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利用一个全球耦合环流模式在理想模式框架下进行了3组动力预报试验,研究了北太平洋和北大西洋海表面温度异常(SSTA)的年代际可预报性和预报技巧。结果表明北太平洋年平均SSTA在年代际尺度上可预报性和预报技巧表现较低,明显弱于北大西洋。通过分析不同季节平均SSTA的可预报性与预报技巧,发现北太平洋中西部区域冬季平均SSTA的年代际可预报性和预报技巧显著高于其他季节,其量值和北大西洋相当,表现为明显的季节依赖性;北大西洋SSTA的可预报性和预报技巧也显示了随季节变化的特征。进一步分析表明,北太平洋SSTA年代际可预报性和预报技巧的季节依赖性归因于北太平洋冬季SSTA的年与年之间再现机制,这一再现机制源于北太平洋混合层显著的季节变化;而北大西洋SSTA的可预报性和预报技巧的季节依赖性则可能与其他过程(如大西洋年代际涛动)的季节变化有关。研究结果表明,对于年代际气候预报,如果考虑所关注指标的季节平均,则可能在某些季节获得比年平均更高的预报技巧。
Using a global coupled circulation model, three sets of dynamic forecasting experiments were carried out in the ideal mode to study the interdecadal predictability and forecasting skills of SSTA over the North Pacific and North Atlantic. The results show that the average annual SSTA of the North Pacific shows less predictability and forecasting skills on the decadal scale than that of the North Atlantic. By analyzing the predictability and forecasting skills of average SSTA in different seasons, it is found that the interdecadal predictability and forecasting skills of winter mean SSTA in the central and western North Pacific are significantly higher than those of other seasons, and their magnitude is similar to that of the North Atlantic, showing obvious Seasonal dependence; the predictability and forecasting skills of the North Atlantic SSTA also show seasonal variations. Further analysis shows that the seasonal dependence of the interdecadal predictability and forecasting skills of the SSTA in the North Pacific Ocean is attributed to the annual year-to-year reappearance mechanism of winter SSTA in the North Pacific Ocean, which is derived from the significant seasonal variation of the North Pacific mixed layer. The seasonal dependence of the North Atlantic SSTA on predictability and forecasting skills may be related to the seasonal variations of other processes such as the decadal Atlantic decadal fluctuations. The results of the study show that for interdecadal climatological forecasts, it is possible to obtain forecasting skills that are higher than the annual average in some seasons, taking into account the seasonal averages of the indicators of interest.