) showed mass gain increases as a result of global warming [5]. To address this discrepancy, the surface mass balance (SMB) needs to be quantified accurately. SMB is defined as the total gain or loss of ice/snow mass at the surface of an ice sheet. Ablation is the total mass loss at the surface caused by melting, evaporation, or wind erosion [6,7]. An understanding of these events is required to correctly interpret the ice cores, and the variability in snow accumulation at a synoptic timescale must be quantified [9,10,11,12]
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