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Prediction of primary climate variability modes at the Beijing Climate Center

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Abstract

Climate variability modes, usually known as primary climate phenomena, are well recognized as the most important predictability sources in subseasonal–interannual climate prediction. This paper begins by reviewing the research and development carried out, and the recent progress made, at the Beijing Climate Center (BCC) in predicting some primary climate variability modes. These include the El Niño–Southern Oscillation (ENSO), Madden–Julian Oscillation (MJO), and Arctic Oscillation (AO), on global scales, as well as the sea surface temperature (SST) modes in the Indian Ocean and North Atlantic, western Pacific subtropical high (WPSH), and the East Asian winter and summer monsoons (EAWM and EASM, respectively), on regional scales. Based on its latest climate and statistical models, the BCC has established a climate phenomenon prediction system (CPPS) and completed a hindcast experiment for the period 1991–2014. The performance of the CPPS in predicting such climate variability modes is systematically evaluated. The results show that skillful predictions have been made for ENSO, MJO, the Indian Ocean basin mode, the WPSH, and partly for the EASM, whereas less skillful predictions were made for the Indian Ocean Dipole (IOD) and North Atlantic SST Tripole, and no clear skill at all for the AO, subtropical IOD, and EAWM. Improvements in the prediction of these climate variability modes with low skill need to be achieved by improving the BCC’s climate models, developing physically based statistical models as well as correction methods for model predictions. Some of the monitoring/prediction products of the BCC-CPPS are also introduced in this paper.

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Acknowledgments

The authors are grateful to the three anonymous reviewers for their insightful comments, which helped to improve the quality of the paper.

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Authors and Affiliations

  1. Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, 100081, China

    Hong-Li Ren, Fei-Fei Jin, Lianchun Song, Bo Lu, Ben Tian, Jinqing Zuo, Ying Liu, Jie Wu, Chongbo Zhao, Yu Nie, Peiqun Zhang, Jin Ba, Yujie Wu, Jianghua Wan, Yuping Yan & Fang Zhou

  2. CMA–NJU Joint Laboratory for Climate Prediction Studies, Institute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, Nanjing, 210093, China

    Hong-Li Ren, Lianchun Song, Bo Lu, Ben Tian, Jinqing Zuo, Ying Liu, Jie Wu, Chongbo Zhao, Yu Nie, Peiqun Zhang, Jin Ba, Yujie Wu, Jianghua Wan, Yuping Yan & Fang Zhou

  3. Department of Atmospheric Sciences, University of Hawaii, Honolulu, 96822, USA

    Fei-Fei Jin

  4. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Jinqing Zuo, Ying Liu & Fang Zhou

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  1. Hong-Li Ren
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  2. Fei-Fei Jin
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  3. Lianchun Song
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  12. Jin Ba
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  16. Fang Zhou
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Corresponding author

Correspondence to Hong-Li Ren.

Additional information

Supported by the National (Key) Basic Research and Development (973) Program of China (2015CB453203), China Meteorological Administration Special Public Welfare Research Fund (GYHY201506013 and GYHY201406022), National Natural Science Foundation of China (41205058, 41375062, 41405080, 41505065, 41606019, and 41605116), US National Science Foundation (AGS-1406601), US Department of Energy (DOE) (DE-SC000511), and also partly supported by the UK–China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund.

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Ren, HL., Jin, FF., Song, L. et al. Prediction of primary climate variability modes at the Beijing Climate Center. J Meteorol Res 31, 204–223 (2017). https://doi.org/10.1007/s13351-017-6097-3

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