• Anderson, T. B., D. J. Gianotti, and G. D. Salvucci, 2015: Characterizing the potential predictability of seasonal, station-based heavy precipitation accumulations and extreme dry spell durations. J. Hydrometeor., 16, 843856, doi:10.1175/JHM-D-14-0111.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Becker, E. J., H. van den Dool, and M. Peña, 2013: Short-term climate extremes: Prediction skill and predictability. J. Climate, 26, 512531, doi:10.1175/JCLI-D-12-00177.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, X. G., W. J. Li, Z. G. Ma, and P. Wang, 2007: Water-vapor source shift of Xinjiang region during the recent twenty years. Prog. Nat. Sci., 17, 569575, doi:10.1080/10020070708541037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DelSole, T., and M. Tippett, 2007: Predictability: Recent insights from information theory. Rev. Geophys., 45, RG4002, doi:10.1029/2006RG000202.

  • Deng, Y. Y., T. Gao, H. W. Gao, X. H. Yao, and L. Xie, 2014: Regional precipitation variability in East Asia related to climate and environmental factors during 1979–2012. Sci. Rep., 4, 5693, doi:10.1038/srep05693.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, H., and Coauthors, 2008: Analysis of the severe cold surge, ice-snow and frozen disasters in south China during January 2008: II. Possible climatic causes (in Chinese). Meteor. Mon., 34 (4), 101106.

    • Search Google Scholar
    • Export Citation
  • Gianotti, D. J., B. T. Anderson, and G. D. Salvucci, 2013: What do rain gauges tell us about the limits of precipitation predictability? J. Climate, 26, 56825688, doi:10.1175/JCLI-D-12-00718.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gianotti, D. J., B. T. Anderson, and G. D. Salvucci, 2014: The potential predictability of precipitation occurrence, intensity, and seasonal totals over the continental United States. J. Climate, 27, 69046918, doi:10.1175/JCLI-D-13-00695.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gong, D. Y., and C. H. Ho, 2002: Shift in the summer rainfall over the Yangtze River valley in the late 1970s. Geophys. Res. Lett., 29, 1436, doi:10.1029/2001GL014523.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurvich, C. M., and C. L. Tsai, 1989: Regression and time series model selection in small samples. Biometrika, 76, 297307, doi:10.1093/biomet/76.2.297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein Tank, A. M. G., F. W. Zwiers, and X. Zhang, 2009: Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation. WCDMP-72, WMO/TD-1500, 56 pp. [Available online at www.wmo.int/datastat/documents/WCDMP_72_TD_1500_en_1_1.pdf.]

  • Leith, C. E., 1973: The standard error of time-average estimates of climatic means. J. Appl. Meteor., 12, 10661069, doi:10.1175/1520-0450(1973)012<1066:TSEOTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leith, C. E., 1978: Predictability of climate. Nature, 276, 352355, doi:10.1038/276352a0.

  • Li, Z. X., T. J. Zhou, H. S. Chen, D. H. Ni, and R. H. Zhang, 2015: Modeling the effect of soil moisture variability on summer precipitation variability over East Asia. Int. J. Climatol., 35, 879887, doi:10.1002/joc.4023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y. J., K. Y. Ma, and Z. S. Lin, 2000: Potential predictability of monthly precipitation over China. Acta Meteor. Sin., 14 (3), 316329.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1973: Predictability and periodicity: A review and extension. Proc. Third Conf. on Predictability and Statistics in the Atmospheric Sciences, Boulder, CO, Amer. Meteor. Soc., 1–4.

  • Luo, Y. L., M. W. Wu, F. M. Ren, J. Li, and W. K. Wong, 2016: Synoptic situations of extreme hourly precipitation over China. J. Climate, 29, 87038719, doi:10.1175/JCLI-D-16-0057.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madden, R. A., 1976: Estimates of the natural variability of time averaged sea level pressure. Mon. Wea. Rev., 104, 942952, doi:10.1175/1520-0493(1976)104<0942:EOTNVO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madden, R. A., and D. Shea, 1978: Estimates of the natural variability of time-averaged temperatures over the United States. Mon. Wea. Rev., 106, 16951703, doi:10.1175/1520-0493(1978)106<1695:EOTNVO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Somerville, R., 1987: The predictability of weather and climate. Climatic Change, 11, 239246, doi:10.1007/BF00138802.

  • Su, B. D., T. Jiang, and W. B. Jin, 2006: Recent trends in observed temperature and precipitation extremes in the Yangtze River basin, China. Theor. Appl. Climatol., 83, 139151, doi:10.1007/s00704-005-0139-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, J. Q., and J. Ao, 2013: Changes in precipitation and extreme precipitation in a warming environment in China. Chin. Sci. Bull., 58, 1395, doi:10.1007/s11434-012-5542-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, B. Q., and K. Fan, 2012: Relationship between the late spring NAO and summer extreme precipitation frequency in the middle and lower reaches of the Yangtze River. Atmos. Ocean. Sci. Lett., 5, 455460.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1984: Some effects of finite sample size and persistence on meteorological statistics. Part II: Potential predictability. Mon. Wea. Rev., 112, 23692379, doi:10.1175/1520-0493(1984)112<2369:SEOFSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., Q. H. Ding, X. H. Fu, I. S. Kang, K. Jin, J. Shukla, and F. Doblas-Reyes, 2005: Fundamental challenge in simulation and prediction of summer monsoon rainfall. Geophys. Res. Lett., 32, L15711, doi:10.1029/2005GL022734.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., and Coauthors, 2009: Advance and prospectus of seasonal prediction: Assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004). Climate Dyn., 33, 93117, doi:10.1007/s00382-008-0460-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H. J., F. Xue, and X. Q. Bi, 1997: The interannual variability and predictability in a global climate model. Adv. Atmos. Sci., 14, 554562, doi:10.1007/s00376-997-0073-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H. J., and Coauthors, 2015: A review of seasonal climate prediction research in China. Adv. Atmos. Sci., 32, 149168, doi:10.1007/s00376-014-0016-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., and W. Chen, 2014: An intensity index for the East Asian winter monsoon. J. Climate, 27, 23612374, doi:10.1175/JCLI-D-13-00086.1.

