Climate Dynamics

, Volume 44, Issue 3–4, pp 907–923 | Cite as

An evaluation of experimental decadal predictions using CCSM4

  • A. KarspeckEmail author
  • S. Yeager
  • G. Danabasoglu
  • H. Teng


This study assesses retrospective decadal prediction skill of sea surface temperature (SST) variability in initialized climate prediction experiments with the Community Climate System Model version 4 (CCSM4). Ensemble forecasts initialized with two different historical ocean and sea-ice states are evaluated and compared to an ensemble of uninitialized coupled simulations. Both experiments are subject to identical twentieth century historical radiative forcings. Each forecast consists of a 10-member ensemble integrated over a 10-year period. One set of historical ocean and sea-ice conditions used for initialization comes from a forced ocean-ice simulation driven by the Coordinated Ocean-ice Reference Experiments interannually varying atmospheric dataset. Following the Coordinated Model Intercomparison Project version 5 (CMIP5) protocol, these forecasts are initialized every 5 years from 1961 to 1996, and every year from 2000 to 2006. A second set of initial conditions comes from historical ocean state estimates obtained through the assimilation of in-situ temperature and salinity data into the CCSM4 ocean model. These forecasts are only available over a limited subset of the CMIP5 recommended start dates. Both methods result in retrospective SST prediction skill over broad regions of the Indian Ocean, western Pacific Ocean and North Atlantic Ocean that are significantly better than reference skill levels from a spatio-temporal auto-regressive statistical model of SST. However the subpolar gyre region of the North Atlantic stands out as the only region where the CCSM4 initialized predictions outperform uninitialized simulations. Some features of the ocean state estimates used for initialization and their impact on the forecasts are discussed.


Decadal prediction Verification CCSM4 Initialization Ocean data assimilation Probabilistic skill score 



We acknowledge the hard work and dedication of all the scientists and software engineers who contributed to the development of the CCSM4. Special thanks are extended to Jeffrey Anderson, Tim Hoar, Nancy Collins, and Kevin Raeder of the National Center for Atmospheric Research (NCAR) Data Assimilation Research Section, who developed DART and provided ongoing support for the ocean assimilation system. Thanks also to Marianna Vertenstein for her guidance in developing the CCSM-DART interface and to Joe Tribbia, Peter Gent, Jerry Meehl and Grant Branstator for useful discussions. We are grateful to Tony Rosati for providing generous support during the initial phase of decadal prediction efforts at NCAR. This work was funded in part by the NOAA Climate Program Office under the Climate Variability and Predictability Program grants NA09OAR4310163 and NA13OAR4310138, and by the NSF Collaborative Research EaSM2 grant OCE-1243015. NCAR is sponsored by the National Science Foundation (NSF) and the CCSM project is supported by NSF and the Office of Science Biological and Environmental Research of the U.S. Department of Energy (DOE). Computing resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Informational Systems Laboratory, sponsored by the NSF and other agencies, and by the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. DOE under contract DE-AC05-00OR22725.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • A. Karspeck
    • 1
    Email author
  • S. Yeager
    • 1
  • G. Danabasoglu
    • 1
  • H. Teng
    • 1
  1. 1.National Center for Atmospheric ResearchBoulderUSA

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