BCC_CSM1.1m对冬季典型环流系统的预测评估

基于国家气候中心气候系统模式1.1版本（BCC_CSM1.1m）的历史回报数据，利用时间相关系数和均方根误差等确定性技巧评分，对西伯利亚高压、阿留申低压、东亚冬季风3种东亚地区冬季典型环流系统的预报技巧进行检验评估，并通过时间序列分析和空间相关系数等方法，分析东亚地区冬季典型环流系统的可预报性来源。结果表明:由于模式对热带海洋和北太平洋海平面气压的预测偏差小、对欧亚大陆的预测偏差大，模式对阿留申低压、东亚冬季风的预测技巧高于西伯利亚高压。进一步分析表明:厄尔尼诺和南方涛动（ENSO）是阿留申低压和东亚冬季风的重要可预报性来源，而土壤温度是西伯利亚高压的重要可预报性来源，并受ENSO调制。此外，东亚冬季风的预报技巧也受到西伯利亚高压预报技巧的制约。

Predication of Typical Winter Circulation Systems Based on BCC_CSM1.1m Model

Accurate prediction of East Asian winter climate has become an important topic in climate research. Coupled ocean-atmosphere dynamical model prediction systems have made great progress. It can offer overall outstanding performance, and become the major tool of dynamical climate prediction. The seasonal prediction performance of BCC_CSM1.1m model has been systematically evaluated. It's found that although the model can predict temperature, precipitation, snow cover, and Asian monsoon to some extent, there are still great challenges in the prediction of East Asian winter climate. It is important to analyze the possible causes of model biases and reveal the source of its predictability. Based on the hindcasts of BCC_CSM1.1m, time correlation coefficient and root mean square error are analyzed to evaluate the prediction skills of 3 typical East Asian winter circulation systems, including Siberian high (SH), Aleutian low (AL) and East Asian winter monsoon (EAWM). Then the predictability sources are also examined through time series analysis and pattern correlation coefficient. The results show that the prediction of sea level pressure in tropical region is better than that in the middle and high latitude region. Due to the influence of El Ni?o and Southern Oscillation (ENSO) and its remote teleconnection, the sea level pressure prediction over the ocean is better than that over the continent, which results in better prediction skills of AL and EAWM compared to SH. Further analysis shows that the elimination of super El Ni?o years leads to lower prediction skills of AL and EAWM. The correlation between sea level pressure in Eurasia and ENSO is less than that in tropical and north Pacific regions, indicating that ENSO is an important source of predictability of AL and EAWM. It is also found that soil temperature at 0-10 cm in Siberia is an important factor affecting the simultaneous and later SH, which suggests that the predictability of the SH may come from the shallow soil temperature. After removing super El Ni?o years, the prediction skill of SH is altered greatly, which reflects the modulation of ENSO on SH prediction. The model can overestimate the linear relationship between SH and ENSO, and lead to a poor SH prediction skill. Moreover, the prediction of EAWM depends on the accurate prediction of SH and AL, and its prediction skill is restricted by the poor SH prediction skills to some extent.