使用ECMWF集合预报数据集估计全球季节平均气候异常的潜在可预报性

POTENTIAL PREDICTABILITY OF SEASONAL MEAN CLIMATE ANOMALY ASSESSED FROM ECMWF ENSEMBLE SEASONAL FORECASTS

利用ECMWFPROVOST项目产生的在给定海表温度强迫下的 15a(1979～ 1993)季节集合预报数据集 ,分析揭示了季节平均气候异常潜在可预报性的全球分布。首先 ,利用可再现的强迫模态重建集合资料场 ,在Kolmogorov Smirnov(K S)检验的基础上定义潜在可预报性指数PUK,然后 ,将重建场的PUK 与重建场贡献于集合平均的方差比结合 ,提出了定量估计局地潜在可预报性的指数PI。以全球 85 0hPa温度季节平均异常场为例 ,对PI进行定量计算表明 :不仅大部分热带地区 ,而且热带外一些地区的季节平均气候异常具有潜在可预报性 ,主要分布在北美、南非和亚洲部分季风区 ;全球大部分潜在可预报地区主要受ENSO型强迫控制 ,而部分温带地区如中国华北、中亚、北美南部主要受非ENSO型强迫控制 ;局地潜在可预报性具有季节性 ,夏季可预报性较强 ,冬季较弱。通过与其他几种估计季节潜在可预报性的方法进行比较表明 。

The potential predictability of the seasonal mean climate anomaly has been assessed by using the dataset of European Centre for Medium Range Weather Forecasts (ECMWF) ensemble seasonal forecasts for the period 1979～1993.The forecasts were created with prescribed observed sea surface temperature taken from ECMWF re-analysis and updated daily in the forecasts.By reconstructing the ensemble in terms of reproducible forced modes,and by defining a predictive utility index (PU K) based on the Kolmogorov-Smirnov (KS) test,a new quantitative measure for evaluating potential predictability,i.e.the predictability index (PI),which combines PU k with the local variance contribution to ensemble mean,was proposed.The quantitative analysis with PI for the 850 hPa temperature has shown that the seasonal mean anomalies over not only most of the tropical regions but some extratropical regions are predictable.Extratropical predictable regions are found mainly over North America,south Africa and part of Asian monsoon region.Interestingly,the potential predictability over some extratropical regions like northern China,middle Asia and southern part of North America is controlled by the non-ENSO forcing,although the ENSO forcing generally dominates most of the predictable regions.The results also indicate that the predictable regions have obvious seasonality.Summer possesses the largest areas with high PI values while winter possesses the smallest.The results have been compared with other methods,which shows that the PI analysis proposed here can extract more predictable information especially for the extratropical regions.