基于统计与动力相结合的南极夏季海冰跨季节预测

A statistical-dynamical joint model for seasonal prediction of Antarctic summer sea ice

选取美国国家环境预测中心气候预测系统(CFS)第二版预测的南大洋(40°S—80°S)夏季海温、海洋性大陆地区(100°E—130°E,10°N—15°S)春季海温以及南半球热带外(20°S—90°S)春季海平面气压3个预测因子,利用奇异值分析的方法提取相关信息,采用回归模型对南极夏季海冰场进行预测。从该模式对1983—2018年南极夏季海冰密集度异常值的回报效果看,模型的预测技巧较CFS原始预测有显著的提高。对单格点上预测与实测的时间相关系数提高到0.76;从交叉检验的结果来看,预测技巧较CFS同样有显著提高;从对南极海冰范围的预测来看,该模型的预测效果最好,明显优于CFS原始预测和持续性预测。这些结果表明,该模型具有较高的预测性能。

To model summer sea ice in Antarctica,three predictors are selected:(1)sea surface temperature prediction in the Southern Ocean(40°S-80°S)from the Climate Forecast System version 2(CFSv2),(2)observed sea surface temperature around the Maritime Continent(100°E-130°E,10°N-15°S)in early austral spring;(3)observed sea level pressure in the southern extratropics(20°S-90°S)in early austral spring.In this study,the singular value decomposition method is used to extract relevant information,then a regression model is applied to predict the sea ice field.When applied retrospectively to simulations for 1983-2018,this model yields significantly improved results compared with raw CFSv2 predictions:temporal correlation between predicted sea ice concentrations at single grid points and observations is significant,with an average value of 0.76 over the Southern Ocean.From cross-validation results,prediction results are significantly better than those of CFSv2,with predicted sea ice extent significantly improved relatively to CFSv2 and to persistence prediction.These results indicate that the prediction performance of the joint statistical-dynamical model described in this study is high.Therefore,this model is important to predict Antarctic sea ice,for example when planning scientific survey activities in Antarctica.