钱卓蕾, 王会军, 孙建奇. 2012: 耦合模式对冬春季节南、北极涛动的季节气候预测能力研究. 气象学报, (2): 222-235. DOI: 10.11676/qxxb2012.022
引用本文: 钱卓蕾, 王会军, 孙建奇. 2012: 耦合模式对冬春季节南、北极涛动的季节气候预测能力研究. 气象学报, (2): 222-235. DOI: 10.11676/qxxb2012.022
QIAN Zhuolei, WANG Huijun, SUN Jianqi. 2012: The hindcast of winter and spring Arctic and Antarctic Oscillation with the coupled climate models. Acta Meteorologica Sinica, (2): 222-235. DOI: 10.11676/qxxb2012.022
Citation: QIAN Zhuolei, WANG Huijun, SUN Jianqi. 2012: The hindcast of winter and spring Arctic and Antarctic Oscillation with the coupled climate models. Acta Meteorologica Sinica, (2): 222-235. DOI: 10.11676/qxxb2012.022

耦合模式对冬春季节南、北极涛动的季节气候预测能力研究

The hindcast of winter and spring Arctic and Antarctic Oscillation with the coupled climate models

  • 摘要: 研究评估了耦合气候模式对冬、春季北极涛动(AO)和南极涛动(AAO)的预测效能。结果表明,模式对于北极涛动和南极涛动的模拟能力都是比较强的,其中,对冬季的模拟能力要强于春季。冬季,几乎所有的模式都能很好地模拟出对流层内北极涛动的空间分布形态特点,空间相关系数很高。春季,大部分模式的模拟结果仍然是比较好,与再分析资料结果的空间相关性比较高。对于南极涛动,模式在全年对流层各个层次上都能比较好地刻画其空间分布,且模式在850 hPa等压面上的空间模拟效能要稍强于海平面。相对来说,冬季的模拟效果也稍强于其他季节,但是,差异不显著,模式与模式的差别也比较小。另外,模式对于北极涛动和南极涛动的时间序列模拟能力有限,时间序列相关系数只有少数几个模式达到显著性水平。

     

    Abstract: This study evaluates the ability of the global coupled climate models in hindcasting the Arctic Oscillation (AO) and Antarctic Oscillation (AAO). The results show that the models can well simulate the spatial distribution of AO with better results in winter than in spring. In the troposphere in spring, the simulation of AO on the whole is still relatively good with a comparatively high correlation with the NCEP/NCAR reanalysis. The models can also well reproduce the spatial distribution of AAO throughout the year at all levels of the troposphere, and the spatial simulation is better at 850 hPa than at the surface. Although the simulation is better in winter than in other seasons, the seasonal variation is not so significant and the differences among different models are relatively small. In addition, the capability of the models for “predicting” the AO and the AAO index time series is limited, because only a few models can capture their observed interannual variability at the 95% significance level.

     

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