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Article Type:
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A Comparison of Antarctic Ice Sheet Surface Mass Balance from Atmospheric Climate Models and In Situ Observations

Yetang Wang College of Geography and Environment, Shandong Normal University, Jinan, China

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Minghu Ding Institute of Climate System, Chinese Academy of Meteorological Sciences, Beijing, China

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J. M. van Wessem Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, Netherlands

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E. Schlosser Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
Austrian Polar Research Institute, Vienna, Austria

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S. Altnau Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
German Weather Service, Offenbach, Germany

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Michiel R. van den Broeke Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, Netherlands

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Jan T. M. Lenaerts Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, Netherlands

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Elizabeth R. Thomas British Antarctic Survey, Cambridge, United Kingdom

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Elisabeth Isaksson Norwegian Polar Institute, Fram Centre, Tromsø, Norway

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Jianhui Wang Department of Pathology, Yale University, New Haven

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Weijun Sun College of Geography and Environment, Shandong Normal University, Jinan, China

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Print Publication:
15 Jul 2016
DOI:
https://doi.org/10.1175/JCLI-D-15-0642.1
Page(s):
5317–5337
Restricted access

Abstract

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 [ERA-Interim, JRA-55, MERRA, the Polar version of MM5 (PMM5), RACMO2.1, and RACMO2.3] for their spatial and interannual variability of Antarctic surface mass balance (SMB), respectively. Simulated precipitation seasonality is also evaluated using three in situ observations and model intercomparison. 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. 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. Despite underestimated precipitation by the three reanalyses and RACMO2.1, this feature is clearly improved in JRA-55. 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. 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. 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. This suggests that ERA-Interim exhibits the highest performance of interannual variability in the observed precipitation.

Corresponding author address: Yetang Wang, Shandong Normal University, College of Geography and Environment, Wenhua Dong Road 88, Jinan 250014, China. E-mail: wangyetang@163.com

Abstract

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 [ERA-Interim, JRA-55, MERRA, the Polar version of MM5 (PMM5), RACMO2.1, and RACMO2.3] for their spatial and interannual variability of Antarctic surface mass balance (SMB), respectively. Simulated precipitation seasonality is also evaluated using three in situ observations and model intercomparison. 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. 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. Despite underestimated precipitation by the three reanalyses and RACMO2.1, this feature is clearly improved in JRA-55. 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. 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. 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. This suggests that ERA-Interim exhibits the highest performance of interannual variability in the observed precipitation.

Corresponding author address: Yetang Wang, Shandong Normal University, College of Geography and Environment, Wenhua Dong Road 88, Jinan 250014, China. E-mail: wangyetang@163.com
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