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Advances in Atmospheric Sciences

, Volume 36, Issue 3, pp 292–302 | Cite as

Verification and Improvement of the Ability of CFSv2 to Predict the Antarctic Oscillation in Boreal Spring

  • Dapeng Zhang
  • Yanyan HuangEmail author
  • Bo Sun
  • Fei Li
  • Huijun Wang
Original Paper
  • 19 Downloads

Abstract

The boreal spring Antarctic Oscillation (AAO) has a significant impact on the spring and summer climate in China. This study evaluates the capability of the NCEP’s Climate Forecast System, version 2 (CFSv2), in predicting the boreal spring AAO for the period 1983–2015. The results indicate that CFSv2 has poor skill in predicting the spring AAO, failing to predict the zonally symmetric spatial pattern of the AAO, with an insignificant correlation of 0.02 between the predicted and observed AAO Index (AAOI). Considering the interannual increment approach can amplify the prediction signals, we firstly establish a dynamical–statistical model to improve the interannual increment of the AAOI (DY AAOI), with two predictors of CFSv2-forecasted concurrent spring sea surface temperatures and observed preceding autumn sea ice. This dynamical–statistical model demonstrates good capability in predicting DY AAOI, with a significant correlation coefficient of 0.58 between the observation and prediction during 1983–2015 in the two-year-out cross-validation. Then, we obtain an improved AAOI by adding the improved DY AAOI to the preceding observed AAOI. The improved AAOI shows a significant correlation coefficient of 0.45 with the observed AAOI during 1983–2015. Moreover, the unrealistic atmospheric response to March–April–May sea ice in CFSv2 may be the possible cause for the failure of CFSv2 to predict the AAO. This study gives new clues regarding AAO prediction and short-term climate prediction.

Key words

Antarctic Oscillation interannual-increment approach CFSv2 dynamical–statistical model prediction 

摘要

春季南极涛动(AAO)对我国春夏季气候异常影响显著.本研究评估了美国第二代气候预测系统(CFSv2)对于1983-2015年春季南极涛动的预测效能.评估结果显示,CFSv2对春季南极涛动的直接预测技巧有限,未能预测出春季AAO空间分布的纬向对称性,南极涛动指数(AAOI)与观测的相关仅有0.02.考虑到年际增量方法可以放大预测信号,本文选取了前期秋季观测海冰和同期模式春季海表面温度作为预测因子,建立动力统计预测模型来改进南极涛动指数的年际增量(DY_AAOI).研究结果显示,该动力统计模型对DY_AAOI改进效果显著,改进后的交叉验证结果与观测的相关系数提高至0.59.然后,我们把改进后的DY_AAOI加上前一年观测的AAOI得出最终改进的AAOI,其与观测的相关提高到了0.45.此外,CFSv2未能成功模拟出春季大气对同期海冰的响应也许是导致CFSv2对春季AAO预测技巧有限的原因.本文的研究成果为AAO的预测以及短期气候预测提供了新的有效途径.

关键词

南极涛动 年际增量方法 CFSv2 动力统计模型 预测 

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Notes

Acknowledgements

This work was supported by the National Key Research and Development Program of China (Grant No. 2016YFA0600703) and the funding of the Jiangsu Innovation & Entrepreneurship Team and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Dapeng Zhang
    • 1
    • 2
  • Yanyan Huang
    • 1
    • 2
    Email author
  • Bo Sun
    • 1
    • 2
  • Fei Li
    • 1
    • 2
    • 3
  • Huijun Wang
    • 1
    • 2
  1. 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of EducationNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Nansen–Zhu International Research Centre, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  3. 3.NILU – Norwegian Institute for Air ResearchKjellerNorway

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