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Decadal prediction experiments of Beijing Climate Center climate system model version 1.1 (BCC-CSM1.1) participated in Coupled Model Intercomparison Project Phase 5 (CMIP5) had poor skill in extratropics of the North Atlantic, the initialization of which was done by relaxing modeled ocean temperature to the Simple Ocean Data Assimilation (SODA) reanalysis data. This study aims to improve the prediction skill of this model by using the assimilation technique in the initialization. New ocean data are firstly generated by assimilating the sea surface temperature (SST) of the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset to the ocean model of BCC-CSM1.1 via Ensemble Optimum Interpolation (EnOI). Then a suite of decadal re-forecasts launched annually over the period 1961-2005 is carried out with simulated ocean temperature restored to the assimilated ocean data. Comparisons between the re-forecasts and previous CMIP5 forecasts show that the re-forecasts are more skillful in mid-to-high latitude SST of the North Atlantic. Improved prediction skill is also found for the Atlantic multi-decadal oscillation (AMO), which is consistent with the better skill of Atlantic meridional overturning circulation (AMOC) predicted by the re-forecasts. We conclude that the EnOI assimilation generates better ocean data than the SODA reanalysis for initializing decadal climate prediction of BCC-CSM1.1 model.

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-mean-square-error of annual mean surface temperatures are reduced from 5.7 and 6.0 to 0.2 and 2.7 K.