  • Wang, L., and Coauthors, 2008: Analysis of the severe cold surge, ice-snow and frozen disasters in south China during January 2008: I. Climatic features and its impact (in Chinese). Meteor. Mon., 34 (4), 95100.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., and Z. W. Yan, 2011: Changes of frequency of summer precipitation extremes over the Yangtze River in association with large-scale oceanic–atmospheric conditions. Adv. Atmos. Sci., 28, 11181128, doi:10.1007/s00376-010-0128-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y. Q., and L. Zhou, 2005: Observed trends in extreme precipitation events in China during 1961–2001 and the associated changes in large-scale circulation. Geophys. Res. Lett., 32, L09707, doi:10.1029/2005GL023769.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1999: Interannual variability and extreme-value characteristics of several stochastic daily precipitation models. Agric. For. Meteor., 93, 153169, doi:10.1016/S0168-1923(98)00125-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, Z. W., B. Wang, J. P. Li, and F. F. Jin, 2009: An empirical seasonal prediction model of the East Asian summer monsoon using ENSO and NAO. J. Geophys. Res., 114, D18120, doi:10.1029/2009JD011733.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ying, K. R., X. G. Zheng, X. W. Quan, and C. S. Frederiksen, 2013: Predictable signals of seasonal precipitation in the Yangtze–Huaihe River valley. Int. J. Climatol., 33, 30023015, doi:10.1002/joc.3644.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., L. Alexander, G. C. Hegerl, P. D. Jones, A. Tank Klein, T. C. Peterson, B. Trewin, and F. W. Zwiers, 2011: Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdiscip. Rev.: Climate Change, 2, 851870, doi:10.1002/wcc.147.

    • Search Google Scholar
    • Export Citation
  • Zhu, Y. M., and X. Q. Yang, 2003: Relationships between Pacific decadal oscillation (PDO) and climate variabilities in China (in Chinese). Acta Meteor. Sin., 61 (6), 641654.

    • Search Google Scholar
    • Export Citation
  • Zong, H. F., L. T. Chen, and Q. Y. Zhang, 2010: The instability of the interannual relationship between ENSO and the summer rainfall in China (in Chinese). Chin. J. Atmos. Sci., 34 (1), 184192.

    • Search Google Scholar
    • Export Citation
  • Zong, Y. Q., and X. Q. Chen, 2000: The 1998 flood on the Yangtze, China. Nat. Hazards, 22, 165184, doi:10.1023/A:1008119805106.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 9 9 9
PDF Downloads 6 6 6

Potential Predictability of Seasonal Extreme Precipitation Accumulation in China

View More View Less
  • 1 Key Laboratory of Regional Climate–Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
  • | 2 Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom, and Center of Excellence for Climate Change Research, Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia
Restricted access

Abstract

The potential predictability of seasonal extreme precipitation accumulation (SEPA) across mainland China is evaluated, based on daily precipitation observations during 1960–2013 at 675 stations. The potential predictability value (PPV) of SEPA is calculated for each station by decomposing the observed SEPA variance into a part associated with stochastic daily rainfall variability and another part associated with longer-time-scale climate processes. A Markov chain model is constructed for each station and a Monte Carlo simulation is applied to estimate the stochastic part of the variance. The results suggest that there are more potentially predictable regions for summer than for the other seasons, especially over southern China, the Yangtze River valley, the north China plain, and northwestern China. There are also regions of large PPVs in southern China for autumn and winter and in northwestern China for spring. The SEPA series for the regions of large PPVs are deemed not entirely stochastic, either with long-term trends (e.g., increasing trends in inland northwestern China) or significant correlation with well-known large-scale climate processes (e.g., East Asian winter monsoon for southern China in winter and El Niño for the Yangtze River valley in summer). This fact not only verifies the claim that the regions have potential predictability but also facilitates predictive studies of the regional extreme precipitation associated with large-scale climate processes.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Zhongwei Yan, yzw@tea.ac.cn

Abstract

The potential predictability of seasonal extreme precipitation accumulation (SEPA) across mainland China is evaluated, based on daily precipitation observations during 1960–2013 at 675 stations. The potential predictability value (PPV) of SEPA is calculated for each station by decomposing the observed SEPA variance into a part associated with stochastic daily rainfall variability and another part associated with longer-time-scale climate processes. A Markov chain model is constructed for each station and a Monte Carlo simulation is applied to estimate the stochastic part of the variance. The results suggest that there are more potentially predictable regions for summer than for the other seasons, especially over southern China, the Yangtze River valley, the north China plain, and northwestern China. There are also regions of large PPVs in southern China for autumn and winter and in northwestern China for spring. The SEPA series for the regions of large PPVs are deemed not entirely stochastic, either with long-term trends (e.g., increasing trends in inland northwestern China) or significant correlation with well-known large-scale climate processes (e.g., East Asian winter monsoon for southern China in winter and El Niño for the Yangtze River valley in summer). This fact not only verifies the claim that the regions have potential predictability but also facilitates predictive studies of the regional extreme precipitation associated with large-scale climate processes.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Zhongwei Yan, yzw@tea.ac.cn
Save