S2S模式对中国冬季冷暖转换事件的预报评估OA

In recent years,winter temperature reversal events in China have become increasingly frequent,wide-spread,and persistent,posing significant challenges to economic activities and societal well-being.Accurate predic-tion of the timing,phase transition,and intensity of these events is therefore of considerable practical importance.In this study,hindcast datasets from three models participating in the Subseasonal-to-Seasonal(S2S)Prediction Project—the ECMWF(European Centre for Medium-Range Weather Forecasts),CMA(China Meteorological Administration),and IAP-CAS(Institute of Atmospheric Physics,Chinese Academy of Sciences)models—were used to evaluate the prediction of six representative winter temperature reversal events that occurred during 2005-2018.Dedicated evaluation metrics were developed to quantify forecast skill in terms of transition timing,post-transition intensity,and event persistence,and a comprehensive skill score was constructed to assess the over-all performance of each model. The results show that the forecast skill of all three models strongly depends on both forecast lead time and the characteristics of individual events.At lead times of 15-21 days,all models exhibit relatively low forecast skill and substantial variability in the comprehensive skill score.In contrast,forecast performance improves markedly and becomes more stable at lead times of 1-14 days,with forecast accuracy increasing consistently as lead time decreases.Systematic biases are also evident in intensity prediction:warm-to-cold transitions tend to be underestimated,while cold-to-warm transitions are generally overestimated.At lead times longer than two weeks,the predictive temperature anomalies may even exhibit phase reversal relative to the observations.Significant differences are observed among the three models.Overall,the ECMWF model demonstrates the highest forecast skill,achieving superior performance in terms of turning-point accuracy,intensity indices,and spatial correlation coefficients.The IAP-CAS model performs less favorably for most events and exhibits the largest inter-event vari-ability.Nevertheless,it successfully predicted the 2014-2015 cold-to-warm event at a lead time of three weeks,with all evaluation metrics outperforming those of the ECMWF and CMA models. Further analysis of the associated circulation anomalies indicates that the superior performance of the IAP-CAS model for this event is primarily attributable to its realistic simulation of stratosphere-troposphere coupling o-ver the northern center of the North Atlantic Oscillation(NAO)region.Observations show that geopotential height anomalies propagated both upward and downward between the troposphere and stratosphere prior to the temperature transition.The IAP-CAS model successfully reproduced both the stratosphere-troposphere coupling process and the associated propagation of Eliassen-Palm(EP)wave activity flux.By comparison,the ECMWF and CMA models captured only the upward propagation phase and exhibited significant biases in representing the subsequent downward propagation.These results suggest that accurately simulating key dynamical precursors may substantially improve extended-range forecasts of winter temperature reversal events. Several limitations should be acknowledged.First,the interpretation of the superior performance of the IAP-CAS model is based primarily on a single case study.Whether the identified dynamical mechanism is generally ap-plicable to other temperature reversal events and other prediction systems remains to be determined.Future studies should therefore incorporate a larger sample of events and forecasting models to systematically evaluate the contri-bution of stratosphere-troposphere coupling and related dynamical processes to subseasonal predictability.Such ef-forts would help clarify the of the present findings and provide further guidance for model development and oper-ational forecast improvement.