Climate Dynamics

, Volume 43, Issue 9–10, pp 2415–2430 | Cite as

An assessment of the ENSO forecast skill of GEOS-5 system

  • Yoo-Geun Ham
  • Siegfried Schubert
  • Yury Vikhliaev
  • Max J. Suarez


The seasonal forecast skill of the NASA Global Modeling and Assimilation Office atmosphere–ocean coupled global climate model (AOGCM) is evaluated based on an ensemble of 9-month lead forecasts for the period 1993 to 2010. The results from the current version (V2) of the AOGCM consisting of the GEOS-5 AGCM coupled to the MOM4 ocean model are compared with those from an earlier version (V1) in which the AGCM (the NSIPP model) was coupled to the Poseidon Ocean Model. It was found that the correlation skill of the Sea Surface Temperature (SST) forecasts is generally better in V2, especially over the sub-tropical and tropical central and eastern Pacific, Atlantic, and Indian Ocean. Furthermore, the improvement in skill in V2 mainly comes from better forecasts of the developing phase of ENSO from boreal spring to summer. The skill of ENSO forecasts initiated during the boreal winter season, however, shows no improvement in terms of correlation skill, and is in fact slightly worse in terms of root mean square error (RMSE). The degradation of skill is found to be due to an excessive ENSO amplitude. For V1, the ENSO amplitude is too strong in forecasts starting in boreal spring and summer, which causes large RMSE in the forecast. For V2, the ENSO amplitude is slightly stronger than that in observations and V1 for forecasts starting in boreal winter season. An analysis of the terms in the SST tendency equation, shows that this is mainly due to an excessive zonal advective feedback. In addition, V2 forecasts that are initiated during boreal winter season, exhibit a slower phase transition of El Nino, which is consistent with larger amplitude of ENSO after the ENSO peak season. It is found that this is due to weak discharge of equatorial Warm Water Volume (WWV). In both observations and V1, the discharge of equatorial WWV leads the equatorial geostrophic easterly current so as to damp the El Nino starting in January. This process is delayed by about 2 months in V2 due to the slower phase transition of the equatorial zonal current from westerly to easterly.


Root Mean Square Error Forecast Skill Zonal Wind Stress Warm Water Volume Thermocline Feedback 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



YGH was funded by the Korea Meteorological Administration Research and Development Program under grant CATER 2012-3041. And, we are grateful for the comments from Christian L. Keppenne to provide a detail information about the initialization systems.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Yoo-Geun Ham
    • 1
    • 2
    • 3
  • Siegfried Schubert
    • 1
  • Yury Vikhliaev
    • 1
    • 4
  • Max J. Suarez
    • 1
  1. 1.Global Modeling and Assimilation OfficeGSFC/NASAGreenbeltUSA
  2. 2.Goddard Earth Sciences Technology and Research Studies and InvestigationsMorgan State UniversityBaltimoreUSA
  3. 3.Faculty of Earth Systems and Environmental SciencesChonnam National UniversityGwangjuKorea
  4. 4.Goddard Earth Sciences Technology and Research Studies and InvestigationsUniversities Space Research AssociationColumbiaUSA

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