Climate Dynamics

, Volume 41, Issue 7–8, pp 2213–2230 | Cite as

Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China

  • Lifeng LuoEmail author
  • Wei Tang
  • Zhaohui Lin
  • Eric F. Wood
Part of the following topical collections:
  1. Topical Collection on Climate Forecast System Version 2 (CFSv2)


National Centers for Environmental Prediction recently upgraded its operational seasonal forecast system to the fully coupled climate modeling system referred to as CFSv2. CFSv2 has been used to make seasonal climate forecast retrospectively between 1982 and 2009 before it became operational. In this study, we evaluate the model’s ability to predict the summer temperature and precipitation over China using the 120 9-month reforecast runs initialized between January 1 and May 26 during each year of the reforecast period. These 120 reforecast runs are evaluated as an ensemble forecast using both deterministic and probabilistic metrics. The overall forecast skill for summer temperature is high while that for summer precipitation is much lower. The ensemble mean reforecasts have reduced spatial variability of the climatology. For temperature, the reforecast bias is lead time-dependent, i.e., reforecast JJA temperature become warmer when lead time is shorter. The lead time dependent bias suggests that the initial condition of temperature is somehow biased towards a warmer condition. CFSv2 is able to predict the summer temperature anomaly in China, although there is an obvious upward trend in both the observation and the reforecast. Forecasts of summer precipitation with dynamical models like CFSv2 at the seasonal time scale and a catchment scale still remain challenge, so it is necessary to improve the model physics and parameterizations for better prediction of Asian monsoon rainfall. The probabilistic skills of temperature and precipitation are quite limited. Only the spatially averaged quantities such as averaged summer temperature over the Northeast China of CFSv2 show higher forecast skill, of which is able to discriminate between event and non-event for three categorical forecasts. The potential forecast skill shows that the above and below normal events can be better forecasted than normal events. Although the shorter the forecast lead time is, the higher deterministic prediction skill appears, the probabilistic prediction skill does not increase with decreased lead time. The ensemble size does not play a significant role in affecting the overall probabilistic forecast skill although adding more members improves the probabilistic forecast skill slightly.


Seasonal prediction Reforecast skill Ensemble prediction Summer climate anomalies 



We would like to thank the reviewers for thoughtful and helpful comments. This research is supported by Grant NA10OAR4310246 and NA12OAR4310081 from the NOAA Climate Program Office, Grant 41175073 from National Natural Science Foundation of China and Grant U1133603 from NSFC—Yunnan Province joint Grant. CFSv2 reforecast data are provided by the National Centers for Environmental Prediction and National Climatic Data Center, observational data of precipitation and temperature are provided by the NOAA Climate Prediction Center (CPC) and the Chinese Meteorological Administration (CMA).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Lifeng Luo
    • 1
    • 2
    Email author
  • Wei Tang
    • 3
    • 4
  • Zhaohui Lin
    • 3
  • Eric F. Wood
    • 5
  1. 1.Department of GeographyMichigan State UniversityEast LansingUSA
  2. 2.Center for Global Change and Earth ObservationsMichigan State UniversityEast LansingUSA
  3. 3.Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  4. 4.University of Chinese Academy of SciencesBeijingChina
  5. 5.Department of Civil and Environmental EngineeringPrinceton UniversityPrincetonUSA

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