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


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 




南极涛动 年际增量方法 CFSv2 动力统计模型 预测 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



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.


  1. Blockeel, H., and J. Struyf, 2003: Efficient algorithms for decision tree cross-validation. Journal of Machine Learning Research, 3, 621–650.Google Scholar
  2. Cai, W. J., and T. Cowan, 2007: Trends in southern hemisphere circulation in IPCC AR4 models over 1950–99: Ozone depletion versus greenhouse forcing. J. Climate, 20, 681–693, Scholar
  3. Deser, C., R. A. Tomas, and S. L. Peng, 2007: The transient atmospheric circulation response to north atlantic SST and sea ice anomalies. J. Climate, 20, 4751–4767, Scholar
  4. Fan, K., 2009a: Linkage between the atlantic tropical hurricane frequency and the antarctic oscillation in the western hemisphere. Atmospheric and Oceanic Science Letters, 2, 159–164, Scholar
  5. Fan, K., 2009b: Predicting winter surface air temperature in Northeast China. Atmospheric and Oceanic Science Letters, 2, 14–17, Scholar
  6. Fan, K., 2009c: Seasonal forecast model for the number of tropical cyclones to make landfall in China. Atmospheric and Oceanic Science Letters, 2, 251–254, Scholar
  7. Fan, K., and H. J. Wang, 2004: Antarctic oscillation and the dust weather frequency in North China. Geophys. Res. Lett., 31, L10201, Scholar
  8. Fan, K., and H. J. Wang, 2006: Interannual variability of Antarctic Oscillation and its influence on East Asian climate during boreal winter and spring. Science in China Series D, 49, 554–560, Scholar
  9. Fan, K., and H. J. Wang, 2007: Simulation of the AAO anomaly and its influence on the Northern Hemispheric circulation in boreal winter and spring. Chinese Journal of Geophysics, 50, 397–403, (in Chinese with English abstract)Google Scholar
  10. Fan, K., and H. Liu, 2013: Evaluation of atmospheric circulation in the southern hemisphere in 20CRv2. Atmospheric and Oceanic Science Letters, 6, 337–342.CrossRefGoogle Scholar
  11. Fan, K., B. Q. Tian, and H. J. Wang, 2016: New approaches for the skillful prediction of the winter North Atlantic Oscillation based on coupled dynamic climate models. International Journal of Climatology, 36, 82–94, Scholar
  12. Fan, K., H. J. Wang, and Y. J. Choi, 2008: A physically-based statistical forecast model for the middle-lower reaches of the Yangtze River Valley summer rainfall. Chinese Science Bul letin, 53, 602–609, Scholar
  13. Fyfe, J. C., G. J. Boer, and G. M. Flato, 1999: The Arctic and Antarctic Oscillations and their projected changes under global warming. Geophys. Res. Lett., 26, 1601–1604, Scholar
  14. Gao, H., F. Xue, and H. J. Wang, 2003: Influence of interannual variability of Antarctic Oscillation on Mei-yu along the Yangtze and Huaihe River valley and its importance to prediction. Chinese Science Bulletin, 48, 61–67.CrossRefGoogle Scholar
  15. Gong, D. Y., and S. W. Wang, 1998: Antarctic Oscillation: Concept and applications. Chinese Science Bulletin, 43, 734–738, Scholar
  16. Gong, D. Y., and S. W. Wang, 1999: Definition of Antarctic Oscillation index. Geophys. Res. Lett., 26, 459–462, Scholar
  17. Gupta, A. S., and M. H. England, 2007: Coupled ocean–atmosphere feedback in the southern annular mode. J. Climate, 20, 3677–3692, Scholar
  18. Han, T. T., H. J. Wang, and J. Q. Sun, 2017: Strengthened relationship between the antarctic oscillation and enso after the mid-1990s during austral spring. Adv. Atmos. Sci., 34, 54–65, Scholar
  19. Hao, X., S. P. He, H. J. Wang, and T. T. Han, 2017: The impact of long-term oceanic warming on the antarctic oscillation in austral winter. Scientific Reports, 7, 12321, Scholar
