Climate Dynamics

, Volume 35, Issue 2–3, pp 267–283 | Cite as

How are seasonal prediction skills related to models’ performance on mean state and annual cycle?

  • June-Yi Lee
  • Bin Wang
  • I.-S. Kang
  • J. Shukla
  • A. Kumar
  • J.-S. Kug
  • J. K. E. Schemm
  • J.-J. Luo
  • T. Yamagata
  • X. Fu
  • O. Alves
  • B. Stern
  • T. Rosati
  • C.-K. Park
Article

Abstract

Given observed initial conditions, how well do coupled atmosphere–ocean models predict precipitation climatology with 1-month lead forecast? And how do the models’ biases in climatology in turn affect prediction of seasonal anomalies? We address these questions based on analysis of 1-month lead retrospective predictions for 21 years of 1981–2001 made by 13 state-of-the-art coupled climate models and their multi-model ensemble (MME). The evaluation of the precipitation climatology is based on a newly designed metrics that consists of the annual mean, the solstitial mode and equinoctial asymmetric mode of the annual cycle, and the rainy season characteristics. We find that the 1-month lead seasonal prediction made by the 13-model ensemble has skills that are much higher than those in individual model ensemble predictions and approached to those in the ERA-40 and NCEP-2 reanalysis in terms of both the precipitation climatology and seasonal anomalies. We also demonstrate that the skill for individual coupled models in predicting seasonal precipitation anomalies is positively correlated with its performances on prediction of the annual mean and annual cycle of precipitation. In addition, the seasonal prediction skill for the tropical SST anomalies, which are the major predictability source of monsoon precipitation in the current coupled models, is closely link to the models’ ability in simulating the SST mean state. Correction of the inherent bias in the mean state is critical for improving the long-lead seasonal prediction. Most individual coupled models reproduce realistically the long-term annual mean precipitation and the first annual cycle (solstitial mode), but they have difficulty in capturing the second annual (equinoctial asymmetric) mode faithfully, especially over the Indian Ocean (IO) and Western North Pacific (WNP) where the seasonal cycle in SST has significant biases. The coupled models replicate the monsoon rain domains very well except in the East Asian subtropical monsoon and the tropical WNP summer monsoon regions. The models also capture the gross features of the seasonal march of the rainy season including onset and withdraw of the Asian–Australian monsoon system over four major sub-domains, but striking deficiencies in the coupled model predictions are observed over the South China Sea and WNP region, where considerable biases exist in both the amplitude and phase of the annual cycle and the summer precipitation amount and its interannual variability are underestimated.

Keywords

Coupled atmosphere–ocean model Multi-model ensemble Precipitation Mean states 1-Month lead seasonal prediction Annual mean Annual cycle Monsoon rain domain Asian–Australian monsoon ENSO 

Notes

Acknowledgments

This research was supported by APEC Climate Center (APCC) as a part of APCC International research project and the Korean meteorological Administration Research and Development Program under Grant CATER 2009-1146. Lee and Wang acknowledge support from IPRC, which is in part supported by JAMSTEC, NOAA (grant No. NNX07AG53G), and NOAA (grant No. NA09OAR4320075). This is the IPRC publication 703 and SOEST publication 7947.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • June-Yi Lee
    • 1
  • Bin Wang
    • 1
  • I.-S. Kang
    • 2
  • J. Shukla
    • 3
  • A. Kumar
    • 4
  • J.-S. Kug
    • 5
  • J. K. E. Schemm
    • 4
  • J.-J. Luo
    • 6
  • T. Yamagata
    • 6
  • X. Fu
    • 1
  • O. Alves
    • 7
  • B. Stern
    • 8
  • T. Rosati
    • 8
  • C.-K. Park
    • 9
  1. 1.International Pacific Research CenterUniversity of HawaiiHonoluluUSA
  2. 2.School of Earth and Environmental SciencesSeoul National UniversitySeoulKorea
  3. 3.George Mason UniversityArlingtonUSA
  4. 4.NCEP/NOAA Climate Prediction CenterWashington, DCUSA
  5. 5.Korea Ocean Research and Development InstituteAnsanKorea
  6. 6.FRCGC/JAMSTECYokohamaJapan
  7. 7.Bureau of Meteorology Research CenterMelbourneAustralia
  8. 8.NOAA/GFDLPrinceton UniversityPrincetonUSA
  9. 9.Korean Meteorological AdministrationSeoulKorea

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