摘要
Regional climate models often lack detailed description of ice sheet surface and, as a result, are limited in their capability to provide useful information for Antarctic climate research and field campaigns. In this study, an upgraded scheme of surface physics for Antarctic ice sheet(IST) is developed to improve the surface temperature simulations in Antarctica. Through stand-alone simulations, IST shows advantages over the Noah glacial module, a commonly utilized scheme in the widely used Weather Research and Forecasting(WRF) model. These improvements are mainly attributed to the incorporation of detailed snow physics and optimized surface layer parameterization, which results in better simulations of both the surface albedo in summer and the turbulent sensible heat flux in winter. When coupled with IST instead of Noah,WRF models show improved simulation of surface temperatures throughout the year. The bias and root-meansquare-error of annual mean surface temperatures are reduced from 5.7 and 6.0 to 0.2 and 2.7 K.
Regional climate models often lack detailed description of ice sheet surface and, as a result, are limited in their capability to provide useful information for Antarctic climate research and field campaigns. In this study, an upgraded scheme of surface physics for Antarctic ice sheet （IST） is developed to improve the surface tem- perature simulations in Antarctica. Through stand-alone simulations, IST shows advantages over the Noah glacial module, a commonly utilized scheme in the widely used Weather Research and Forecasting （WRF） model. These improvements are mainly attributed to the incorporation of detailed snow physics and optimized surface layer param- eterization, which results in better simulations of both the surface albedo in summer and the turbulent sensible heat flux in winter. When coupled with IST instead of Noah, WRF models show improved simulation of surface tem- peratures throughout the year. The bias and root-mean- square-error of annual mean surface temperatures are reduced from 5.7 and 6.0 to 0.2 and 2.7 K.
基金
supported by the National Basic Research Program of China (2013CBA01805)
the National Natural Science Foundation for Young Scientists of China (41305054)
Tsinghua University Initiative Scientific Research Program (20131089356)