  20. Hendon, H. H., D. W. J. Thompson, and M. C. Wheeler, 2007: Australian rainfall and surface temperature variations associated with the Southern Hemisphere annular Mode. J. Climate, 20, 2452–2467, Scholar
  21. Huang, Y. Y., H. J. Wang, and K. Fan, 2014: Improving the prediction of the summer Asian-Pacific Oscillation using the interannual increment approach. J. Climate, 27, 8126–8134, Scholar
  22. Jiang, X. W., S. Yang, Y. Q. Li, A. Kumar, W. Q. Wang, and Z. T. Gao, 2013: Dynamical prediction of the east asian winter monsoon by the ncep climate forecast system. J. Geophys. Res., 118, 1312–1328, Scholar
  23. Kalnay E., and Coauthors, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437–472,<0437:TNYRP>2.0.CO;2.CrossRefGoogle Scholar
  24. Kumar, A., M. Chen, L. Zhang, W. Wang, Y. Xue, C. Wen, L. Marx, and B. Huang, 2012: An analysis of the nonstationarity in the bias of sea surface temperature forecasts for the NCEP Climate Forecast System (CFS) version 2. Mon. Wea. Rev., 140, 3003–3016, Scholar
  25. Kushnir, Y., W. A. Robinson, I. Bladé, N. M. J. Hall, S. Peng, and R. Sutton, 2002: Atmospheric GCM response to extratropical SST anomalies: Synthesis and evaluation. J. Climate, 15, 2233–2256,<2233:AGRTES>2.0.CO;2.CrossRefGoogle Scholar
  26. Li, F., H. J. Wang, and Y. Q. Gao, 2015: Modulation of Aleutian Low and Antarctic oscillation co-variability by ENSO. Climate Dyn., 44, 1245–1256, Scholar
  27. Lim, E. P., H. H. Hendon, and H. Rashid, 2013: Seasonal predictability of the Southern Annular mode due to its association with ENSO. J. Climate, 26, 8037–8054, Scholar
  28. Limpasuvan, V., and D. L. Hartmann, 2000: Wave-maintained annular modes of climate variability. J. Climate, 13, 4414–4429, https://<4414:WMAMOC>2.0.CO;2.CrossRefGoogle Scholar
  29. Lovenduski, N. S., and N. Gruber, 2005: Impact of the Southern Annular mode on Southern Ocean circulation and biology. Geophys. Res. Lett., 32, L11603, Scholar
  30. Marshall, G. J., 2007: Half-century seasonal relationships between the Southern Annular mode and Antarctic temperatures. International Journal of Climatology, 27, 373–383, Scholar
  31. Marshall, G. J., and T. J. Bracegirdle, 2015: An examination of the relationship between the Southern Annular Mode and Antarctic surface air temperatures in the CMIP5 historical runs. Climate Dyn., 45, 1513–1535, Scholar
  32. Michaelsen, J., 1987: Cross-validation in statistical climate forecast models. J. Climate Appl. Meteor., 26, 1589–1600,<1589:CVISCF>2.0.CO;2.CrossRefGoogle Scholar
  33. Mo, K. C., 2000: Relationships between low-frequency variability in the Southern Hemisphere and sea surface temperature anomalies. J. Climate, 13, 3599–3610,<3599:RBLFVI>2.0.CO;2.CrossRefGoogle Scholar
  34. Pokhrel, S., H. Rahaman, A. Parekh, S. K. Saha, A. Dhakate, H. S. Chaudhari, and R. M. Gairola, 2012: Evaporation-precipitation variability over Indian Ocean and its assessment in NCEP Climate Forecast System (CFSv2). Climate Dyn., 39, 2585–2608, Scholar
  35. Raphael, M. N., W. Hobbs, and I. Wainer, 2011: The effect of Antarctic sea ice on the Southern Hemisphere atmosphere during the southern summer. Climate Dyn., 36, 1403–1417, Scholar
  36. Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, Scholar
  37. Riddle, E. E., A. H Butler., J. C. Furtado, J. L. Cohen, and A. Kumar, 2013: CFSv2 ensemble prediction of the wintertime Arctic Oscillation. Climate Dyn., 41, 1099–1116, Scholar
  38. Saha S., and Coauthors, 2014: The NCEP climate forecast system version 2. J. Climate, 27, 2185–2208, Scholar
  39. Screen, J. A., N. P. Gillett, A. Y. Karpechko, and D. P. Stevens, 2010: Mixed layer temperature response to the southern annular mode: Mechanisms and model representation. J. Climate, 23, 664–678, Scholar
