秋季冰雪因子对我国冬季气温预报技能的改善

Improving prediction skills of winter temperature in China using autumn cryosphere

对1979—2012年冬季气温应用经验正交分解方法,并利用北极海冰密集度(Sea Ice Concentration,SIC)和欧亚大陆雪盖(SNow Cover,SNC)观测数据,计算出秋季SIC和SNC对气温变化有显著影响的区域,建立SIC和SNC指数。基于交叉验证方法构建冰雪指数和我国气温的预测模型,定量评估冰雪因子对冬季气温的预测技能。结果表明,在预报技巧范围和评分上,9月SIC和11月SNC指数的综合预报效果优于单个指数的预报效果,高预报技巧区主要位于我国华北和东北地区,该区域平均距平相关系数为0.58,并且优于气候态后报高达18.7%,表明在季节预报系统中考虑冰冻圈的异常是非常有必要的。

In this study,the empirical orthogonal function(EOF) was performed at the anomaly field of the 600-station winter mean temperature in China during the period of 1979-2012.Then,using the observed antecedent Arctic sea ice concentration(SIC) and Eurasian snow cover(SNC) data,the key areas where SIC and SNC anomalies in autumn have significant effects on the principal variation of following temperature in China are calculated,and based on those areas,the SIC and SNC indices are built.Next,the standard linear regression models which can be used to predict the mean winter temperature at individual stations are established,using one or two cryospheric predictor indices.Through the statistical cross-validation,the mean of the anomaly correlation coefficient(ACC) and root mean square error skill score(RMSESS) between the observed and predicted temperatures are used to quantitatively evaluate the predictive skill of cryospheric factors for the winter mean temperature in China.The results show that the skill of hindcasts is greatly different among regions between the single September SIC predictor and November SNC predictor.The SIC index has more noticeable skill on central north China,while the November SNC index has more noticeable skill on northeastern China.While hindcasts using both September SIC and November SNC predictors are better than the single on area and score,almost all stations except the Tibetan Plateau area show significant skill.The grid points with superior skill are centered on north-central,northeastern and northern China,where the regional average ACC is 0.58,and the method outperforms a climatological hindcast is 18.7%.The results obtained in this study suggest that it is very important to incorporate cryosphere variability in seasonal prediction systems.