  40. Seager, R., N. Harnik, and Y. Kushnir, 2003: Mechanisms of hemispherically symmetric climate variability. J. Climate, 16, 2960–2978,<2960:MOHSCV>2.0.CO;2.CrossRefGoogle Scholar
  41. Silvestri, G. E., and C. S. Vera, 2003: Antarctic Oscillation signal on precipitation anomalies over southeastern South America. Geophys. Res. Lett., 30, 2115, Scholar
  42. Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006). J. Climate, 21, 2283–2296, Scholar
  43. Stammerjohn, S. E., and R. C. Smith, 1997: Opposing Southern Ocean climate patterns as revealed by trends in regional sea ice coverage. Climatic Change, 37, 617–639, Scholar
  44. Stammerjohn, S. E., D. G. Martinson, R. C. Smith, X. J. Yuan, and D. Rind, 2008: Trends in Antarctic annual sea ice retreat and advance and their relation to El Niño-Southern Oscillation and Southern Annular mode variability. J. Geophys. Res., 113, C03S90, Scholar
  45. Sun, B., and H. J. Wang, 2013: Larger variability, better predictability? International Journal of Climatology, 33, 2341–2351, Scholar
  46. Sun, J. Q., 2010: Possible impact of the boreal spring Antarctic oscillation on the North American summer monsoon. Atmospheric and Oceanic Science Letters, 3, 232–236, Scholar
  47. Sun, J. Q., H. J. Wang, and Y. Wei, 2009: A possible mechanism for the co-variability of the boreal spring Antarctic Oscillation and the Yangtze River valley summer rainfall. International Journal of Climatology, 29, 1276–1284, Scholar
  48. Thompson, D. W. J., and J. M. Wallace, 2000: Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Climate, 13, 1000–1016,<1000:AMITEC>2.0.CO;2.Google Scholar
  49. Thompson, D. W. J., and S. Solomon, 2002: Interpretation of recent Southern Hemisphere climate change. Science, 296, 895–899, Scholar
  50. Tian, B. Q., and K. Fan, 2015: A skillful prediction model for winter NAO Based on Atlantic sea surface temperature and Eurasian snow cover. Wea. Forecasting, 30, 197–205, Scholar
  51. Tian, B. Q., K. Fan, and H. Q. Yang, 2018: East Asian winter monsoon forecasting schemes based on the NCEP’s climate forecast system. Climate Dyn., 51, 2793–2805, Scholar
  52. Wang, H. J., Y. Zhang, and X. M. Lang, 2010: On the predictand of short-term climate prediction. Climatic and Environmental Research, 15, 225–228. (in Chinese with English abstract)Google Scholar
  53. Wang, W. Q., M. Y. Chen, and A. Kumar, 2013: Seasonal prediction of Arctic sea ice extent from a coupled dynamical forecast system. Mon. Wea. Rev., 141, 1375–1394, Scholar
  54. Wu, Q. G., and X. D. Zhang, 2011: Observed evidence of an impact of the Antarctic sea ice dipole on the Antarctic Oscillation. J. Climate, 24, 4508–4518, Scholar
  55. Xu, X. P., F. Li, S. P. He, and H. J. Wang, 2018: Subseasonal reversal of East Asian surface temperature variability in winter 2014/15. Adv. Atmos. Sci., 35, 737–752, Scholar
  56. Xue, F., H. J. Wang, and J. H. He, 2003: Interannual variability of Mascarene high and Australian high and their influences on summer rainfall over East Asia. Chin. Sci. Bull., 48, 492–497, Scholar
  57. Yin, Z. C., and H. J. Wang, 2016: Seasonal prediction of winter haze days in the north central north china plain. Atmos. Chem. Phys., 16, 14 843–14 852, Scholar
  58. Zhang, J. L., 2007: Increasing Antarctic sea ice under warming atmospheric and oceanic conditions. J. Climate, 20, 2515–2529, Scholar
  59. Zhou, T. J., and R. C. Yu, 2004: Sea-surface temperature induced variability of the Southern Annular Mode in an atmospheric general circulation model. Geophys. Res. Lett., 31, L24206, Scholar

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

Personalised recommendations